INDUSTRY · LEGALTECH
The 1% of documents that matter, surfaced before the billing clock runs out.
We build contract analysis pipelines, discovery automation systems, and IP search infrastructure that process millions of unstructured documents and return defensible, auditable outputs.
WHY
Legal documents are unstructured at scale. NDAs, MSAs, SOWs, patent filings, court records. We've built NLP pipelines that extract clauses, obligations, and risk signals from 100,000+ document corpora. Classification models trained on legal text, entity recognition tuned for contract entities, and citation parsers that work across jurisdictions.
Discovery automation reduces the most expensive phase of litigation: document review. We built systems that ingest, OCR, deduplicate, and classify millions of documents, then surface the 1% that matter. Privilege log generation, metadata preservation, and defensible deletion procedures are part of the delivery.
IP search requires connecting patent databases, trademark registries, scientific literature, and prior art repositories into a coherent search layer. We built semantic search systems on top of USPTO, EPO, and WIPO data that return conceptually relevant results even when exact keyword matching fails.
WHAT WE BUILD
Relevant capabilities
CAPABILITY · 01
AI & Machine Learning
Contract clause extraction, obligation detection, risk scoring, and NLP models fine-tuned on legal corpora.
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CAPABILITY · 02
Data Engineering
Document ingestion pipelines, OCR processing, metadata extraction, and legal data warehouses.
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CAPABILITY · 03
Custom Platforms
Contract lifecycle management tools, discovery review platforms, and IP search interfaces.
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CAPABILITY · 04
Automation & Integration
Document generation automation, e-signature integrations, and court filing workflow systems.
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CAPABILITY · 05
Algorithms & Optimization
Semantic search algorithms, similarity scoring for prior art, and contract comparison engines.
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CAPABILITY · 06
Infrastructure & DevOps
Secure document storage with encryption, access logging, and retention policy enforcement.
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REDACTION PIPELINE
Document-redaction pipelines
Production-grade redaction is two passes, not one. First pass uses a domain-trained NER model for names, addresses, SSNs, account numbers, medical identifiers, and case-specific entities supplied per matter. Second pass is regex and dictionary-based for known patterns the model misses. Every redaction writes a source-of-truth contract: original offset, replacement value, rule that fired, model version, reviewer attestation. Reviewers see a side-by-side diff with confidence scores per entity. Burned-in redactions go to the production PDF. The structured contract goes to the audit log. False-negative tracking runs against a held-out gold set every release. Throughput stays at 5,000+ pages per hour on a single worker.
Pass 1
Domain-trained NER, 14 entity types
Pass 2
Regex + dictionary, matter-specific
Audit contract
Offset, replacement, rule, reviewer attestation
Reviewer UX
Side-by-side diff, per-entity confidence
Output
Burned-in PDF + structured log
Throughput
5,000+ pages/hour per worker
SAMPLE WORK
What we've shipped
Contract analysis engine that extracted 40+ clause types from 100K+ MSAs with 91% precision on obligation detection.
Discovery automation system that processed 4M+ documents, applied privilege classification, and generated defensible review sets.
Patent prior art search tool using semantic embeddings across USPTO and EPO corpora, returning conceptually relevant results in under 2 seconds.
Contract lifecycle platform tracking obligations, renewal dates, and risk flags with automated stakeholder alerts.
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