The problems we solve

• Manual review costs — Linear review at $1–$3/doc doesn't scale to modern data volumes.
• Privilege risk — Missed privilege calls cost reputations and cases.
• Multi-format ingestion — Email, chat, voice, video, and structured data all need review.

What's included

• Multi-format ingestion — Email, Slack, Teams, audio transcripts, scanned PDFs.
• Predictive coding (TAR 2.0) — Continuous active learning to surface responsive docs first.
• Privilege & PII detection — Custom models for attorney-client and regulated data.
• Production & redaction — Bates numbering, redactions, and load files.

Outcomes you can measure

• 60–85% review time reduction
• Higher privilege accuracy
• Defensible audit trail
• Lower per-matter cost

Technology stack

Python · PyTorch · Elasticsearch · Postgres · S3 · Tesseract

Typical timeline

8–16 weeks to a production-ready MVP. Fixed-scope discovery in week 1, working software every 2 weeks.