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 | Agentic 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 a contract. Where an agent belongs, it runs behind a schema contract and a read-only or least-privilege gateway. Nothing reaches the ledger without passing the contract and, where required, a person.
- Publish the row. Thirty days after deployment the before/after telemetry becomes a row in the 100x time ledger with its source class.
Four 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-governed 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 agentic 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.
The 100x time 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 11 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.
Frequently asked questions
Is this NetSuite-only?
The deepest instrumentation is NetSuite. The same pattern, read the system directly, automate behind a schema contract, 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
AI automation for private-equity portfolios, measured in basis points: a few hours of operating-partner time in, hundreds of engineering hours and margin out, delivered as working code in two-week sprints.