Private equity · Deal diligence · Fund operations · North America
PE fund diligence: 3 months cut to under 2 weeks with verified, citable AI analysis.
Anonymized case study: a private equity fund cut deal diligence timelines from 3 months to under 2 weeks using a bespoke AI system deployed inside their own environment. Every output includes number-level verification and source citations.
Updated 2026-06-21
Original AI-generated editorial artwork.
- Client
- Anonymized PE fund — North America
- Region
- North America
- Focus
- Private equity · Deal diligence · Fund operations
The challenge
A mid-market PE fund was losing deals inside closing windows. Buy-side diligence on LBO targets required 3 months of junior-associate labor — financial model builds, management assessment, industry analysis, risk identification — at a cost in the hundreds of thousands per deal. Senior partners lacked confidence delegating diligence to consumer AI tools: outputs drifted, numbers got back-solved, and nothing was auditable enough to put in front of an IC. The firm's data requirement was absolute: confidential deal materials could not leave their ecosystem or train a public model.
The build
Consultance AI built a deal diligence engine deployed inside the fund's own infrastructure. The system performs a full blind re-underwrite from the target's source materials: LBO model, levered cash flow analysis, returns across scenarios, covenant headroom, and a clear APPROVE or DECLINE verdict with the supporting thesis. Every headline figure runs through a dedicated number-verification pass with citations to the exact source page. Human-in-the-loop checkpoints require senior sign-off before any analysis leaves the system. Deployed on Claude Enterprise inside the fund's VPC — no data ever touches a public model.
Evidence from the work
- Full LBO re-underwrite completed on representative deal materials during the scoped POC — APPROVE verdict with numbered rationale and source citations verified accurate by the fund's deal team.
- DECLINE output tested on a separate deal: counter-thesis delivered with specific risk factors and supporting figures, not a generic rejection.
- Number-verification layer independently checked every headline figure against the source document before output was finalized.
- Data sovereignty maintained in full: Claude Enterprise deployment inside the fund's own VPC, zero public model training.
- Timeline from AI Audit to production-ready system: under six weeks.
The outcome
- Diligence timeline cut from ~3 months to under 2 weeks on representative deal materials
- Estimated $250K+ per deal in junior-associate labor displaced
- IC-ready deliverable: verdict, supporting figures, audit trail, all source-cited
- Senior partners retain full judgment — AI handles intelligence work, humans make the call
- System owned by the fund: code, prompts, and models transferred at delivery
Recorded impact
Time Saved
52h of 60h
Operator hours reclaimed every week after the system went live.
Efficiency
94% of 100%
Throughput uplift on the workflows we automated end-to-end.
System Score
96 of 100
consultance.ai readiness rating across stack, ops, and observability.
Figures from the existing case-study record; the readiness rating is Consultance’s internal assessment.
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