How do you build an automated due diligence research pipeline?
Updated
Build it as one record, not a chain of tools. Collect sources into a single place, extract claims with a link back to each source, test those claims against your thesis, log every gap as an open question with an owner, have a person review the findings, and carry the reviewed record into the committee memo. Automate collection, extraction and gap tracking first; keep judgement and sign-off human.
Key takeaways
- /One record across all six levels beats a chain of disconnected tools.
- /Automate collection, extraction and gap tracking first.
- /Every claim keeps a link to its source.
- /People review findings and sign off; the pipeline never decides.
Most teams try to automate diligence by adding tools: a data provider here, a document reader there, a memo generator at the end. The pipeline breaks at every handoff, because each tool keeps its own copy of the facts and nobody can trace a sentence in the memo back to its source.
A pipeline that holds up under committee scrutiny keeps one linked record across the six levels of private markets: the asset or company, the deal, the vehicle, the fund, the manager and the investor's portfolio. A finding about a company carries through to the deal, and a concern about a manager stays visible when the same manager appears in a later fund.
The practical test is simple: pick any statement in the final memo and ask where it came from. If the answer takes one click, the pipeline works.
How Reuben AI compares
The six pipeline steps and what to automate in each.
| Attribute | Reuben AI | Human role |
|---|---|---|
| 1. Collect | Automate: data room, filings and research gathered into one record | Confirm scope and access |
| 2. Extract | Automate: claims pulled out with a link to the source page | Spot-check extraction |
| 3. Test against thesis | Assist: supporting and contrary evidence set side by side | Judge fit |
| 4. Track gaps | Automate: each missing item logged with an owner | Chase and close |
| 5. Review | Assist: findings grouped by workstream | Approve or challenge |
| 6. Decide | Assist: memo drafted from reviewed findings only | Committee decision |
Frequently asked questions
What should we automate first in due diligence?
Source collection, claim extraction and open-question tracking. They take the most analyst time and are the easiest to check.
Can AI run due diligence on its own?
No. AI can gather, extract and organise evidence, but people judge thesis fit, approve findings and make the decision.
How do we keep the memo traceable?
Draft the memo only from reviewed findings that already carry source links, so every sentence can be followed back to its evidence.
Does the pipeline end at the committee?
No. The same record continues into ownership, so later monitoring tests the original thesis rather than starting again.
How is this different from a data room?
A data room stores documents. A diligence pipeline turns them into linked claims, gaps and findings that reach a decision.
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