Empliance's OneVision TPRM Platform with AI & MCP

TPRM platform (OneVision) manages the full third-party risk lifecycle on a single system — vendor onboarding, due diligence questionnaires, evidence collection, maker–checker review, risk scoring and dashboards, with a complete audit trail. It is live in production with a leading payments bank, covering third-party due diligence, banking outlet monitoring, sanctions screening and adverse media checks, and every piece of evidence (documents, photos, video, audio) is stored securely against the specific control it supports.

The platform's AI layer works at three points in the assessment cycle. First, evidence validation: uploaded documents are auto-classified and mapped to the right control, OCR extracts entity names, certificate numbers and validity dates, and the details are cross-verified against live sources such as GSTIN, MCA and sanctions databases. Tampered, expired or reused documents are
flagged, and a gap check confirms the evidence actually substantiates the answer it supports. Second, questionnaire autofill: answers are pre-filled from the uploaded evidence with the source clause cited, registry and API data populates company profile questions before the vendor even responds, and prior-cycle answers carry forward on repeat assessments with only the changes highlighted. Third, questionnaire auto review: submitted responses are checked for completeness, mismatches between answers and attached evidence, and contradictions across sections; each answer is graded against the client's control framework with rationale and rolled up into section and overall risk scores, and a draft review summary — key risks, weak controls, missing evidence and follow-up questions — is generated for the reviewer to edit rather than write from scratch.

The AI layer is built on the Model Context Protocol (MCP), the open standard for connecting AI models to enterprise systems. Through MCP, the platform's AI connects to verification sources (registries, sanctions lists, adverse media feeds) as governed tools rather than hard-coded integrations — new sources plug in without rework, and every AI data access is scoped, logged and auditable. The same standard makes the platform agent-ready: a client's own AI assistants can securely query vendor risk status, pull assessment summaries or trigger a re-screening through the platform's MCP interface, with the platform's access controls enforced end to end.

Throughout, the maker–checker model stays intact: AI drafts, flags and scores with a confidence level and evidence link on every output, while a human reviewer approves, overrides or sends back each decision. The result is faster assessment turnaround with tighter, more consistent scrutiny