The only B2B SaaS platform purpose-built for US insurance defense law firms. Generate coverage opinions in 15 minutes, cut LEDES rejections to under 2%, and automate matter management — all with attorney-in-the-loop review.
The Daily Pain Points We Eliminate
Three modules. Zero legal liability for the first two. Maximum ROI from day one.
Upload a claim packet — policy PDF, complaint, supporting docs. Our RAG pipeline extracts, chunks, embeds, and retrieves relevant case law from CourtListener's 6.9M cases. Claude generates a structured opinion draft in under 15 minutes. Attorney reviews and exports as DOCX or PDF.
AI-suggested UTBMS codes as you type. Real-time carrier rule validation with color-coded entries. Generate LEDES files with under 2% rejection rate.
Full case lifecycle from intake to close. Conflict checks, deadline tracking, document storage, and team assignment — all isolated per firm with row-level security.
Built on Ambuj Kumar Tripathi's fine-tuned Llama 3.2 1B model — trained on 14,543 Indian Legal QA pairs covering the Constitution, IPC, and CrPC. GGUF Q4_K_M quantization means it runs on CPU with no GPU required.
For the US insurance defense market, this model serves as the domain-locked legal reasoning backbone, augmented by our RAG pipeline with CourtListener case law and carrier-specific billing rules.
⚠ Version 0.1 Alpha — 100 training steps. Full epoch training planned. Not for production legal advice.
Deliberately scoped to get into the hands of 2–3 US pilot firms within 6 months.
The core backbone of the platform. Case intake, conflict check, deadline tracking, document storage, and team assignment — all with multi-tenant isolation.
The biggest daily pain point. AI-suggested UTBMS codes, real-time carrier rule validation, and LEDES file generation — cutting rejection rates from 15–25% to under 2%.
RAG-based coverage opinion generation. No fine-tuning required — the RAG approach works with the actual policy documents the attorney uploads, augmented by CourtListener case law.
Retrieval Augmented Generation — no expensive fine-tuning. The AI reads the actual policy document before answering.
AI jobs run via AWS SQS + BullMQ — never synchronously in an API route. A 15-minute generation job would timeout an HTTP connection. The browser polls /api/jobs/{'{'}jobId{'}'}/status every 5 seconds for live progress updates.
75% of the MVP is built on open source tools at near-zero licensing cost.
2 developers. 24 weeks. 4 phases. In that order. No shortcuts.
6-month MVP build. 2 developers. US-first market.
Law firms won't trust AI legal outputs without expert sign-off.
Auto-submission feature cannot launch without enterprise partnership.
Large law firms (50+ attorneys) will not buy without SOC 2.
Attorneys won't trust coverage opinion drafts if quality is insufficient.
CaseMark or Harvey could add a billing module.
Bedrock inference costs spike with heavy usage.
Build → Pilot → Partner → Scale. In that order. No shortcuts. We're looking for 2–3 US insurance defense law firms for our pilot program.
Prepared by Neural Arc Inc. — Product & Engineering Division · hello@neuralarc.ai · Confidential · April 2026