Products we're building

Problem

Associates spend hours manually searching contracts and case files for relevant precedent, with no reliable way to verify an AI-generated summary against the actual source text.

Solution

A retrieval-grounded research assistant scoped to a firm's own document set, where every answer carries a citation back to the exact clause or case it came from.

Architecture

Structure-aware document chunking preserves clause and heading boundaries. Hybrid semantic + keyword retrieval surfaces exact defined terms alongside conceptually similar passages. Matter-scoped indexes keep each client's documents isolated.

Problem

Design engineers lose flow switching between their CAD tool and a stack of PDF standards documents every time a spec or clearance question comes up.

Solution

An assistant with typed access to both the active drawing and an indexed spec library, so standards lookups and geometry checks resolve inline instead of interrupting the workflow.

Architecture

A spec knowledge base built on structure-aware retrieval over design standards and codes, paired with a CAD-integration layer that reads the active drawing's geometry to run validated clearance checks as function calls, not free-text guesses.

Problem

Institutional knowledge is scattered across wikis, chat threads, and PDFs. New hires and existing staff alike re-ask questions that already have answers buried somewhere no one can find.

Solution

A unified ingestion pipeline that indexes every connected source into one semantic search layer, while preserving the original system's access permissions.

Architecture

Per-source connectors normalize content into a common index backed by hybrid vector + keyword search. Permission metadata is carried through from each source system so search results never expose what a user couldn't already see directly.

Problem

Teams manually re-key data out of PDFs, scans, and forms into downstream systems — slow, error-prone work that doesn't scale with document volume.

Solution

A layout-aware parsing pipeline that extracts structured data against a configurable schema per document type, exposed through an API built for pipeline integration.

Architecture

Document layout analysis separates text, tables, and figures before extraction, rather than treating a page as an undifferentiated block of text. Extraction schemas are configured per document type, and OCR handles scanned input where no text layer exists.