Roshan Kumar

Roshan Kumar

AI Product Manager

I build agentic products
for enterprise workflows.

Independent product design

VendorLens

Evidence a reviewer can trace. Decisions a human can own.

Ownership
Problem framing, product scope and system design
User
Third-party risk analyst
Scope
New-vendor onboarding

Vendor documents can disagree, omit material facts or contain claims the source does not support. A fluent summary does not resolve those problems.

Designed a workflow that turns documents into a citation-backed evidence pack, policy results and a recommendation. The analyst makes the final decision.

Follow one case

A questionnaire says a control is enabled. An assurance report lists an exception. What should the system do?

Illustrative design walkthrough, not a live assessment.

Documents to human review Documents become candidate claims, a validated evidence ledger, policy results, investigation and a human review package. Connecting lines highlight the selected stage. DocumentsClaimsEvidencePolicyInvestigateReview

Step 1 of 6

Preserve what was submitted.

Keep the questionnaire and assurance report as immutable originals, each with its own identity and version.

Design choice: Every downstream claim must lead back to the exact source used.

Output: two source documents, preserved separately.

What if a tool fails?

A tool failure is a workflow state, not evidence that the vendor failed a policy.

Record the failure and checkpoint. Retry within allowed limits; if unresolved, block completion. A failed policy evaluation cannot become a ready-for-review package.

System architecture

The full review flow: traceable evidence, controlled investigation and a human decision.

VendorLens architecture: case intake, document extraction, an evidence ledger, policy evaluation, controlled agent investigation, a readiness gate and human review. Feedback and clarification loops return through validation; shared controls, durable stores and audit support the flow.
Full product design. The local pilot implements intake, document processing, evidence validation and the ledger.

Product decisions

Validate claims before using them.

Separate extraction from validation. Check the citation location, quoted text and support for the claim. Keep rejected and uncertain evidence visible.

Preserve disagreements.

A source-supported claim can still conflict with another source. Record both; do not let reconciliation silently choose a convenient answer.

Keep policy outside the model.

Apply versioned rules to an exact evidence-ledger revision. New evidence triggers reevaluation; missing evidence must not become an automatic pass.

Separate reasoning from permission.

The agent chooses an investigation action. The harness validates and records it. Independent controls enforce permissions, approvals, budgets and retry limits before execution.

Make completion a checked state.

Gate the review package on successful policy evaluation, current evidence references and required findings. An agent saying “done” is not enough.

Scope choice: new-vendor review with a human decision. Renewal, continuous monitoring, autonomous onboarding and policy creation sit outside this product scope.

Evaluation design

Primary risk: an unsupported “proceed” recommendation.

Offline golden cases cover contradictions, missing evidence, scanned-table exceptions, lost qualifiers, wrong policy versions and prohibited actions.

Measure citation correctness, missed gaps, false-pass recommendations, recovery behavior and human corrections. Track analyst review time and clarification rounds alongside quality.