VegradeAI engineering
Legal technology8–12 week production programIllustrative program

Citation-backed M&A diligence RAG

Legal teams need defensible speed—answers must move quickly while remaining traceable to source spans. This program shape delivers a retrieval stack with explicit provenance, review SLAs, and eval gates so partners can trust answers under scrutiny.

Challenge

Deal teams in M&A practices get buried in redlines across fragmented deal rooms with no unified, trustworthy search. Defensibility under partner scrutiny is non-negotiable—answers must trace back to bates ranges and source spans.

Approach

Citation-backed retrieval over curated corpora with layout-aware parsing, hybrid lexical + dense retrieval, cross-encoder reranking, and human review queues for low-confidence answers. Confidence tiers drive mandatory escalation.

Architecture & delivery

  • Ingestion pipeline with virus scan, OCR hooks, and retention policies per matter
  • Hybrid lexical + dense retrieval with cross-encoder reranking and abstention
  • Structured citations mapped to source spans and bates ranges
  • Async review queue with SLA timers and partner escalation paths

Governance & controls

  • Least-privilege access to matters; immutable access logs
  • Redaction profiles for PII before model calls
  • Change management artifacts for enterprise procurement

Operational outcomes

  • Offline relevance suite seeded from real failure modes—not toy benchmarks
  • Confidence tiers with mandatory human review below agreed thresholds
  • Tenant-scoped corpora with exportable audit logs for disputes
  • Staged rollout: pilot workgroup → department → firm-wide

Programs of this shape land when traceability and consistent citation formatting drive adoption first—speed follows from reduced rework. Review teams typically adopt the workflow as the default first pass for standard M&A playbooks within the first production quarter.

Illustrative content. This brief describes a reference program shape—architecture, governance, and target outcomes drawn from common industry patterns. Vegradeis pre-launch on named client references; numbers shown are targets we design toward and depend on data readiness, baselines, and adoption in any future engagement.