How to Automate an Information Request List

AI can suggest links between an information request list (IRL) and incoming data room documents, extract candidate responses and flag missing information. The Transaction Services team must verify each match before marking a request complete. Document receipt, answer completeness and professional review are different statuses.

Solenor is an AI platform for Transaction Services teams, supporting financial due diligence, IRL workflows and carve-out/TSA analysis with source-linked outputs and human review. Its public showcase is illustrative; connector access and deployment requirements must be agreed before a real engagement.

Assign a stable request ID, workstream, owner, priority, entity perimeter and reporting period. State the expected evidence and the completion criterion. A broad request for financial information is harder to match and review than a dated ledger covering identified entities.

Import only approved sources. Let the system propose document matches, then inspect the document, page or worksheet and the relevant passage. Confirm that the file covers the requested period and perimeter. Record partial responses and follow-up questions rather than closing the request prematurely.

Separate missing, received, under review and reviewed-complete statuses. Keep the original request, linked file version, reviewer and decision together. A confidence score is a prioritization aid, not a sign-off.

Request a Q4 aged receivables ledger and the supporting bad-debt calculation. A spreadsheet titled Receivables_Q4 may be a candidate match, but its name does not establish the reporting date, entity scope or reconciliation to the general ledger.

Verify the totals, aging buckets, currency and provision assumptions. If one entity or the provision calculation is missing, retain a partial-response status and a precise follow-up. This example describes a review method, not a customer outcome or an accuracy benchmark.

Use a defined set of requests reviewed by the engagement team. Count incorrect matches, missed relevant documents, incomplete responses and the effort needed to verify suggestions. Compare with the team's existing process using the same scope.

Do not use sensitive engagement data until permissions, hosting, retention and access controls have been reviewed. AI assistance cannot establish the authenticity or completeness of the data room.