Industries · Last updated Jul 2026
AI financial due diligence that reads every page.
No deal team reads a full dataroom. There isn't time. We build diligence pipelines where AI reads everything, extracts and cross-checks the numbers, and hands your team a drafted workbook with a flag on everything that doesn't tie.
Talk through your operationA closer look
Work shaped around ai financial due diligence
We cut one diligence workflow from 3 months to 2 weeks. Every extracted number links to its source page and material figures are re-derived by an independent check before they survive into the draft.
No deal team reads a full dataroom. There isn't time. We build diligence pipelines where AI reads everything, extracts and cross-checks the numbers, and hands your team a drafted workbook with a flag on everything that doesn't tie.
Where we help
AI workflows for ai financial due diligence
Full dataroom ingestion and indexed extraction
Financial statement tie-out and cross-checking
Quality of earnings analysis support
Red flag and inconsistency detection
Diligence report and workbook drafting
The intended difference
Operational value
- Every document read, not sampled
- Numbers verified against source, automatically
- Deal teams reviewing flags instead of hunting for them
Inside the work
Sampling was always the compromise
Traditional diligence samples because humans are the scarce resource. You check the top 20 contracts, spot-check the revenue ledger, and hope the pattern holds. AI removes the constraint: every contract gets read, every ledger line gets checked against the statements, and the CIM's numbers get traced to the underlying documents one by one.
The interesting output isn't the extraction, it's the exceptions. Revenue recognized ahead of contract terms. An addback that appears in the adjusted EBITDA bridge but not in the ledger. Customer concentration that the CIM's grouping obscured. Those flags used to depend on an associate happening to look in the right place. Now they surface by default.
Inside the work
The verification layer is the product
Anyone can pipe a dataroom into a model and get plausible-looking output. The build that survives contact with a real deal is the one with a verification layer: independent re-derivation of material numbers, source links on every figure, and a hard rule that unverified means labeled, not assumed.
That's how we build. Two passes must agree before a number is trusted. Humans clear every flag. The draft your team works from is grounded in pages it can click through to, which is also exactly what the IC will ask for.
A few useful details
Questions about ai financial due diligence
What is AI financial due diligence?
Using AI to read the full dataroom, extract financials into structured workbooks, cross-check figures between documents, and flag inconsistencies for human review. It replaces the sampling approach with full coverage, because the model has time to read everything.
How accurate is AI extraction on deal documents?
High enough to work from, if verified. Our pipelines re-derive material numbers with an independent pass that must agree with the first, and every figure links back to its source page. Mismatches queue for a human. Unverified numbers are labeled, never silently trusted.
How much faster is AI-assisted diligence?
Our reference build cut a 3-month workflow to 2 weeks. The reading and extraction collapses to hours. What remains human is judgment: which flags matter, what to renegotiate, and what kills the deal.
Does this replace the QoE provider?
No. It makes their input cleaner and your review of it sharper. The pipeline preps tied-out schedules and flags before the accountants arrive, so their hours go to judgment instead of data assembly. Some clients also run it as a check on the QoE draft.
Keep exploring
Further reading
Hebbia vs Rogo
How the two finance AI platforms compare on agents, document analysis, integrations and pricing, from sourced facts.
AlphaSense competitors for diligence teams
The four groups that actually compete with AlphaSense, what each one leaves out, and when a private build beats another seat-based subscription.
BlueFlame for private equity analysts
What the tool actually does for an analyst day, where it stops, and what a team still has to build itself.
Rogo vs Model ML for private equity
Side-by-side on deal workflows, dataroom handling, and what each one leaves to your associates.
Rogo competitors for private equity teams
The competitors and alternatives worth shortlisting, including the build-it-yourself option and when it makes sense.
Allocator due diligence pack
A free Claude pack that runs manager due diligence the way an allocator team does it, step by step.
AI consulting for private equity
Diligence, portfolio reporting, and deal desk systems built for PE teams, with the approval gates that LPs and auditors expect.
Next step
Want this mapped to your operation?
Book a call and we will identify the first AI workflow worth shipping.
Explore the rest
Services
Locations
