AI Automation for Private-Equity Portfolios, Measured in Basis Points
What we automate, and the basis points it moves
Operating partners do not buy automation; they buy margin. Each workstream below names the automation, the agent or code that does the work, and the lever it moves in a portfolio company’s P&L.
| Workstream | What the automation does | Basis-point lever |
|---|---|---|
| Technology diligence | AI static analysis of the target’s scripts, permissions and integrations in 48-72 hours | Avoided discount at close; leaks priced before wire |
| Close and consolidation | Automated close checklist, automated eliminations, one set of books | Close 21 days to 3; working capital and reporting cadence |
| Order and fulfillment throughput | Single Entry Point routers, Map/Reduce pipelines, pricing floors | Labor hours, stalled-order revenue, support capacity |
| Documents and devices | OCR intake agents; scales, scanners and printers posting to the ledger | Re-keying labor, carrier penalties, license fees |
| Executive reporting | Natural-language queries over the general ledger through a read-only gateway | Decision latency; analyst hours |
How a few hours of your time returns a hundred
- Read, do not interview. The automated diagnostic runs against the actual transaction, line and system-note records. Your team spends two or three hours granting access and hearing the read-out, not forty hours in workshops.
- One leak, one sprint. Each fourteen-day sprint removes one named leak with code promoted to production. Your time is a scope sign-off, a mid-sprint review and acceptance.
- Agents behind validation rules. Where an agent belongs, it runs behind validation rules and a read-only or least-privilege gateway. Nothing reaches the ledger without passing the validation rules and, where required, a person.
- Publish the row. Thirty days after deployment the before/after telemetry becomes a row in the results ledger with its source class.
The productized offers
48-72 Hour 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.
Two-Week 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.
One-Set-of-Books Close 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.
Floor-to-Ledger Device and Document 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.
Same-Day PE M&A Cryptographic NetSuite Consolidation (24-Hour Audit Vault)
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.
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.
Containerized NetSuite OCR Authority & 3-Way Match Sprint
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).
Sovereign Self-Hosted Local LLM Stack for 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).
The results ledger
100x is the design target. The ledger shows how close each automation gets, with measured rows between roughly 15x and 90x on time and projected rows above 100x. Each figure states whether it is measured in a consulting engagement, a founder-role result, projected, or modeled. Read the ledger.
| Automation | Executive hours in* | Returned over 12 months | Multiple on time | Source class |
|---|---|---|---|---|
| Month-end close pipeline: 8 hours to 15 minutes per close Leadership: global employment services, 2020-2022 | ~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 Leadership: controlled-environment agriculture, 2022-2025 | ~8 | 720 hours/yr (60 hours/month recovered) | ~90x | Founder track record, pre-DataOngoing |
| Native banking integration replacing AP middleware Leadership: controlled-environment agriculture, 2022-2025 | ~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 Leadership: global employment services, 2020-2022 | ~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 Specialty produce importer and distributor (archetype) | ~10 | $280,000/yr in administrative and operational labor recovered; 100% lot traceability | Dollar return; see bps conversion | Measured, consulting engagement |
Five of 12 rows. Full ledger, method and basis-point conversion.
The $300k advisory trap, and the alternative
| Conventional consultancy | DataOngoing.ai | |
|---|---|---|
| Discovery | 3-4 months of interviews, $250,000-$400,000 | 48-72 hours of automated analysis, flat fee credited to remediation |
| Staffing | Partner sells, manager attends, juniors learn on your invoice | Principal architects only |
| Deliverable | A deck whose last slide quotes Phase 2 | Working code in production every fourteen days |
| Billing | Open-ended time and materials | Fixed price; no production, no milestone fee |
| Proof | Case studies without denominators | A published ledger with source classes |
What stays governed regardless of speed
Speed is a property of the analysis, not of the governance. Agents read through a least-privilege gateway with field controls and audit logging; proposed changes are compared against committed scope and checked for net financial impact before anything touches a system of record. The governed structure is built first. That is why the fast part is safe.
Where your data goes when a model reads it
Every design on this site puts a model in front of your records at some point: a packing slip, a vendor master, a role listing, a general ledger. Three questions decide whether that is acceptable to your counsel and your limited partners: what leaves your systems, who can train on it, and who can read it afterwards. The architecture answers all three before a model is chosen, and the model is your choice.
What leaves your systems: the minimum the task needs
- The prepared context. A worker never receives a database or an export. It receives the rows the task needs, assembled by pre-flight queries, with the policy that applies to them. The design, lane by lane.
- Personal data is masked where the task does not need it. Names, bank details, tax identifiers and contact fields are replaced by stable tokens before the prompt and re-joined only inside the system of record, by the person who approves. A model matching a vendor bill to a purchase order does not need to know who the vendor’s controller is.
- Reads return live rows; writes are drafts a person approves. Every prompt, the rows it carried, the model and version, the response and the approver are logged against the record they touched, so an auditor can replay what the model saw and what a person decided.
Who can train on it: the model is yours to choose
Two routes, delivered the same way. The router, the prepared context, the approval gate and the audit log do not change when the model does; only the endpoint does.
| Route | Where the model runs | Where we recommend it | What to weigh |
|---|---|---|---|
| Frontier models under commercial terms | Anthropic’s Claude, Google’s Gemini or OpenAI’s models, through their enterprise or API endpoints on your own account | The default. Document extraction, reasoning over policy, tool use and the two-model agreement check. These models are trained well, documented well, and carry the most delivered work behind the designs on this site | Rows leave your tenant for the duration of the call. The commercial terms exclude training on your prompts and keep retention short; we confirm the current terms in writing at the start of the engagement. Data-residency options differ by vendor |
| Open-weight models on dedicated capacity | Open-weight models (the Llama, Qwen, Mistral and gpt-oss families) on single-tenant GPUs: your own hardware, or dedicated AMD GPU capacity through TensorWave, the Las Vegas AI cloud we work with | Regulated data, contracts that forbid third-party processors, sovereignty requirements, and volumes where per-token pricing stops making sense | Nothing leaves the tenant. More operations work, and a step down in raw accuracy on the hardest extraction, which the agreement check and the exception queue are sized to absorb |
Our recommendation, stated plainly: use Claude, Gemini or OpenAI’s models unless a contract, a regulator or a volume figure says otherwise. The open-weight route is a real option, not a concession, and it is the right one when data cannot leave. In both routes the accounts, the keys and the invoices belong to the portfolio company, so the data relationship is between you and the vendor, and nothing about the data path depends on DataOngoing after the sprint.
Who can read it afterwards
- No consumer chat tools. Work runs through enterprise or API endpoints under a signed agreement, never through a personal chat account.
- No pooled learning across clients. Nothing from one engagement trains, fine-tunes or prompts another. What carries over is the pattern, published here as a modeled composite with the client shown as an archetype.
- Our own access follows the engagement. Least-privilege roles granted for the engagement and removed when it ends; the diligence read runs read-only, and analysis is performed on anonymized, PII-restricted extracts under your NDA, not ours. Confidentiality terms.
- Written into the SOW. The vendor, the endpoint, the retention setting and the masking rules are named in the statement of work with the rest of the scope, so they are checkable at acceptance like every other term.
Frequently asked questions
Do you send our NetSuite data to Anthropic, Google or OpenAI?
Only the rows a task needs, with personal data masked where the task does not need it, through the vendor’s commercial endpoint on your own account and under terms that exclude training on your prompts. If a contract or a regulator says the data cannot leave, the same designs run on open-weight models on dedicated capacity and nothing leaves the tenant.
Can the AI workers run entirely on infrastructure we control?
Yes. The designs do not depend on a vendor: the router, the prepared context, the approval gate and the audit log stay the same and the endpoint changes. Open-weight models run on your own hardware or on dedicated GPU capacity through TensorWave. Plan for more operations work, and size the agreement check and the exception queue for a lower raw accuracy than the frontier models deliver.
Is this NetSuite-only?
The deepest expertise is NetSuite. The same pattern, read the system directly, automate behind validation rules, publish the telemetry, runs against QuickBooks, Sage, Dynamics and bespoke systems in the context of consolidating them into a platform ERP.
How do you state a result in basis points?
Annual dollars returned divided by the portfolio company’s revenue, times ten thousand. The ledger publishes the dollar figure and its source class; the SOW states the revenue base, so the basis-point target is checkable at the end of the engagement.
What is the relationship to dataongoing.com?
DataOngoing.com is the NetSuite managed-services practice of DataOngoing LLC. DataOngoing.ai is the AI automation consultancy for private equity. Same founder, same engineering standards, different buyer.
Talk to the architect, not a salesperson
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. Read the ledger.
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