Multimodal Document Intelligence for Claims Review & Compliance

We built a document intelligence platform that turns complex claim and compliance paperwork into structured, searchable data that teams can act on. The system ingests scanned forms, PDFs, email threads, attachments, and supporting evidence, then links them into a single case context with consistent identifiers and provenance. Instead of reviewing each document in isolation, users see the full record as a coherent bundle with extracted fields, timeline events, and cross-document references.

Our platform combines automated extraction with human review in one operational workflow. Analysts can validate key data points, flag inconsistencies, and generate structured outputs that feed downstream systems. This foundation supports faster triage, more consistent decisions, and an auditable trail that explains how each outcome was reached.

What this solves

Claims and compliance processes often rely on document-heavy workflows where critical information is buried in unstructured content. Data needed for eligibility checks, policy alignment, and reporting is scattered across forms, correspondence, and evidence files, frequently with inconsistent naming and partial context. Teams spend significant time reading, re-keying information, and resolving discrepancies across documents, which slows cycle times and increases the risk of errors.

The operational impact grows when volumes spike or rules change. Manual review does not scale cleanly, and even small interpretation differences between reviewers can create inconsistent outcomes. Without structured, case-level visibility, it is hard to detect patterns like repeated missing fields, unusual supporting evidence, or systematic compliance issues. Audits become painful because the rationale for decisions is spread across emails, notes, and multiple versions of the same document.

We addressed this by building a multimodal system that extracts, links, and reasons over documents while keeping evidence traceable. The platform bridges unstructured inputs with structured case models so reviewers can focus on judgement and exceptions rather than administrative reading and data entry.

How we did it

We designed an ingestion layer that supports both high-volume document intake and controlled case creation. The platform accepts PDFs, scans, images, and email-based submissions, then standardises formats and applies OCR where needed. Documents are stored in a lakehouse-style foundation with versioning, metadata enrichment, and consistent case linking, enabling both operational retrieval and analytics across large historical corpora.

On top of this, we implemented AI components for extraction and retrieval-augmented reasoning. Models identify document types, extract key fields and entities, and detect inconsistencies such as mismatched identifiers, missing signatures, or conflicting dates. A retrieval layer surfaces relevant passages across the case bundle, allowing reviewers to quickly verify claims against policy text, prior correspondence, or supporting evidence. Outputs remain grounded in source documents, with citations at the paragraph or field level to support auditability.

We embedded these capabilities into a practical review workflow. Users can validate extracted fields, approve suggested classifications, and generate structured summaries and reports that integrate with downstream case management and reporting systems via APIs. The architecture supports privacy and regulatory constraints through role-based access, configurable retention policies, and controlled data residency, while remaining modular so organisations can add new document types and rules without reworking the core pipeline.