Business Context
LexiCounsel is a legal-tech SaaS platform used by 1,200 enterprise customers (fintech, healthcare, and Fortune 500 procurement teams). A new feature, “Matter Briefs”, must summarize thousands of documents per matter (contracts, amendments, emails, court filings) into an executive brief for in-house counsel. Today, associates spend 30–60 hours per matter producing these summaries; missed clauses (e.g., indemnity carve-outs) can create multi-million dollar exposure and regulatory risk.
Your task is to design a GenAI summarization application that can ingest and summarize large legal corpora while being accurate, traceable, and compliant.
Data Characteristics
- Volume: ~8M documents total; typical matter contains 2,000–20,000 documents.
- Document types: PDFs (scanned + digital), DOCX, HTML, email exports.
- Length: 1–300 pages per document; long contracts often exceed LLM context windows.
- Language: 93% English, 5% Spanish, 2% mixed (bilingual exhibits).
- Domain vocabulary: defined terms (e.g., “Affiliate”, “Change of Control”), citations (“§2.3(b)”), legal Latin, and jurisdiction-specific phrasing.
- Noise: OCR artifacts, headers/footers, signature blocks, tables, exhibits, and duplicated versions.
Success Criteria
A solution is “good enough” if it:
- Produces an executive summary (≤ 1 page) and a detailed brief (3–8 pages) per matter.
- Provides verifiable citations: every key claim links to supporting passages (page/section).
- Achieves high factual consistency (target: <2% “unsupported claims” in human audit).
- Runs within 15 minutes per 5,000 documents on a shared GPU cluster (batch mode), and supports interactive drill-down with <2s latency for follow-up questions.
Constraints
- Compliance: SOC2 + customer contractual requirements; no data leaves the customer’s region.
- PII/PHI: must be redacted or masked before model calls when required.
- Auditability: store prompts, model versions, retrieved evidence, and outputs.
- Cost: target <$40 per 5,000 documents (amortized), with caching and dedup.
Requirements (Deliverables)
- Propose an end-to-end architecture for hierarchical summarization across thousands of documents (document → cluster → matter).
- Describe preprocessing for PDFs/OCR, sectioning, deduplication, and handling tables/exhibits.
- Explain how you will ensure faithfulness (evidence-based summaries, citation grounding, hallucination mitigation).
- Include a retrieval strategy (embeddings + metadata filters) to support both summarization and interactive Q&A.
- Define an evaluation plan: automatic metrics + human review rubric; include how you will sample for audits.
- Provide a Python implementation skeleton showing chunking, embeddings, retrieval, and a two-stage summarization pipeline.
