Diligence Red-Flag Engine
An AI-extraction-and-review layer that ingests the data room, surfaces the anomalies and the off-market clauses, benchmarks against comparable transactions, and screens beneficial ownership against sanctions and PEP lists — delivered as a risk heatmap the deal team works against.
- Engagement
- 1–2 week sprint per target · risk heatmap report
- Built for
- Deal teams · Operating partners
Financial and legal diligence reads thousands of pages per target. The anomalies, the off-market contract clauses, the related-party transactions — they're in there, but the diligence team finds them by hand in a compressed window.
What this is
A diligence accelerator that takes a data room and a few days, and gives the deal team back a risk heatmap with the anomalies surfaced and the off-market clauses flagged.
Four dimensions:
- Financial-statement anomalies. Trial-balance checks, accruals trajectory, related-party exposure, revenue-recognition anomalies relative to sector peers.
- Contract-clause risk. Extraction across the contract corpus — term length, termination triggers, MFN clauses, change-of-control language, indemnity asymmetry — benchmarked against comparable-transaction norms.
- Beneficial ownership and sanctions. Screening against major lists (OFAC, EU, UK sanctions; PEP databases) plus relationship-graph analysis on ownership chains.
- Regulatory exposure. Industry-specific (healthcare, fintech, defense) regulatory posture mapped against the target's operations.
How it's built
Document AI for extraction (LayoutLM-class for structured-document fields, dedicated contract-clause models for the legal half), graph database for ownership and relationship analysis, sanctions screening via the standard list feeds. Anomaly detection layers a classical statistical baseline with a model-trained-on-peer-set comparison.
What you get
- The risk heatmap with source-document links per finding.
- The contract-clause extraction as a structured dataset (rows: contracts, columns: clause families).
- A short partner-readable summary — what the heatmap concludes, in one page.
- A working session with the deal team to walk findings before close.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 1–2 week sprint per target · risk heatmap report
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Same suite · Private Equity Suite
Technical Due Diligence Engine
A code- and architecture-level diligence report — red/yellow/green risk heatmap across modules, dependency analysis, test-coverage and CI/CD posture, security vulnerability surface, and the operating-partner-readable summary of what the next twelve months of platform work will cost.
- Same suite · Private Equity Suite
Deal-Sourcing Engine
A scoring pipeline that ingests structured (PitchBook-class) and unstructured (news, hiring, patents, alt-data) sources, applies your fund's thesis-specific filters, and ranks candidate targets — feeding your CRM with the top tier weekly.
What buyers ask about this one.
Xapien does sanctions and ownership screening. Why this?
Xapien is excellent at the screening half. This product is broader — it also extracts and analyzes the financial statements and the contract corpus, surfaces anomalies against comparable transactions, and runs the integrations across the full diligence stack in one pass. If your firm already uses Xapien and wants the rest of the stack to match, this layers over it; if you're starting from scratch, this is the integrated alternative.
What does the heatmap actually look like?
Four risk dimensions per target — financial-statement anomalies, contract-clause risk (off-market terms, change-of-control triggers, restrictive covenants), related-party / beneficial-ownership exposure, regulatory / sanctions exposure. Each scored R/Y/G with the specific findings linked to source documents in the data room.
How accurate is the contract extraction?
Extraction is reliable for the common clause families (term, termination, indemnity, exclusivity, change-of-control, MFN). The model surfaces clauses with confidence scores; high-confidence findings go straight into the heatmap, lower-confidence findings get flagged for analyst review. The output is a tool for the diligence team, not a replacement for it.
What's the cost-time tradeoff?
On a typical mid-market deal, the engagement takes 1–2 weeks from data-room access to packaged report, and replaces roughly 30–40 hours of analyst time on the extraction-and-anomaly-detection work. The analyst time saved goes into the judgment work where it actually pays off.
Pricing?
Per-target, scoped to data-room size and the depth of regulatory screening required. Discovery call covers both.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026