The Results Ledger
Source classes
Observed in a DataOngoing client environment. Client shown as an industry archetype.
Delivered by Kyle Castor as an employee (2013-2025), as stated on the public resume of record. Not a DataOngoing engagement.
Measured on DataOngoing’s own operations, with the manual baseline estimated.
Arithmetic on a measured baseline with stated assumptions. Not yet observed after deployment.
Illustrative scenario built from several engagements. Figures illustrate assumptions, not a named result.
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 |
| Perishable FIFO lot allocation and profitability close: 14-hour spreadsheet close to a 20-minute automated run Proof card: perishable FIFO allocation engine | ~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 Floor-to-Ledger Device and Document Automation | ~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 Method and sources | 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 The Single Entry Point pattern | ~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 Script and permission inventory benchmark | ~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 Technical arbitrage memo | 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 Live GAAP P&L and SHA-256 Merkle console (Project Allium) | ~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 |
*Executive hours in are DataOngoing’s estimates of client-side time (access grants, decisions, reviews, sign-off). They are replaced by logged hours on each new engagement. Hours returned are annualized from the stated monthly or per-cycle figure.
Outcomes stated by the owner, outside the 100x arithmetic
Four results Kyle Castor stated on 2026-10-23 and classed himself. They carry no executive-hours denominator, so they are not ledger rows and do not enter the 100x multiples above; the three client outcomes are shown with the client as an industry archetype, in the owner's wording, and the founder-role figure is the pre-DataOngoing track record.
Measured, consulting engagement
Measured, consulting engagement
Measured, consulting engagement
Founder track record, pre-DataOngoing
Converting dollar returns to basis points
Private-equity readers think in basis points of EBITDA margin, so here is the conversion. Basis points equal annual dollars returned divided by portfolio-company revenue, times ten thousand. Revenue bases below are illustrative; substitute your own.
| Published annual return | On $50M revenue | On $100M revenue | On $250M revenue | Source class of the dollar figure |
|---|---|---|---|---|
| $200,000/yr middleware eliminated | 40 bps | 20 bps | 8 bps | founder-role |
| $280,000/yr labor recovered | 56 bps | 28 bps | 11 bps | measured |
| $55,725/yr script consolidation | 11 bps | 6 bps | 2 bps | projected |
| $621,000/yr freight recovery (33% to 65%) | 124 bps | 62 bps | 25 bps | modeled |
Method and sources
- Each row is one automation. Hours in count only the client-side executive time, not DataOngoing engineering hours, because the claim is about the buyer’s time.
- Returned hours are annualized from the measured monthly or per-cycle saving. Dollar returns are stated as published and converted to basis points separately.
- The multiple is hours returned divided by hours in. Where the return is cash or enterprise value, the row says so instead of inventing a time multiple.
- Source class is mandatory. Founder-role rows were delivered as an employee and are shown so the record is complete, not to imply a DataOngoing engagement.
- The internal file-organization row is included because it is the cleanest measured AI-automation ratio we hold (367 files, about 3 minutes against an estimated 3.5-4 hours by hand, June 2026), and because we apply the same automation to client document sets.
Delivery speed, the other half of the arithmetic, is published with denominators in the 10-50x benchmark. The fee guarantee is 10x and is stated separately in every engagement letter.
Frequently asked questions
Is 100x a marketing number?
It is the design target, and the ledger shows how close each automation gets. The measured rows run from roughly 15x to 90x on time; the projected rows exceed 100x and are labeled projected. We publish the arithmetic so you can disagree with a specific row rather than with a slogan.
Why are founder-role results on a company site?
Because they are the largest documented outcomes and they were delivered by the same architect who leads every DataOngoing engagement. They carry their own source class and the note that they were not DataOngoing engagements.
How does 100x on time relate to the 10x fee guarantee?
They are different denominators. 10x is value returned against the fee paid, and it is the guarantee in the engagement letter. 100x is work returned against the executive hours the client invests. A $100,000 sprint that removes a $1,000,000 bottleneck satisfies the first; a six-hour executive investment that returns 600 hours of automated work satisfies the second.
What would move a projected row to measured?
Thirty days of production telemetry after deployment. The sales-order router rows are projected because the baseline latency was measured in a client sandbox and the recovery is arithmetic on that baseline.
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.
Schedule a strategy call Run the five-role calculator
The direct email and phone number are shared in the receipt of a verified request.