AI Claims Intelligence Platform

Purpose-built AI infrastructure for VA disability law firms. Not a wrapper around a general-purpose LLM, but a domain-specific intelligence system engineered around the regulatory architecture of 38 CFR, the adjudication logic of the Department of Veterans Affairs, and the document taxonomy of real-world claims files.

Overview

VetClaim Services provides accredited claims attorneys with an AI-powered research and case preparation platform built exclusively for VA disability law. Every layer of the system—from document ingestion to legal drafting—is designed around the specific regulatory structure, document formats, and adjudicative logic that govern the Department of Veterans Affairs.

The platform unifies three capabilities that do not currently exist together in any comparable system: automated C-File intelligence capable of reading and analyzing complete claims folders, a regulatory knowledge engine that maps diagnostic codes and rating criteria across 38 CFR, and an AI legal advisor equipped with 43 specialized tools for claims strategy, evidence analysis, and legal drafting.

The Challenge: Why General AI Fails on VA Claims

VA disability claims are unusually hostile to conventional AI systems. The domain sits at the intersection of federal regulatory law, military medical documentation, and benefits adjudication—each governed by its own terminology, structure, and internal logic.

Document complexity

A typical C-File (Claims Folder) contains 200 to 2,000+ pages spanning 8+ document categories, including Rating Decisions, DD-214s, Service Treatment Records, C&P DBQ Exams, BVA Decisions, Nexus Letters, Notices of Disagreement, and VA Award Letters. These records often arrive as government-scanned PDFs with OCR degradation, inconsistent formatting, and cross-document dependencies that cannot be resolved without domain-specific understanding.

Regulatory density

Title 38 of the Code of Federal Regulations contains hundreds of diagnostic codes, each with distinct rating tiers, evidentiary thresholds, and presumptive frameworks. Combined rating calculations follow whole-person theory—for example, 50% + 30% = 65%, not 80%. Secondary conditions create dependency chains. Presumptive eligibility varies by service era, deployment theater, and exposure type. General-purpose models do not encode these relationships with sufficient accuracy.

Legal precision requirements

This is not a domain where approximate answers are acceptable. Errors carry real legal consequences. A misquoted diagnostic code, an incorrect combined rating calculation, or a missed appeal deadline can cost a veteran years of entitled benefits. The system must therefore be regulation-accurate, not merely “helpful.”

C-File Intelligence Pipeline

The C-File intelligence pipeline transforms raw claims folders into structured, searchable, AI-analyzable case files. It manages the full document lifecycle: secure upload, multi-format text extraction, page-level classification, semantic embedding, and automated case briefing generation.

Document type classification

Each page is automatically classified into one of eight VA-specific document categories, enabling precise retrieval and targeted analysis:

Synthetic training data: sample C-File pages

To train and evaluate the pipeline without exposing real veteran data, we generate fully synthetic C-Files with realistic document formatting, scan artifacts, and internally consistent veteran profiles. Below are sample pages from a synthetic training file:

Synthetic — AI Training Data

VA Award / Notification Letter

Combined rating, individual condition percentages, monthly payment, appeal rights. VBA Regional Office letterhead format.

Synthetic — AI Training Data

Rating Decision — Combined Rating Computation

DC codes, service connection analysis, 38 CFR § 4.25 combined rating table with whole-person math, appeal instructions.

Multi-format extraction with production-grade OCR

Real C-Files rarely arrive in pristine form. They are often scanned on government equipment, copied repeatedly, skewed, faded, or composed of mixed digital and scanned pages within a single file. Our extraction pipeline handles these conditions automatically:

• Digital PDFs — Native text extraction that preserves structural integrity
• Scanned PDFs — HIPAA-compliant cloud OCR with automatic retry and fallback
• DOCX / TXT — Direct text extraction with intelligent page segmentation
• Hybrid documents — Per-page format detection, combining native extraction and OCR within the same file

Hybrid search: BM25 + cosine similarity with Reciprocal Rank Fusion

C-File search combines two complementary retrieval methods using Reciprocal Rank Fusion (RRF): a BM25 keyword index for exact term matching and a cosine similarity vector search over per-page semantic embeddings stored in pgvector. BM25 captures exact diagnostic codes, form numbers, and regulatory citations. The vector branch retrieves semantically related material that keyword search would miss. RRF merges both ranked result sets into a single retrieval layer without requiring score calibration between the two methods.

As a result, a search for “knee injury” will return not only pages containing that exact phrase, but also pages discussing “range of motion deficit in the right lower extremity” or “DeLuca findings for DC 5260”—even where the original search terms do not appear verbatim.

Case Briefing AI: Lexi

Lexi is the platform’s automated case briefing system—a multi-pass AI analyst that reads an entire C-File and produces a structured intelligence briefing for the attorney. Named after the paralegal workflow it replaces, Lexi operates the way a senior VA disability paralegal would—triaging the file, identifying high-value documents, performing deeper evidence analysis, and synthesizing the results into an actionable briefing.

Multi-pass architecture

Rather than forcing an entire C-File through a single LLM call—which would exceed context limits and produce shallow output—Lexi uses a proprietary multi-pass architecture designed for depth and precision:

1. Triage pass — Rapidly scans all pages, identifies high-value documents for deeper review, and builds an evidence inventory
2. Deep analysis pass — Reads flagged pages in full, extracting favorable evidence, unfavorable findings, and evidentiary gaps
3. Synthesis pass — Combines triage and deep-analysis outputs into a structured, attorney-ready briefing

Structured briefing output

Each Lexi briefing contains:

• Evidence inventory — Every document cataloged by type, date, and source
• Favorable evidence — Findings that support the claim, with page citations
• Unfavorable evidence — Findings the VA may rely on to deny or underrate the claim
• Evidence gaps — Missing nexus letters, buddy statements, or medical records
• Key conditions — Identified conditions and associated diagnostic codes, where detected
• Recommended next steps — Priority-ranked attorney actions

Regulatory Knowledge Engine

The platform maintains a continuously updated regulatory knowledge base sourced directly from the Electronic Code of Federal Regulations (eCFR). This is not a static reference archive. It is a live regulatory system that ingests, structures, and cross-references the corpus governing VA disability adjudication.

38 CFR ingestion pipeline

Our ingestion system parses the authoritative XML source of Title 38 from the eCFR API (ecfr.gov/api/versioner/v1), extracting structured data at every level of the regulatory hierarchy: Parts, Subparts, Body Systems, Sections, Diagnostic Codes, and Rating Criteria Tables. Extracted prose is chunked at semantic boundaries, embedded, and stored in pgvector for cosine similarity retrieval. Structured rating criteria are stored in normalized PostgreSQL tables for deterministic lookup. The result is a dual-representation—both searchable and queryable—of VA rating law.

Verified regulatory facts

Beyond structured regulatory data, the platform maintains a curated corpus of 170+ verified regulatory facts—hand-reviewed legal assertions tied to specific CFR or USC citations, each tagged by relevance category and priority. These facts form the highest-authority knowledge layer in the system. The AI is not permitted to contradict a verified fact under any circumstance, regardless of what lower-authority sources suggest.

Each fact includes: the legal assertion, the controlling CFR or USC citation, the date of last verification, and the conditions under which it should be surfaced. This is not a generic knowledge base—it is a regulation-by-regulation accuracy layer.

Secondary conditions & presumptive categories

The knowledge engine maintains structured databases for:

• Secondary condition relationships — Documented causal and aggravation links between conditions, with medical rationale and diagnostic code mappings. Search uses stemming, synonym expansion, and a domain-specific abbreviation dictionary (e.g., PTSD ↔ post-traumatic stress disorder, GERD ↔ gastroesophageal reflux disease, TBI ↔ traumatic brain injury) to handle the wide variation in how conditions are referenced across medical and legal documents.
• Presumptive conditions — Complete mappings for Agent Orange, PACT Act, Gulf War, radiation, POW, and other presumptive categories, with service era requirements, deployment theater qualifiers, and regulatory citations.
• State-level benefits — 50-state veteran benefit database with structured eligibility criteria (rating thresholds, combat requirements, residency, discharge type, service branch) for automated matching.

AIDEN: AI Claims Advisor

AIDEN is the attorney-facing AI advisor at the core of the platform. Unlike chatbot systems that depend on a single LLM prompt, AIDEN is a multi-tool AI system with 43 specialized capabilities spanning claims strategy, evidence analysis, legal drafting, regulatory research, and case management.

When an attorney asks AIDEN to “find secondary conditions for PTSD” or “draft a Higher-Level Review for this denied tinnitus claim,” it selects the relevant tools, assembles context from multiple knowledge layers, and produces a regulation-backed response with citations.

Tool Ecosystem: 43 Specialized Capabilities

Multi-Layered Retrieval Architecture

The most important architectural decision in the platform is its three-tier fact hierarchy. Unlike systems that treat all retrieved context as equivalent, this architecture enforces a strict authority ranking to eliminate the most dangerous failure mode in legal AI: hallucinated or incorrect regulatory citations.

Intent-driven namespace routing

Every user query is first classified into one of eight intent categories. That classification determines which pgvector namespaces are searched, which structured tables are queried, and which verified facts are eligible for injection.

A question about combined rating math routes to rating-criteria sources. A question about PTSD secondary conditions routes to the secondary-conditions and claims-process namespaces. A state-specific benefits question routes to the state benefits index.

This routing prevents a common failure mode in single-namespace RAG systems: high-similarity retrieval that is topically adjacent but legally irrelevant. A query about “tinnitus rating criteria” should return 38 CFR § 4.87, not unrelated BVA case law mentioning tinnitus.

Context window optimization with prefix caching

The system prompt is assembled in three independently cacheable blocks to maximize Anthropic API cache hit rates:

• Static instruction block — identical across users and heavily cached
• Session context block — veteran profile and case data, stable within a conversation
• Dynamic retrieval block — fresh RAG results, verified facts, and structured data assembled per query

Follow-up queries within the same session also benefit from Redis-backed retrieval caching, reducing redundant vector searches for semantically equivalent questions.

Security & HIPAA Compliance

The platform is built for HIPAA-regulated environments from the ground up. All infrastructure operates under signed Business Associate Agreements (BAAs), with access controls enforced at every layer.

Integration Ecosystem

The platform integrates with the software law firms already use, reducing duplicate data entry and keeping external systems synchronized.

Platform Metrics

These are production figures from the live platform, not theoretical benchmarks.

VetClaim Services is built for accredited VA disability claims attorneys. For partnership inquiries, contact legal@veteranclaimservices.com.