# DataOngoing.ai

> HelpAirs technology for NetSuite: the five roles a company pays for, delivered as one supervised AI service at a modeled 20-80% lower cost, with the existing team repurposed rather than replaced, and every outcome published in the results ledger with its source class.

DataOngoing.ai is the HelpAirs technology and AI automation consultancy of DataOngoing LLC. HelpAirs puts the five NetSuite roles a company pays for (administrator, SuiteScript developer, business analyst, support specialist and QA) into one supervised AI service that answers questions from live SuiteQL, drafts schema-validated records and posts documents through an OCR pipeline, so the existing team is repurposed rather than replaced. The 48-72 hour AI technology diligence read, close and consolidation into one set of books, and shop-floor devices and documents posting straight into the ledger are the evidence behind it, each published in the results ledger with its source class.

DataOngoing.ai is the AI automation consultancy of DataOngoing LLC. https://dataongoing.com is the NetSuite managed-services practice. Founder: Kyle Castor, Founder & Principal Architect (https://jkcastor.com), 13 years in enterprise systems. Contact: the intake form at https://dataongoing.ai/contact/ (the direct email and phone number are shared in the receipt of a verified request).

Three numbers, never confused: 100x is executive time invested vs hours returned (the ledger). 10x is the fee guarantee. 10-50x is delivery speed vs the conventional baseline (the benchmark).

## HelpAirs technology for NetSuite

- [HelpAirs: five NetSuite roles in one supervised AI service](https://dataongoing.ai/#helpairs): A typical NetSuite environment carries an administrator, a developer, a business analyst, a support specialist and a quality lead: roughly $1,000,000 a year fully loaded, modeled. HelpAirs runs all five roles as one supervised AI service at a modeled 20% to 80% lower cost, and repurposes your existing employees instead of replacing them. Repurpose, not replace: the people stay and the routine queues move.
- [The $1M five-role calculator](https://dataongoing.ai/#calculator): Modeled defaults: NetSuite Administrator $170,000, Senior SuiteScript Developer $240,000, NetSuite Business Analyst $190,000, NetSuite Support Specialist $150,000, Quality (QA), Release and Turnover Cost $250,000; 1,000,000 dollars and 7,400 routine hours a year across the five roles. Savings tiers: Dedicated Pod 20%, Hybrid 50%, HelpAirs Box 80%. Savings are arithmetic on the visitor's inputs, modeled, never a quote.
- [Role by role: what HelpAirs absorbs and what the person does instead](https://dataongoing.ai/#roles): NetSuite Administrator: absorbs 0-4 Role Risk Index audits, saved searches and SuiteQL generated on request, form and field governance checked against the validation rules. Person: The internal ops lead moves from password resets and search building to vendor and customer service levels. Senior SuiteScript Developer: absorbs Single Entry Point router architecture, a governed MCP tool layer and production code shipped in two-week sprints. Person: Agency change orders stop; in-house technical staff move to product work instead of patching scripts. NetSuite Business Analyst: absorbs Chat with NetSuite: plain-English questions answered from live SuiteQL across GL, inventory, AR/AP and multi-entity consolidations. Person: Finance and FP&A analysts stop compiling spreadsheets and spend the time on margin. NetSuite Support Specialist: absorbs Governed record creation and the OCR document-to-ledger pipeline, posting validated transactions after a person approves the draft. Person: Customer service and AP/AR clerks move to account management and cash collection. Quality (QA), Release and Turnover Cost: absorbs Mandatory-field validation rules, sandbox-to-production regression checks before each release, and SOP memory the board can read. Person: Key-person risk falls as every business process is codified into governed, auditable corporate memory.
- [Two readiness tracks](https://dataongoing.ai/#tracks): We have good data: plug HelpAirs in now.: Your chart of accounts, SKUs and core transaction workflows are structured. You want to query NetSuite in plain English, create governed records and stop manual PDF entry this month. We do not have good data: build clean processes first.: Your tenant has duplicate SKUs, forty-plus colliding scripts, spreadsheet workarounds or un-integrated acquisitions. We re-architect clean, deterministic business processes first, then switch HelpAirs on.
- [The same choice, by outcome: Attrition](https://dataongoing.ai/#track-attrition): A NetSuite administrator, developer or analyst gives notice, and the routine queue they carried has no second person. We have good data: Absorb the routine queue before the next resignation. Your records are structured and most of what the departing person does is routine: tickets, saved searches, re-keying and reports. HelpAirs takes that queue in fourteen days, so a resignation becomes a handoff instead of a hiring emergency, and the people who stay move to the exceptions the queue was crowding out. We do not have good data yet: Read the tenant before the person who understands it leaves. When the departing person is also the one who knows where the colliding scripts and spreadsheet workarounds live, a tool bolted on top would guess. The 48-72 hour read captures the tenant as it is, from access; the foundation sprint turns the workarounds into deterministic processes that depend on no one; then HelpAirs takes the queue.
- [The same choice, by outcome: Loss-of-knowledge prevention](https://dataongoing.ai/#track-knowledge-loss): Undocumented scripts, hard-coded internal IDs and workarounds that only one person can explain. We have good data: Corporate memory, written while the knowledge is still in the building. Your processes are sound; they live in people rather than in documents. HelpAirs connects in fourteen days and the handoff phase codifies every script, permission and integration into runbooks that the next administrator, an auditor or a buyer can read, without pulling your team into interview rooms. We do not have good data yet: Document the tenant as it is, then rebuild what no one can explain. Forty colliding scripts and spreadsheet workarounds are knowledge too, held by whoever wrote them. The read records every deployment, orphaned field and integration from access; the foundation sprint retires what cannot be explained into a Single Entry Point router with written rules; the result is logic that lives in code and SOPs rather than in a person.
- [The same choice, by outcome: Saved implementations](https://dataongoing.ai/#track-rescued-implementations): An implementation or integration that stalled: orders frozen, the go-live slipping, the integrator billing change orders. We have good data: Finish the implementation on the data you already cleaned. The item master and the chart of accounts survived the project; what stalled is the last mile: the reports nobody built, the records still keyed by hand, the documents still in email. HelpAirs plugs into the structured tenant in fourteen days and covers that last mile with chat, governed record creation and OCR, so the go-live stops waiting on a build queue. We do not have good data yet: Stop, read it in 48-72 hours, restart on deterministic processes. A stalled implementation usually stalled for a reason the status reports do not show: duplicate items, scripts that fight each other on every save, a middleware layer nobody owns. The read finds the reason from access, without another discovery phase; the foundation sprint rebuilds the broken processes in fixed-fee two-week increments; HelpAirs goes on top of a tenant that works.
- [The same choice, by outcome: Integrations done faster](https://dataongoing.ai/#track-faster-integrations): A source system the business needs connected this month, quoted as weeks of middleware work. We have good data: Direct, idempotent integrations on a clean schema: same or next day for a standard source. With structured items, customers and a stable chart of accounts, a standard source (an e-commerce platform, an EDI partner, a CRM, a payment processor, a bank) connects directly to NetSuite through idempotent RESTlets, with no iPaaS layer to license. The published benchmark is same or next day for a standard source, and HelpAirs’s record creation governs every record the integration proposes. We do not have good data yet: Clean the schema the integration would inherit, then connect in days rather than weeks. An integration built onto duplicate SKUs and colliding User Event scripts copies the mess at machine speed. The read shows every script on the save path and every duplicate the source would match against; the foundation sprint collapses the scripts into a Single Entry Point router and deduplicates the masters; then the source connects at the published same-or-next-day pace, with validation rules to post against.
- [The same choice, by outcome: Exception reporting](https://dataongoing.ai/#track-exception-reporting): The variance, the stuck order and the duplicate invoice surface at month end, from a spreadsheet, instead of the day they happen. We have good data: Exceptions surface the day they happen, from live SuiteQL, with the rows cited. On structured data, HelpAirs answers an executive’s exception question (unbilled orders over a floor, price variances on vendor bills, open intercompany pairs) from live SuiteQL and cites the transactions it read. The 10 / 80 / 10 rule applies: 10% human intent in the question, 80% machine execution in the query and the draft, 10% human oversight on the exception that needs a decision. We do not have good data yet: Make the exceptions true first, then report them. An exception report over duplicate customers and un-reconciled clearing accounts reports noise. The read measures the noise: duplicates, orphaned fields, clearing-account leaks. The foundation sprint deduplicates the masters and declares the rules an exception is measured against; then the reports run on a ledger where an exception means something, and the 10 / 80 / 10 split holds because the 10% of human oversight is spent on real exceptions.
- [The same choice, by outcome: Compliance standards](https://dataongoing.ai/#track-compliance-standards): A national-chain barcode mandate, a lot-traceability audit or a segregation-of-duties finding, with the evidence scattered across spreadsheets and inboxes. We have good data: Approval gates and audit trails on the records you already keep clean. When items, lots and roles are already structured, compliance is evidence: who approved which record, from which document, under which role. HelpAirs connects in fourteen days with role-level segregation-of-duties guardrails from the 0-4 Role Risk Index, posts every document with its source archived and linked, and writes only through a person, so the audit trail is produced by the work rather than assembled for the auditor. We do not have good data yet: Traceability starts with a clean item master, not with a label printer. A standard applied to duplicate items, missing lot subrecords and shared administrator logins produces findings, not evidence. The read scores the roles on the 0-4 Role Risk Index and inventories the items, lots and documents the standard covers; the foundation sprint cleans the masters and codifies the standard (label formats, lot capture, approval steps) into deterministic processes with a live cutover; then HelpAirs keeps the trail per record.
- [Capability 01: Chat with NetSuite: executive and analyst questions over live SuiteQL](https://dataongoing.ai/#cap-chat): Pick an executive question to see how HelpAirs turns it into a governed, read-only query and answers with the rows it read. Replaces: Ad-hoc analyst report tickets and manual Excel joins Demonstrations on the site are scripted and labeled simulated.
- [Capability 02: Governed record creation: conversational and automated, approved by a person](https://dataongoing.ai/#cap-records): Select a record-creation workflow to inspect the pre-flight check against mandatory fields and the custom schema. Replaces: Manual CSV imports, form save errors and support-clerk re-keying Demonstrations on the site are scripted and labeled simulated.
- [Capability 03: OCR and document-to-ledger automation: 2,881 production documents processed (measured)](https://dataongoing.ai/#cap-ocr): Select an unstructured document to see how the pipeline extracts it, cross-walks it to NetSuite records and posts after approval. Replaces: Paper clipboards, PDF invoice re-typing and delayed warehouse receipts Demonstrations on the site are scripted and labeled simulated.
- [Structured 3, 6, 9 and 12-month roadmaps, next to the fixed-price sprints](https://dataongoing.ai/#roadmaps): 3 months, Process cleanse plus the core HelpAirs service: For single-entity operators ready to remove immediate reporting, script and OCR bottlenecks. 6 months, All five roles in HelpAirs: The modeled $1,000,000/yr five-role stack absorbed into HelpAirs across finance, operations, warehouse and IT. 9 months, OneWorld roll-up and edge automation: For multi-subsidiary operators and PE roll-ups integrating acquisitions and warehouse hardware. 12 months, A full year of fractional principal architecture: HelpAirs governance across every subsidiary, with the architect on call for the year. No published price; fixed deliverables are quoted after the diligence read.
- [Owner-classed evidence figures](https://dataongoing.ai/#evidence): 80+ subsidiary companies automated, 100+ currencies, $1B+ in revenue flows over 13 years. (Founder track record, pre-DataOngoing) A radical transformation in barcodes for the warehouse: 3:1 QR-code use across pack, ship, fulfill and receive, with intercompany effects. (Measured, consulting engagement) An 80% reduction in wasteful SKUs through better data modeling. (Measured, consulting engagement) $800k saved per year on AP cost, a separate outcome from the published $280K/yr labor recovery. (Measured, consulting engagement)

## The results ledger

- [The Results Ledger: Executive Hours In, Hours and Dollars Out](https://dataongoing.ai/100x-roi-ledger/): 100x ROI for time invested means that for every hour an operating partner, CFO or CTO puts into an engagement, the automation returns about one hundred hours of work or the equivalent margin over twelve months. 100x is the design target for every engagement. The ledger below shows measured rows between roughly 15x and 90x on time, one self-reported internal row near 80x, and projected rows above 100x. Every figure states its source class so it can be checked.

Automation | Executive hours in* | Returned over 12 months | Multiple on time | Source class
Month-end close pipeline: 8 hours to 15 minutes per close | ~6 | ~93 hours/yr; 32x faster per close cycle | ~15x | Founder track record, pre-DataOngoing
Transaction pre-processing and label automation: 200,000 to 30,000 transactions/month; 250,000+ labels/yr | ~8 | 720 hours/yr (60 hours/month recovered) | ~90x | Founder track record, pre-DataOngoing
Native banking integration replacing AP middleware | ~4 | $200,000/yr in middleware fees eliminated | Dollar return; see bps conversion | Founder track record, pre-DataOngoing
Automated dunning and milestone-triggered billing: overdue AR $6.5M to $1.4M | ~4 | $5.1M of liquidity returned (one-time) | Cash return, not time | Founder track record, pre-DataOngoing
Catalog and BOM schema consolidation (881 to 315 items) with a single-scan traceability workflow | ~10 | $280,000/yr in administrative and operational labor recovered; 100% lot traceability | Dollar return; see bps conversion | Measured, consulting engagement
Perishable FIFO lot allocation and profitability close: 14-hour spreadsheet close to a 20-minute automated run | ~6 | ~164 hours/yr (13.7 hours per close, 12 closes); 100% auditable lot-level reconciliation | ~27x | Measured, consulting engagement
AI document intake (OCR) pipeline posting into the ERP | ~5 | 2,881 documents processed to date; per-document manual time not yet published | Measured volume; hours pending | Measured, consulting engagement
Programmatic file organization: 367 files categorized | 0.05 (about 3 minutes) | 3.5-4 hours of manual sorting avoided per run | ~80x | Self-reported, internal
Sales-order Single Entry Point router replacing 42 script deployments | ~4 | 1,065 hours/yr recovered; $55,725/yr (at 2,500 saves/day, $30 loaded wage, $150 developer cost) | ~265x (projected) | Projected
Sales-order form latency: 44.02 hours of user wait in 25 days across 42 scripts | ~4 | 373 hours/yr recovered at an 84.8% latency reduction (measured baseline, projected recovery) | ~93x (projected) | Projected
Roll-up multiple expansion: 5.9x entry to 8.5x exit, +$28.5M on $11M EBITDA | n/a | Enterprise value, not hours | n/a | Modeled composite
Same-day cryptographic M&A NetSuite vault (Project Allium, anonymized cold-chain processor): 26 legacy workbooks and 10,070 rows (6,128 GL lines, 1,321 FSMA 204 lots, 1,820 crosswalks, 801 source files) sealed in 24 hours against a 6-9 month SI quote | ~4 | 6-9 months of SI migration drag avoided; GAAP P&L tied to the source exports across 5 fiscal years; every row SHA-256 hashed under one Merkle root | ~120x | Modeled composite

Source classes: measured = Measured, consulting engagement; founder-role = Founder track record, pre-DataOngoing; self-reported = Self-reported, internal; projected = Projected; modeled = Modeled composite.

## Services

- [AI Automation Consulting for Private Equity Portfolios](https://dataongoing.ai/ai-automation-private-equity/): DataOngoing.ai is the AI automation consultancy for private-equity sponsors, operating partners and portfolio CFOs and CTOs. We take a process that costs a portfolio company hours every week, rebuild it with schema-validated agents and native ERP code in a fixed-price two-week sprint, and state the result in basis points of EBITDA margin. A few hours of executive time in returns hundreds of hours of automated work out, and every outcome is published in the results ledger with its source class.
- [Data privacy in AI work: where your data goes when a model reads it](https://dataongoing.ai/ai-automation-private-equity/#data-privacy): Only the rows a task needs reach a model, with personal data masked where the task does not need it. The model is the client's choice: Anthropic's Claude, Google's Gemini or OpenAI's models under commercial terms that exclude training on prompts (recommended), or open-weight models on dedicated GPU capacity, on the client's hardware or through TensorWave in Las Vegas, when data cannot leave the tenant. Writes go through a person; prompts, rows, model and approver are logged per record.
- [48-72 Hour AI Technology Diligence Read](https://dataongoing.ai/services/ai-technology-diligence-read/): A flat-fee, AI-driven technology diligence read on a NetSuite or ERP target in 48-72 hours, with a quantified risk matrix and a costed remediation plan. 100% of the fee is credited to downstream remediation. Your time: 2-3 hours: one access grant, one read-out. Turnaround: 48-72 hours from access. Price: $12,500 flat fee, 100% credited to remediation.
- [Two-Week Basis-Point Sprint](https://dataongoing.ai/services/basis-point-sprint/): A fixed-price, fourteen-day production sprint that removes one named margin leak or bottleneck in a portfolio company with working code and a schema-validated agent where one belongs. If it does not run in production, the milestone is not billed. Your time: 3-5 hours: scope sign-off, one mid-sprint review, acceptance. Turnaround: 14 days. Price: Fixed price, quoted after the diligence read.
- [One-Set-of-Books Close Automation](https://dataongoing.ai/services/close-and-consolidation-automation/): A fixed-scope 100-day program that consolidates acquired entities into one governed NetSuite OneWorld tenant with an automated close: chart of accounts harmonization, automated intercompany eliminations and a consolidated period close compressed from roughly 21 business days to 3. Your time: 6-10 hours across the program: chart of accounts decisions and two reviews. Turnaround: 100 days. Price: Fixed scope, quoted after a one-week consolidation assessment.
- [Floor-to-Ledger Device and Document Automation](https://dataongoing.ai/services/document-intake-automation/): Edge devices and an AI document-intake (OCR) pipeline posting directly into the ledger, quoted per site: no re-keying, no per-user warehouse licenses, and documents processed by an agent instead of a clerk. Your time: 3-4 hours per site: floor walk and device list sign-off. Turnaround: 2-3 week sprint per site. Price: Quoted per site after a device and document audit.
- [Same-Day PE M&A Cryptographic NetSuite Consolidation (24-Hour Audit Vault)](https://dataongoing.ai/services/same-day-pe-ma-netsuite-consolidation/): Replace 6-to-9 month post-acquisition ERP data migrations with a 24-hour cryptographic NetSuite cutover. Seal the historical GL rows, master crosswalks, and FSMA 204 lot traceability into 4 NetSuite Custom Records (customrecord__do_ma_*) with SHA-256 row hashing, Binary Merkle Tree verification, and a live date-range GAAP P&L console. Your time: 3-4 hours: raw archive drop, COA mapping sign-off, Merkle root acceptance. Turnaround: 24 hours from archive drop. Price: Fixed flat fee, quoted per acquired entity.
- [NetSuite Digital Marketing & B2B Inventory Bundle](https://dataongoing.ai/services/netsuite-digital-marketing-b2b-inventory-bundle/): An SDF bundle that syndicates NetSuite inventory, lot age and wholesale price tiers to Google, Facebook, Instagram, TikTok and YouTube, turns SKU photos into motion video ads, takes the B2B order through a checkout that posts the NetSuite transaction, and measures every campaign against a Day-0 baseline in realized gross margin. $15,000 flat setup, $2,500 per month to run the engine. Your time: 3 hours: catalog and floor sign-off, ad account access, launch review. Turnaround: 14 days from account access. Price: $15,000 flat setup, $2,500 per month to run the engine.
- [Containerized NetSuite OCR Authority & 3-Way Match Sprint](https://dataongoing.ai/services/netsuite-ocr-ap-sales-order-3way-match/): Lightweight discovery containers crawl email inboxes, SFTP/SMB shares, warehouse scanners, and the NetSuite File Cabinet, attach source PDFs to any native or custom record via record.attach(), and automate Sales Orders, AP Vendor Bills, and line-level 3-Way Matching in a 14-day fixed-price sprint ($15,000 flat fee). Your time: 3-4 hours: inbox/SFTP folder mapping, tolerance rules sign-off, and exception queue review. Turnaround: 14 days (two-week production sprint). Price: $15,000 flat fee (zero per-page or per-document recurring tolls).
- [Sovereign Self-Hosted Local LLM Stack for NetSuite](https://dataongoing.ai/services/self-hosted-local-llm-netsuite/): Eliminate recurring cloud LLM API token bills and keep 100% of your NetSuite General Ledger, customer pricing, and vendor banking data inside your own firewall with a Dual-Tier open-weight Local LLM stack (vLLM Qwen2.5-VL + Llama-3.3-70B or Ollama + Docling) deployed in 14 days ($18,500 flat fee). Your time: 2-3 hours: VPC or on-premise hardware target selection, network firewall verification, and role-permission sign-off. Turnaround: 14 days (two-week production sprint). Price: $18,500 flat setup fee ($0.00 per token; runs on client hardware or dedicated VPC).
- [Technology Due Diligence for Private Equity](https://dataongoing.ai/technology-due-diligence-private-equity/): DataOngoing performs technology due diligence on NetSuite and ERP targets in 48 to 72 hours rather than the conventional four to six weeks. We read the codebase, script deployments and role permission tables directly instead of interviewing stakeholders, then deliver a board-ready risk matrix with a dollar-quantified remediation cost before capital is wired. Basis-point lever: Avoided discount at close.
- [Financial Statement Consolidation in NetSuite](https://dataongoing.ai/financial-statement-consolidation-netsuite/): DataOngoing consolidates acquired entities into a single NetSuite OneWorld tenant so a portfolio company reports from one governed set of books. We harmonize the chart of accounts, automate intercompany eliminations, and compress the consolidated period close from roughly 21 business days to 3, which is what makes the financials defensible at exit. Basis-point lever: Close speed and working capital.
- [Print, Scan and Weigh Consolidation Across a Portfolio](https://dataongoing.ai/print-scan-weigh-consolidation/): DataOngoing unifies the physical device layer across acquired sites: printers and MFP fleets, barcode and RFID scanners, and bench, floor and forklift scales all post directly into a single NetSuite instance. Operators stop re-keying weights and labels, print queues stop crashing, and the portfolio stops paying for warehouse software it does not need. Basis-point lever: Labor and freight basis points.
- [NetSuite Digital Marketing and B2B Inventory Ads](https://dataongoing.ai/netsuite-digital-marketing-b2b-inventory/): Most B2B distributors and manufacturers advertise from a product export: the ad account does not know a SKU sold out this morning, that a lot has sat for 118 days, or what the order actually earned after landed cost. DataOngoing installs the inventory, creative, checkout and attribution layers inside NetSuite as one SDF bundle, so the feed reads Available-to-Promise every 15 minutes, the creative is rendered from the SKU record, the order is a NetSuite Sales Order or Cash Sale with the lot reserved, and the return is incremental gross profit above a 90-day baseline. Basis-point lever: Gross margin on advertised inventory.
- [Containerized NetSuite OCR Authority: Sales Orders, AP Vendor Bills, 3-Way Matching & Custom Records](https://dataongoing.ai/netsuite-ocr-document-authority-3-way-matching/): DataOngoing’s Containerized NetSuite OCR Authority (com.dataongoing.ocr.authority) replaces rigid, per-page AP SaaS tools with lightweight discovery containers and a native SuiteScript 2.1 transaction engine. Built on our production OCR baseline (2,881 documents processed to date, measured), the containers crawl email inboxes, SFTP/SMB shares, warehouse scanners, and orphaned NetSuite File Cabinet folders, compute SHA-256 hashes, sanitize hidden 1pt adversarial text, bind every PDF via record.attach(), and automate Sales Orders, AP Vendor Bills, line-level PO-Receipt-Bill 3-Way Matching, and any Custom Record (customrecord_*) in a 14-day sprint ($15,000 flat fee, 10x fee guarantee). Basis-point lever: Order-to-cash velocity, AP clerical repurposing & vendor price-variance recovery.
- [Self-Hosted Local LLM for NetSuite: Zero Token Tax & 100% ERP Data Sovereignty](https://dataongoing.ai/self-hosted-local-llm-erp-sovereignty/): Companies running high-volume NetSuite OCR, automated 3-Way Matching, and conversational SuiteQL reporting should not have to pay a perpetual per-token tax to public AI providers or send their General Ledger, customer pricing, and vendor banking records outside their firewall. DataOngoing deploys a Dual-Tier Self-Hosted Local LLM stack (com.dataongoing.localllm.sovereign) inside an isolated Docker network (internal: true) in a 14-day fixed-price sprint ($18,500 flat setup, 10x fee guarantee), delivering $0.00 per-token inference and 78%–92% 3-year TCO savings (modeled). Basis-point lever: Eliminated cloud API token tax & 100% on-premise / private-VPC data residency.
- [The 100X Speed Advantage: How We Work, and the Arithmetic Behind It](https://dataongoing.ai/how-we-work-10-50x/): DataOngoing delivers AI automation, diligence and consolidation work 10 to 50 times faster than conventional consulting because we run AI-driven static analysis against the actual codebase and metadata instead of weeks of stakeholder interviews, staff every engagement with principal architects only, and ship working code in fixed-price two-week sprints. Speed is what makes 100x on your time possible: a few hours of operating-partner input returns hundreds of hours of automated work, and every row of that claim is published in the results ledger.

## Delivery velocity benchmark

Workstream | Conventional baseline | DataOngoing | Multiple
Technology diligence report | 4-6 weeks | 48-72 hours | 14-21x
Source-system API integration | 6-12 weeks | Same or next day | 30-60x
Script and permission inventory | 2-3 weeks of manual review | Automated static analysis, hours | 40x+
Multi-entity period close | 21 business days | 3 business days | 7x
Shop-floor device integration | 3-6 months via WMS project | 2-3 week sprint | 6-12x

## Proof library

- [What has been built](https://dataongoing.ai/proof/): 104 sanitized proof cards from deployed automations, grouped by domain and labeled by source class.
- [Perishable FIFO lot allocation engine and real-time profitability analytics deployed in NetSuite.](https://dataongoing.ai/proof/perishable-fifo-lot-allocation-engine-and-real-time-profitability-h00783/): Inventory Valuation & COGS Architecture. Before: 14-hour manual close spreadsheets; estimated margin guesswork. After: 20-minute automated run; 100% auditable lot-level GL reconciliation. (Measured, consulting engagement)
- [Specialized NetSuite AI agent skill automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-automating-erp-operations-h00746/): SuiteScript Performance & Script Flattening. Before: 12-15 second transaction save delay; intermittent timeout errors. After: Sub-second UI response time; 0 record locking collisions. (Modeled composite)
- [Specialized NetSuite AI agent skill automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-automating-erp-operations-h00295/): Autonomous AI Workers & NetSuite Governance. Before: 4-week consulting discovery audit costing tens of thousands. After: Instant, board-ready system health X-Ray generated in minutes. (Modeled composite)
- [Design Change Request (DCR) management runbook and approval automation in NetSuite.](https://dataongoing.ai/proof/design-change-request-dcr-management-runbook-and-approval-h00374/): Manufacturing, BOM & Engineering Governance. Before: 4-week consulting discovery audit costing tens of thousands. After: Instant, board-ready system health X-Ray generated in minutes. (Modeled composite)
- [Automated generator for board-ready NetSuite SOP handoffs, technical runbooks, and Mermaid architecture diagrams.](https://dataongoing.ai/proof/automated-generator-for-board-ready-netsuite-sop-handoffs-technical-h00755/): SuiteScript Performance & Script Flattening. Before: 12-15 second transaction save delay; intermittent timeout errors. After: Sub-second UI response time; 0 record locking collisions. (Modeled composite)
- [Operational runbook and technical retrospective for deployed NetSuite automations.](https://dataongoing.ai/proof/operational-runbook-and-technical-retrospective-for-deployed-netsuite-h00372/): Procure-to-Pay & AP Automation. Before: Manual email threads, offline spreadsheets, and untracked approvals. After: 100% native NetSuite digital workflow with complete audit trail. (Measured, consulting engagement)
- [Audits synchronous NetSuite form scripts and refactors 10-15s save bottlenecks into asynchronous Map/Reduce pipelines.](https://dataongoing.ai/proof/audits-synchronous-netsuite-form-scripts-and-refactors-10-15s-h00526/): Order-to-Cash & Transaction Lifecycle. Before: Manual email threads, offline spreadsheets, and untracked approvals. After: 100% native NetSuite digital workflow with complete audit trail. (Modeled composite)
- [Landed cost synchronization and inventory valuation automation operational in NetSuite.](https://dataongoing.ai/proof/landed-cost-synchronization-and-inventory-valuation-automation-operational-h04733/): Inventory Valuation & COGS Architecture. Before: 14-hour manual close spreadsheets; estimated margin guesswork. After: 20-minute automated run; 100% auditable lot-level GL reconciliation. (Measured, consulting engagement)
- [Transaction Lifecycle Automation (TLA) runbook managing intercompany order flows and validation.](https://dataongoing.ai/proof/transaction-lifecycle-automation-tla-runbook-managing-intercompany-order-h00364/): Manufacturing, BOM & Engineering Governance. Before: 4-week consulting discovery audit costing tens of thousands. After: Instant, board-ready system health X-Ray generated in minutes. (Modeled composite)
- [Specialized NetSuite AI agent skill (dataongoing-helpairs-ai-worker-deployer) automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-dataongoing-helpairs-ai-worker-deployer-automating-erp-h00311/): Order-to-Cash & Transaction Lifecycle. Before: Manual email threads, offline spreadsheets, and untracked approvals. After: 100% native NetSuite digital workflow with complete audit trail. (Modeled composite)
- [Specialized NetSuite AI agent skill (dataongoing-helpairs-gl-margin-leak-detector) automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-dataongoing-helpairs-gl-margin-leak-detector-automating-erp-h00417/): Inventory Valuation & COGS Architecture. Before: 14-hour manual close spreadsheets; estimated margin guesswork. After: 20-minute automated run; 100% auditable lot-level GL reconciliation. (Modeled composite)
- [Specialized NetSuite AI agent skill (dataongoing-helpairs-interactor) automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-dataongoing-helpairs-interactor-automating-erp-h00420/): Manufacturing, BOM & Engineering Governance. Before: 4-week consulting discovery audit costing tens of thousands. After: Instant, board-ready system health X-Ray generated in minutes. (Modeled composite)
- [Specialized NetSuite AI agent skill automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-automating-erp-operations-h00771/): Order-to-Cash & Transaction Lifecycle. Before: Manual email threads, offline spreadsheets, and untracked approvals. After: 100% native NetSuite digital workflow with complete audit trail. (Modeled composite)
- [Specialized NetSuite AI agent skill (dataongoing-helpairs-document-formatter) automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-dataongoing-helpairs-document-formatter-automating-erp-h00332/): SuiteScript Performance & Script Flattening. Before: 12-15 second transaction save delay; intermittent timeout errors. After: Sub-second UI response time; 0 record locking collisions. (Modeled composite)
- [Operational runbook and technical retrospective for deployed NetSuite automations.](https://dataongoing.ai/proof/operational-runbook-and-technical-retrospective-for-deployed-netsuite-h00378/): SuiteScript Performance & Script Flattening. Before: 12-15 second transaction save delay; intermittent timeout errors. After: Sub-second UI response time; 0 record locking collisions. (Measured, consulting engagement)
- [Specialized NetSuite AI agent skill (dataongoing-helpairs-proposer) automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-dataongoing-helpairs-proposer-automating-erp-h00469/): Manufacturing, BOM & Engineering Governance. Before: 4-week consulting discovery audit costing tens of thousands. After: Instant, board-ready system health X-Ray generated in minutes. (Modeled composite)
- [Modernization blueprint transforming legacy SuiteCommerce Advanced (SCA) into modern Next.js storefronts.](https://dataongoing.ai/proof/modernization-blueprint-transforming-legacy-suitecommerce-advanced-sca-into-h00521/): SuiteScript Performance & Script Flattening. Before: 12-15 second transaction save delay; intermittent timeout errors. After: Sub-second UI response time; 0 record locking collisions. (Modeled composite)
- [Specialized NetSuite AI agent skill automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-automating-erp-operations-h00286/): Financial Reporting, SOX & Period-Close. Before: Manual email threads, offline spreadsheets, and untracked approvals. After: 100% native NetSuite digital workflow with complete audit trail. (Modeled composite)
- [Specialized NetSuite AI agent skill (dataongoing-helpairs-document-formatter) automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-dataongoing-helpairs-document-formatter-automating-erp-h00331/): Manufacturing, BOM & Engineering Governance. Before: 4-week consulting discovery audit costing tens of thousands. After: Instant, board-ready system health X-Ray generated in minutes. (Modeled composite)
- [Operational runbook and technical retrospective for deployed NetSuite automations.](https://dataongoing.ai/proof/operational-runbook-and-technical-retrospective-for-deployed-netsuite-h00376/): Manufacturing, BOM & Engineering Governance. Before: 4-week consulting discovery audit costing tens of thousands. After: Instant, board-ready system health X-Ray generated in minutes. (Measured, consulting engagement)
- [Specialized NetSuite AI agent skill (dataongoing-helpairs-ma-trial-balance-reconciler) automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-dataongoing-helpairs-ma-trial-balance-reconciler-automating-erp-h00459/): SuiteScript Performance & Script Flattening. Before: 12-15 second transaction save delay; intermittent timeout errors. After: Sub-second UI response time; 0 record locking collisions. (Modeled composite)
- [Specialized NetSuite AI agent skill (dataongoing-helpairs-proposer) automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-dataongoing-helpairs-proposer-automating-erp-h00482/): Procure-to-Pay & AP Automation. Before: Manual email threads, offline spreadsheets, and untracked approvals. After: 100% native NetSuite digital workflow with complete audit trail. (Modeled composite)
- [Specialized NetSuite AI agent skill (dataongoing-helpairs-proposer) automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-dataongoing-helpairs-proposer-automating-erp-h00484/): Autonomous AI Workers & NetSuite Governance. Before: 4-week consulting discovery audit costing tens of thousands. After: Instant, board-ready system health X-Ray generated in minutes. (Modeled composite)
- [Specialized NetSuite AI agent skill automating ERP operations and technical governance.](https://dataongoing.ai/proof/specialized-netsuite-ai-agent-skill-automating-erp-operations-h00743/): Inventory Valuation & COGS Architecture. Before: 14-hour manual close spreadsheets; estimated margin guesswork. After: 20-minute automated run; 100% auditable lot-level GL reconciliation. (Modeled composite)
- [Automated generator for board-ready NetSuite SOP handoffs, technical runbooks, and Mermaid architecture diagrams.](https://dataongoing.ai/proof/automated-generator-for-board-ready-netsuite-sop-handoffs-technical-h00360/): Core NetSuite Architecture & Customization. Before: Manual email threads, offline spreadsheets, and untracked approvals. After: 100% native NetSuite digital workflow with complete audit trail. (Modeled composite)

## Playbooks

- [The 48-Hour Diagnostic vs. The $300k Discovery Trap: Why Enterprise Discovery Is Broken](https://dataongoing.ai/playbooks/48-hour-diagnostic-vs-300k-discovery-trap/): Traditional ERP consultancies charge $300,000 for 4-month discovery slide decks. Discover how DataOngoing delivers automated empirical diagnostics in 48 hours.
- [Atomic Landed Cost Capitalization: Eliminating the 60-Day Freight Illusion](https://dataongoing.ai/playbooks/atomic-landed-cost-capitalization-netsuite/): Inbound ocean freight and drayage often lag receipts by 60 days. Learn how DataOngoing automates point-of-receipt landed cost capitalization in NetSuite.
- [Liquidating a $42M Stalled Backlog in 28 Days: Algorithmic Order Orchestration](https://dataongoing.ai/playbooks/liquidate-42m-stalled-order-backlog-netsuite/): How algorithmic order triage and split-fulfillment automation in NetSuite liquidated a $42M frozen order backlog in under 30 days.
- [The 72-Hour Item Master Cleanse: Purging 35,000 Dead SKUs Without Re-Implementation](https://dataongoing.ai/playbooks/72-hour-item-master-sanitation-netsuite/): Don't spend $500k re-implementing NetSuite to fix dirty item data. Discover DataOngoing's 72-hour non-destructive in-place sanitation engine.
- [Modular Legacy Deprecation: Deconstructing On-Prem Monoliths in 7 Two-Week Sprints](https://dataongoing.ai/playbooks/deconstruct-legacy-monolith-netsuite-sprints/): Avoid $1M+ Big-Bang ERP failures. Learn how DataOngoing retired a 15-year legacy manufacturing system in 7 modular sprints with zero plant downtime.
- [Collapsing the 33-Day Scrap Lag: Real-Time Shop-Floor Quality in NetSuite](https://dataongoing.ai/playbooks/real-time-manufacturing-scrap-qms-netsuite/): Paper travelers create a 33-day lag on manufacturing scrap reporting. Discover how DataOngoing deployed real-time shop-floor QMS in NetSuite in 14 days.
- [Governed CAD Vaulting: Eliminating NetSuite Storage Surcharges with Microsoft Graph](https://dataongoing.ai/playbooks/governed-cad-sharepoint-netsuite-integration/): Stop paying $14,400/yr in NetSuite storage fees for CAD files. Discover DataOngoing's Microsoft Graph and Azure SSO integration bridge.
- [The 60-Day M&A Playbook: Integrating Acquired Entities into NetSuite OneWorld](https://dataongoing.ai/playbooks/60-day-ma-netsuite-oneworld-integration/): Private equity M&A integrations shouldn't take 12 months. Discover DataOngoing's 60-Day NetSuite OneWorld post-merger integration framework.
- [Model Context Protocol (MCP) in ERP: AI Agents That Understand NetSuite](https://dataongoing.ai/playbooks/model-context-protocol-mcp-ai-netsuite/): Beyond toy chatbots: Learn how DataOngoing uses Model Context Protocol (MCP) to run autonomous AI agents executing live SuiteQL queries under strict governance.
- [Why the Work Goes 100x Faster: How Agile Enterprise Architecture Disrupts Legacy Consulting](https://dataongoing.ai/playbooks/physics-of-100x-speed-enterprise-erp-architecture/): How DataOngoing solves enterprise NetSuite challenges 100X faster than legacy consultancies through AI diagnostics and fixed 2-week production sprints.
- [NetSuite Inventory to Five-Channel Video Ads, With the Return Read in the Ledger](https://dataongoing.ai/playbooks/netsuite-b2b-inventory-to-omnichannel-video-ads-attribution/): How a NetSuite SDF bundle turns SKU photos and live Available-to-Promise into motion video ads across Google, Facebook, Instagram, TikTok and YouTube, posts the B2B order with the lot reserved, and reports the return against a Day-0 baseline in realized gross margin.

## Whitepapers

- [Weekly Shop-Floor Operator Briefing (Issue #01)](https://dataongoing.ai/newsletter/): How fast a computer can codify your shop floor, barcode your warehouse, and get you compliant with national chains.
- [Technical Arbitrage and Multiple Expansion](https://dataongoing.ai/library/technical-arbitrage-and-multiple-expansion/): Why bolt-on acquisitions fail to achieve multiple expansion when technology integration is deferred, and how a 48-72 hour NetSuite diligence read protects fund returns across SuiteScript 1.0 liabilities and role permission sprawl.
- [The Disconnected Dock](https://dataongoing.ai/library/the-disconnected-dock-print-and-weight-friction/): How manual clipboards, static floor scales and unmanaged print spoolers waste 15-20% of warehouse profits, and how in-transit forklift scales and 2D ring scanners eliminate the driver dismount entirely.
- [The 100-Day Post-Merger Unification Blueprint](https://dataongoing.ai/library/100-day-post-merger-netsuite-unification-blueprint/): A disciplined four-phase architectural roadmap to consolidate acquired entities into a single OneWorld tenancy, retire script sprawl, and automate intercompany eliminations by Day 100.
- [How DataOngoing Maps Enterprise Architecture](https://dataongoing.ai/library/how-dataongoing-maps-enterprise-architecture/): The three layers every enterprise system map must separate, the four structural anti-patterns behind slow saves, lying margins, shadow spreadsheets and brittle middleware, the five standards a correctly mapped transaction meets, and how the map becomes production code in a fourteen-day sprint.
- [How Fast a Computer Can Codify Your Shop Floor, Barcode Your Warehouse, and Get You Compliant With National Chains](https://dataongoing.ai/library/codify-shop-floor-national-chain-barcode-compliance/): Why what used to take an army of expensive software consultants six months and $150,000 to build for your plant can now be built and running on shop-floor tablets in one to two weeks—covering national-chain EDI/ASN barcode compliance, codifying a 30-year foreman into touchscreen QC gates, 3-second roll recall audits, and 48-hour multi-plant part merges.
- [Containerized NetSuite OCR Authority: Automating Sales Orders, AP Vendor Bills, 3-Way Matching & Universal Record Attachment](https://dataongoing.ai/library/containerized-ocr-authority-sales-orders-ap-3way-match/): How lightweight Docker containers crawl email inboxes, SFTP shares, warehouse scanners, and the NetSuite File Cabinet, attach source PDFs via record.attach(), and automate Sales Orders, AP Vendor Bills, line-level 3-Way Matching, and any Custom Record in a 14-day sprint ($15,000 flat fee).
- [Self-Hosted Local LLM for NetSuite: Zero Token Costs, 100% On-Premise Data Sovereignty & Private ERP Automation](https://dataongoing.ai/library/self-hosted-local-llm-zero-token-tax-netsuite/): Why CFOs and CISOs are replacing metered cloud LLM APIs with a Dual-Tier Self-Hosted Local LLM stack (vLLM or Ollama) inside an air-gapped Docker network (internal: true): $0.00 per token, 78%–92% 3-year TCO reduction (modeled), and zero ERP data egress.

## How discovery happens, and how few people it takes

- [Dialogues: How Discovery Happens and How Few People It Takes](https://dataongoing.ai/dialogues/): Discovery at DataOngoing is a read of the target’s system, not an interview of its people: access lands, automated static analysis runs for 48-72 hours, the principal architect interprets the findings, and one read-out of about an hour delivers a priced risk matrix. Technology keeps the people involved to a minimum by the 10/80/10 rule: 10% human intent, 80% machine execution behind validation rules, 10% human oversight. The dialogues below walk through both, role by role. They are modeled composites: the roles are archetypes, the figures are illustrative with their assumptions stated, and the process they describe is the actual one.

Discovery sequence (48-72 hours from access; 2-3 hours of acquirer time; zero interviews):
1. Intake: The contact form is stored privately and routed to the architect. No sales development rep, no qualification call; the reply comes from the person who does the read. Who: You, Kyle Castor. Your time: About 15 minutes.
2. Scope and terms: Systems in scope confirmed: a single instance, or a platform plus the add-on systems that fold into it. Flat fee of $12,500, credited in full to remediation. We sign your NDA. Who: You, Kyle Castor. Your time: 30-45 minutes.
3. Access: The target’s administrator creates one read-only, least-privilege integration role or, pre-LOI, exports the metadata and a role listing. Who: Target administrator (under an hour). Your time: About 15 minutes of coordination.
4. Automated read: 48-72 hours from access. Every customization parsed, every role scored, every integration listed, transaction telemetry queried. Who: Automated passes. Your time: 0.
5. Interpretation: Findings priced and sequenced into a costed Day 1 and 100-day remediation plan. Who: Kyle Castor. Your time: 0.
6. Read-out: Risk matrix, customization and integration inventory, role index and costed plan presented to you and the deal team. Who: You, deal team, Kyle Castor. Your time: About 1 hour.

Where people are involved (10/80/10):
Workstream | Human intent (10%) | Machine execution (80%) | Human oversight (10%)
Our own intake | You describe the situation on the form | Private storage, routing and alerting | The architect replies; no SDR, no qualification call
Technology diligence | What the investment committee needs priced | Static analysis of scripts, permissions, integrations and telemetry | Interpretation and one read-out: 2-3 hours
Two-week sprint | Which leak to remove; scope sign-off | Code written, tested and promoted through an automated deployment pipeline | One mid-sprint review and acceptance: 3-5 hours
Close and consolidation | Chart-of-accounts decisions in the first two weeks | Automated intercompany eliminations; automated close checklist | Exception clearing and two reviews: 6-10 hours across 100 days
Devices and documents | Floor walk; device list and document types | OCR intake behind validation rules; scales, scanners and printers posting directly to the ledger | Exception queue a person clears: 3-4 hours per site to set up

## Executive dialogues

- [Operating Partner Dialogue: How Discovery Happens](https://dataongoing.ai/library/operating-partner-dialogue-how-discovery-happens/): An operating partner walks through exactly what happens after the intake form: the access grant, the 48-72 hour automated static-analysis read of scripts, permissions and integrations, the one-hour read-out, and why nobody on the target’s team is interviewed.
- [CFO Dialogue: How Few People Does This Take?](https://dataongoing.ai/library/cfo-dialogue-how-few-people-does-this-take/): A portfolio CFO with a controller at capacity asks how many of her people an engagement consumes. Kyle counts the hours offer by offer, shows where automation replaces interviews, status meetings and re-keying, and names the decisions that stay human on purpose.
- [CFO Dialogue: Protecting EBITDA Multiple Expansion](https://dataongoing.ai/library/cfo-dialogue-ebitda-multiple-expansion/): A skeptical CFO grills Kyle on why traditional roadmaps fail, how phantom inventory receipts destroy exit value, and how 14-day production sprints eliminate $120k/yr middleware subscription tolls.
- [CTO Dialogue: SuiteScript 1.0 and the Single Entry Point](https://dataongoing.ai/library/cto-dialogue-suitescript-technical-debt/): A deep architectural debate on why dozens of ad-hoc User Event scripts lock database rows, how AST static analysis catches SuiteScript 1.0 sunset risk, and how a Single Entry Point router stabilizes high-throughput ledgers.
- [COO Dialogue: Dock Congestion and Freight Penalties](https://dataongoing.ai/library/coo-dialogue-dock-efficiency-and-throughput/): A COO battling truck line-ups and carrier re-weigh fines. Kyle shows how carriage scales and direct ZPL thermal printing eliminate static scale stops and bypass expensive third-party WMS software.
- [IT Director Dialogue: Killing Spooler Crashes and Shadow Databases](https://dataongoing.ai/library/it-director-dialogue-printer-queues-and-shadow-code/): An overworked IT Director explains why 60 thermal barcode printers choke Windows spoolers at 3:00 PM. Kyle replaces heavy PDFs with direct TCP/IP socket streaming on port 9100 and retires a shadow Access database.
- [Floor Lead Dialogue: Scales, Scanners and FIFO Subrecords](https://dataongoing.ai/library/floor-lead-dialogue-scales-scanners-and-fifo/): A forklift veteran on why consumer tablets froze in a 15F freezer. Kyle details heated vehicle-mount terminals, hands-free ring scanning, and automated subrecord FIFO lot locks that alert drivers on a wrong pick.
- [Executive Roundtable: The Governed Process Experience](https://dataongoing.ai/library/executive-roundtable-governed-process-experience/): A six-stakeholder retrospective contrasting conventional consulting engagements with 14-day production sprints, the hard financial outcomes, and why disciplined systems engineering beats billable-hour discovery.
- [CFO and CMO Dialogue: Ad Spend Read in Gross Margin, Not in a Pixel](https://dataongoing.ai/library/cfo-cmo-dialogue-netsuite-digital-marketing-inventory-attribution/): A CFO who will not approve ad spend he cannot reconcile, a marketing lead with 1,800 SKUs and no video, and a COO with two lots past 90 days ask how a NetSuite SDF bundle feeds five ad channels from live ATP, renders SKU photos into video, posts the order with the lot reserved, and reports the return against a Day-0 baseline in realized gross margin.

## Case studies (composite)

- [Four ERPs to One Set of Books in a Chemical Distribution Roll-Up](https://dataongoing.ai/case-studies/pe-roll-up-consolidation/): A sponsor acquired four chemical distributors in 14 months across four different ERPs. Consolidated reporting took 27 days. We migrated all four into one governed NetSuite tenant and took the close to 3 days.
- [EDI and Multi-Store Fulfillment Live in 36 Hours](https://dataongoing.ai/case-studies/same-day-api-integration/): A retail partnership required real-time EDI and multi-store fulfillment syncing. An incumbent integrator quoted $140,000 and 16 weeks. We delivered production integration in 36 hours with no middleware subscription.
- [Scales and Scanners Posting Directly to the Ledger in Cold Storage](https://dataongoing.ai/case-studies/warehouse-scale-edge-automation/): Operators hand-weighed pallets and retyped the numbers, costing $90,000 a year in billing shortfalls. We bridged floor scales and scanners directly into the ERP, cutting weight capture from 90 seconds to 180 milliseconds.
- [Perishable Lot Traceability and a Live Cutover for a Cold-Chain Food Distributor](https://dataongoing.ai/case-studies/perishable-lot-traceability-cutover/): A fresh-food distributor was told its move from a custom legacy platform onto NetSuite would take nine months and a 48-hour operational shutdown. We staged receipts asynchronously, bound lots and expiration dates at the dock, and cut over across a shift change with no missed shipments.
- [A Fourteen-Day Invoicing Lag Removed and Engineering Changes Billed at an Industrial Manufacturer](https://dataongoing.ai/case-studies/manufacturing-dcr-and-auto-invoicing/): Shipments took two weeks to become invoices, and mid-production design changes lived in spreadsheets and never reached the sales order. We paired the carrier scan to the invoice and moved design-change approval inside NetSuite, taking billing from fourteen days to minutes.
- [HubSpot, Stripe and NetSuite Reconciled to the Cent for a B2B SaaS Company](https://dataongoing.ai/case-studies/hubspot-stripe-netsuite-revenue-spine/): Closed-won deals, card payments and revenue recognition lived in three systems joined by generic connectors, and finance spent eight days a month reconciling Stripe payouts. We built direct, idempotent endpoints that split gateway fees at ingestion and generate the ASC 606 arrangement on deal close.
- [Governed AI Workers Inside NetSuite, With a Human Approval Gate on Every Write](https://dataongoing.ai/case-studies/governed-ai-workers-netsuite-mcp/): Generative AI stalls inside the ERP for three reasons: it guesses, it drowns in tokens, and nobody will give it write access. We deployed HelpAirs workers that read through governed SuiteQL tools, propose transactions as drafts, and commit nothing without a human approval.

## Workflow designs (lane diagrams from delivered work; clients as archetypes)

- [Workflow Designs: How Work Moves Through NetSuite and the AI Workers](https://dataongoing.ai/workflows/): Ten lane diagrams from delivered DataOngoing engagements: document intake, AI worker orchestration, a two-model agreement check, voice intake, the single entry point router, the telemetry-first diligence read, a live cutover, a design-change portal and a HubSpot-Stripe-NetSuite revenue chain. Each shows the stages, the handoffs and where a person approves.
- [Document intake: paper to a posted receipt](https://dataongoing.ai/workflows/#document-intake-paper-to-ledger): A packing slip or vendor bill arrives as a PDF, a scan or a phone photo. A vision model reads it, SuiteQL resolves the vendor, the parts and the open purchase-order lines, and the worker proposes an item receipt or vendor bill with its lot subrecords. A person approves; nobody re-types a heat number.
- [Governed AI workers: read freely, write only through a person](https://dataongoing.ai/workflows/#governed-ai-worker-orchestration): A central router dispatches work to specialized workers, each acting only through formal Model Context Protocol tools: parameterized read-only SuiteQL, record schema, and a draft proposal. Reads return live rows. Writes stop at a review modal where a supervisor approves before NetSuite commits the record.
- [The prepared context: pre-flight queries before any model call](https://dataongoing.ai/workflows/#context-packet-for-ai-workers): Commercial models fail inside an ERP when they are asked to remember internal IDs, tax nexus and currency rates across a long context. This design never asks them to. Code runs the queries first and hands the model a small, structured packet of live rows for the one task in front of it.
- [Two-model consensus: independent readings before a draft is proposed](https://dataongoing.ai/workflows/#two-model-consensus-gate): One model’s reading of an invoice is an opinion. Two independent readings compared by code against the purchase order are evidence. The gate proposes a draft only when both readings agree and match NetSuite; any disagreement becomes an exception with both readings side by side, and no path leads from either model to the ledger.
- [Voice intake: a phone call that ends as a scheduled work order](https://dataongoing.ai/workflows/#voice-intake-to-scheduled-work): A caller reaches a conversational agent instead of voicemail. The agent discloses what it is, captures the customer, site, issue and urgency, checks the account and the schedule in NetSuite by read-only calls, and drafts a service order with a held slot. The dispatcher releases it; exceptions go to a person with the transcript.
- [Single entry point: one governed router for every transaction save](https://dataongoing.ai/workflows/#single-entry-point-transaction-router): A dozen scripts on one record type run in an order nobody chose, each loading searches on the user’s thread, and the save freezes or collides. The single entry point replaces the stack with one typed router: synchronous work is in-memory validation, and everything heavy runs behind the save in an idempotent queue.
- [The diligence read: telemetry first, interviews last](https://dataongoing.ai/workflows/#telemetry-first-diligence-read): A conventional discovery interviews people for weeks and delivers a deck. This design reads the system: a read-only role, SuiteQL over execution logs, scripts, searches, roles and integrations, and a dependency map with findings ranked by exposure. The read-out comes 48-72 hours after access, and the fee is credited to the first sprint.
- [Live cutover: dock to ledger with lot traceability](https://dataongoing.ai/workflows/#live-cutover-with-lot-traceability): Physical intake is decoupled from accounting. Scanners post to an idempotent staging RESTlet and are released in under a second; a Map/Reduce worker binds lots and expiration dates, accrues landed cost and releases the SKU to the pick wave. The move off the legacy platform ran as idempotent delta ingestion across a shift change.
- [Design-change portal and invoicing on the carrier scan](https://dataongoing.ai/workflows/#design-change-portal-and-event-invoicing): A design change is a commercial event. A Suitelet logs it, locks the production ticket, reprices against material cost and waits for the project manager’s approval before updating the sales order and work orders in one transaction. Invoicing becomes an event too: the carrier scan triggers verification, posting and dispatch of the invoice.
- [The revenue chain: HubSpot, Stripe and NetSuite reconciled to the cent](https://dataongoing.ai/workflows/#revenue-spine-hubspot-stripe-netsuite): Commercial terms are captured once in the CRM. A closed-won deal provisions the customer, the sales order and the Stripe subscription; each payment webhook posts gross, net and fee as one entry; the ASC 606 arrangement is created on deal close. Every payload is hashed against the Stripe event ID so a retry posts nothing twice.

## Comparisons

- [Big-Four Technology Diligence vs an AI Automation Consultancy](https://dataongoing.ai/compare/big-four-vs-ai-automation-consultancy/): A side-by-side of conventional technology diligence against AI-driven static analysis: timeline, staffing, deliverable, cost structure and what reaches the purchase agreement.
- [Native Integration vs iPaaS Middleware for Portfolio Companies](https://dataongoing.ai/compare/native-integration-vs-ipaas-middleware/): Why most middleware subscriptions in a portfolio company exist to abstract an API that is already straightforward, what they cost over a hold period, and when a native integration is the better call.
- [Fractional Principal Architect vs an In-House NetSuite Administrator](https://dataongoing.ai/compare/fractional-architect-vs-in-house-admin/): Comparing a salaried ERP administrator with a fractional principal architect and specialist pod: fixed overhead, tribal knowledge, response time and what happens at exit.
- [QuickBooks vs NetSuite OneWorld for a Private-Equity Roll-Up](https://dataongoing.ai/compare/quickbooks-vs-netsuite-oneworld-for-roll-ups/): The point in a roll-up at which multiple QuickBooks files stop being a cost saving and start being a discount at exit, and how the consolidation is sequenced.
- [Containerized NetSuite OCR Authority vs Per-Page AP SaaS Platforms](https://dataongoing.ai/compare/containerized-netsuite-ocr-vs-per-page-ap-saas/): Comparing rigid, per-page third-party AP OCR SaaS tools against DataOngoing’s lightweight discovery containers and SuiteScript 2.1 engine across Sales Orders, AP Vendor Bills, line-level 3-Way Matching, and unlimited Custom Records.
- [Self-Hosted Local LLM vs Metered Cloud LLM APIs for NetSuite ERP](https://dataongoing.ai/compare/self-hosted-local-llm-vs-cloud-api-token-tax/): Comparing metered commercial cloud LLM APIs and SaaS AI wrappers against a self-hosted open-weight Local LLM stack (vLLM Qwen2.5-VL + Llama-3.3-70B or Ollama + Docling) for NetSuite OCR, 3-Way Matching, and SuiteQL analytics.

## Leadership

- [Leadership: 13 Years of Consolidation, Automation and Governed AI](https://dataongoing.ai/leadership/): Kyle Castor founded DataOngoing in Las Vegas after 13 years of enterprise systems work (2013-2026) consolidating international acquisitions onto single governed ledgers, engineering high-throughput transaction pipelines, and building deterministic AI for ERP and industrial systems. He is the architect on every DataOngoing engagement and the author of every paper in the library, and he oversees a global team of developers and an American team of marketers, strategists, and architects. Results from his employee roles are published here as founder track record and are labeled as such wherever they appear on this site.
- 2025-present, Founder & Principal Architect, DataOngoing (Las Vegas) (Measured, consulting engagement): HelpAirs: a read-only, least-privilege diagnostic gateway with 700+ query patterns and multi-persona board output; post-acquisition systems diligence on a five-business-day SLA; Specialty produce importer and distributor: catalog and BOM schema reduced 64% (881 to 315); $280,000/yr labor recovered; 100% lot traceability; AI document-intake pipeline in production: 2,881 documents processed to date
- 2022-2025, NetSuite Systems Engineer & AI Application Developer, controlled-environment agriculture operator (Sensei Ag) (Founder track record, pre-DataOngoing): Monthly transactions reduced 85% (200,000 to 30,000) with no loss of ledger fidelity; $200,000/yr in AP middleware eliminated through native banking integration; 250,000+ labels/yr automated; $350,000 freight savings; 60 hours/month of labor recovered
- 2020-2022, NetSuite Solutions Manager & Systems Administrator, global employment services (Velocity Global) (Founder track record, pre-DataOngoing): International acquisitions consolidated into one OneWorld core with zero downtime; 80+ subsidiaries, 100+ currencies, $1B+ revenue flows; $21M/month invoiced on a 3-day turnaround at 97% first-pass accuracy; Overdue AR reduced from $6.5M to $1.4M ($5.1M of liquidity returned); month-end close 8 hours to 15 minutes
- 2013-2020, NetSuite Administrator & Systems Developer, value-added reseller (Technologent) (Founder track record, pre-DataOngoing): 2,000+ engineering certifications tracked in real time for government-contracting eligibility; Recurring contract engine processing 1,000+ invoices/month with auto-renewal and non-payment hold

## Named frameworks

- The 100X Speed Advantage: no junior staff billing hours; diagnostic automation over manual interviews; fixed-price two-week production sprints where code that does not run in production is not billed.
- The results ledger: every automation published with executive hours in, hours or dollars returned over twelve months, the multiple, and a source class.
- The 0-4 Role Risk Index: a fixed five-point scale (0 None, 1 View, 2 Create, 3 Edit, 4 Full) for scoring ERP permission and segregation-of-duties exposure during diligence.
- The Single Entry Point pattern: collapsing dozens of ad-hoc record-level scripts into one governed router to eliminate record-lock contention.
- The 100-Day Unification Blueprint: a four-phase sequence for consolidating acquired entities into a single OneWorld tenant.
- The 10 / 80 / 10 rule: 10% human intent, 80% machine execution, 10% human oversight.
- The operator-first position: business cases are built on repurposing clerical time, not on layoffs; the clerical load moves into code and existing staff move to exceptions, customers and judgment. Stated at https://dataongoing.ai/#operator-first.
- The data-privacy position: a model receives only the rows a task needs, with personal data masked; the model is the client's choice (Claude, Gemini or OpenAI's models recommended; open-weight models on the client's hardware or through TensorWave when data cannot leave); writes go through a person. Summarized at https://dataongoing.ai/#data-privacy, stated in full at https://dataongoing.ai/ai-automation-private-equity/#data-privacy.
- The Five Operating Principles: the delivery methodology at https://dataongoing.ai/methodology/.

## Reference

- [Methodology](https://dataongoing.ai/methodology/): The five first-principles frameworks behind delivery.
- [Executive FAQ](https://dataongoing.ai/faq/): Direct answers on turnaround, timelines, middleware, licensing and the results ledger.
- [Contact](https://dataongoing.ai/contact/): Engagement types, turnaround times, your time in each, and what to send.
- [Interactive tools](https://dataongoing.ai/tools/): Diligence calculator, edge hardware simulator and architecture topology (JavaScript).

## Full corpus

- [Complete text of every page and paper](https://dataongoing.ai/llms-full.txt)
- Raw markdown for each library item is at https://dataongoing.ai/raw/library/<slug>.md and for each playbook at https://dataongoing.ai/raw/playbooks/<slug>.md