AI Due Diligence &
Risk Management.
Proof powered by AI. Every finding linked to source. Every conclusion defensible. Built for VC, PE and M&A.
The shift
AI is no longer just supporting work — it's moving inside the workflow.
- 01Software development
- 02Legal and document analysis
- 03Enterprise knowledge access
- 04Operational workflows
That's what the Forbes AI 50 reveals: AI is no longer a standalone tool — it's the workflow layer.
Problem
Traditional due diligence is broken.
Slow, expensive, prone to human error. Most deals get shallow analysis — funds are forced to decide without proof.
The fund that loses speed loses the deal.
The insight
AI solved analysis. Verification is missing.
AI generates inference at scale. But decisions require deterministic validation. Due diligence fails at the verification layer — and that layer is structurally falling behind as decision speed accelerates.
Broad inference, comparison, relational analysis.
Section rules, cross-checks, source-verification layer.
Every claim traces to a source. A bad citation is an error — not a warning.
The big shift: from probability to proof.
Solution
SolaVeritas.ai compresses due diligence.
Decision speed becomes a competitive edge.
Deep analysis is now economical on every deal.
Contradictions surface. Every finding ships with an auditable source.
The critical difference: the output can be defended.
Who it's for
Where decision speed determines outcomes.
Primary
High speed, high risk. Most deal flow is reviewed only superficially.
Large tickets, long holds. A provable decision is worth a lot.
In strategic acquisitions, missing documents and contradictions translate directly into value loss.
Secondary
Scalable capacity on the DD side of the engagement.
Firms that want a speed edge on client deals.
Small teams missing deep-analysis capacity.
Product · From document to proof — four steps
AI analysis inside a verification system.
Documents are digitised and every fragment is bound to its source.
Critical company data is pulled automatically.
Multi-layer analysis finds contradictions and inconsistencies.
Executive summary + full DD report + contradiction analysis.
Each step is audited by the deterministic layer.
Output
Auditable due diligence — every finding bound to its source.
Critical findings on a single page.
Comprehensive, auditable DD output — every finding bound to its source document.
Inconsistencies across documents, highlighted and flagged.
Critical risks that could threaten the deal — investor-ready, single page.
Click any citation. The source opens.
Every finding — bound to its source.
Founder identity
Co-founded by Bora Gemicioğlu¹, active in M&A.
Financial figure
FY2023 revenue reported as $2.4M¹.
Cross-document contradiction
Share counts disagree across two filings¹².
Missing document
DPA requested — not found¹.
Names. Numbers. Contradictions. Missing pieces. All traceable.
Live demo
Now let's see this — live.
On a real dataset — from document to verifiable conclusion.
Let's look at how every claim is linked to its source, together.
Competition
Different systems.
Different optimization paths.
| Category | What they are building | SolaVeritas difference |
|---|---|---|
| Harvey / Irys | AI legal workspaces for drafting, research, review, agents, and legal productivity | Our focus is defensible and verifiable due diligence conclusions |
| Legora | Collaborative AI workflows and legal operations orchestration | We focus more on evidentiary validation and consistency |
| General AI / LLMs | Broad reasoning, summarization, retrieval, and tool usage | We use AI inside a verification architecture |
| Traditional DD | Human-led legal review and judgment | We structure verification across large document sets |
| SolaVeritas | Verification infrastructure for due diligence | Claim → source → contradiction check → missing evidence → defensible conclusion |
Speed matters. But in due diligence, defensibility is the constraint.
Moat
Defensibility compounds.
It operates through source lineage, control layers, and validation logic.
The system shows not only what exists, but what is missing, contradictory, or unsupported.
Contradiction patterns, legal edge cases, document structures, and failure scenarios compound with usage.
Over time, the moat comes less from model access and more from accumulated verification intelligence: citation lineage, cross-document consistency, defensibility logic.
Not a model moat. A continuously hardened defensibility layer.
Security · Control
AI is already touching your data.We make it controlled.
- Documents pasted into chat tools
- Data leaves the device → vendor servers
- Possible retention and human review
- No audit trail, no contract
- Paid enterprise APIs only — no training on your data
- Stateless processing — no cross-deal contamination
- Full audit trail — every access logged
- Contractual data protection — DPA chain end-to-end
- Runs in your controlled environment (cloud or on-prem)
- Documents remain within your workspace
- Access is restricted and permissioned
- We do not access your data unless explicitly granted
Security · Learning
We don't train on your data.We improve the system — not the model.
- Paid APIs — no training on inputs or outputs
- Stateless calls — no memory across requests
- Each step is an isolated interaction
- No data is reused to train external models
- No dataset aggregation
- No model fine-tuning on client data
- No cross-client learning
- No retention beyond the engagement
- Verification rule engine refinement
- Workflow and structural improvements
- Expert feedback from lawyers and analysts
- Case-driven evolution — without using client data
Next step
Let the decision come from your own data.
Not from what we tell you.
- 01Try it on your own data
- 02Compare with your existing due diligence
- 03See a clear result
In or out?