Legal tech engineering capabilities
Confidential files, citations and live request flows, and how AI has to fit them.
AI for legal products only works when it keeps files where they belong and shows the page behind every answer. These are the systems we have worked with and how the team works.
The team
Who builds it.
Product and workflow analysis
Business analysts who check the live platform against your own checklist and write each workflow out step by step before building.
AI engineering
Retrieval with citations, entity extraction, span level writing tools and rule anchored claim checks, evaluated on your own documents.
Platform engineering
Request lifecycles, payment and release flows, role based dashboards and browser side encryption on a live legal marketplace.
Document processing
OCR chosen by side by side tests on real documents, handwriting included, with fast paths for digital files.
Systems
Systems we have worked with.
Built in client products. We scope yours in the Product Review.
| Area | Systems |
|---|---|
| Documents and OCR | TXT, DOCX, PDF, Word, Excel and scanned pages; Surya and PaddleOCR self-hosted; OCR in the browser |
| Retrieval | PostgreSQL with pgvector, LlamaIndex, Amazon S3, OpenWebUI Pipelines |
| Self-hosted models | vLLM on client-owned AWS GPU instances behind Nginx, in Docker Compose, Qwen models |
| Writing tools | Self-hosted LanguageTool, in English and French |
| Payments, sign in and email | Stripe checkout tied to a request ID, Amazon Cognito, JWT and Google sign in, queued emails from MJML templates |
| Cloud and AI | AWS and Azure, Azure OpenAI with primary and fallback routing, SBERT embeddings, Redis, FastAPI and React |
How the team works
One team across product, AI, platform and documents.
A named lead engineer and US-hours overlap, from the first Product Review to production.
Every engagement
- We assess what is built against what is required before we change a live platform.
- We change trust models in stages, starting with a limited pilot.
- Every change goes through your pull request review and our QA.
- We work in your cloud accounts or on your own GPU host, with access you grant and can revoke.
See it on your own platform.
Bring one product, workflow or AI feature. In 30 minutes we tell you which of the three ways to start fits, what it would cost to serve per user and what your customers' counsel will ask.
Calls run in US Central time. No generic AI presentation: we discuss your product, users and production constraints.