Before any client engagement, we build and measure the same systems on our own infrastructure. This page shows that methodology and the internal benchmarks behind it — real client case studies will be added here, with permission, as engagements complete.
Professional firms sit on years of documents that only exist as institutional memory. We built the system we now deploy for law and advisory clients: every document ingested, chunked, embedded and made queryable in plain language — with cited sources on every answer, running entirely on one owned machine.
Document pipeline (OCR + structure extraction) → chunking → local embeddings → vector database → retrieval with reranking → open-weight LLM tuned for cited answers → role-based web interface with full audit logging. Every component open, inspectable and running offline.
Evaluation method and dataset description available on request.
Responding to inbound briefs — RFPs, client requests, engagement letters — is expensive senior time spent mostly on assembly: extracting requirements, finding what the firm did last time, drafting to match. We built an agent that does the assembly and leaves the judgment to a human.
Brief received → requirements extracted → firm knowledge base and past responses searched → qualification assessment → cited draft generated in house style → routed through workflow automation into the firm's existing tools for human review and approval. Nothing is sent without a person signing off.
Built and benchmarked on our own inbound briefs before client use.
Why we publish engineering builds, not logos. Client confidentiality is the whole point of what we sell — so we will never decorate this page with a logo wall. Client case studies appear here only with written permission, in the same measured format. Until then, you get the honest version: our architecture, our benchmarks, our methods.
Two weeks, one workflow, and an evaluation report in this same format, measured on your real documents. That report is yours whether or not you continue.