AI & Data Engineering
AI grounded in your data — and your compliance.
We design and ship AI features you can trust: retrieval-augmented assistants grounded in your documents, agentic workflows, OCR and document intelligence, and the evaluation that proves it works — with privacy and compliance designed in.
Start a projectWhat we deliver
- ✓RAG assistants and chatbots grounded in your data
- ✓Agentic, tool-using workflows
- ✓Document intelligence (OCR, classification, extraction)
- ✓Vector search and embeddings
- ✓Evaluation harnesses and answer-quality validation
- ✓Data pipelines and ingestion
Who it's for
Teams adding AI that must be accurate, private, and auditable.
Technologies we work with
Our working toolkit across projects. We're not tied to one stack — we choose the right tools for each engagement, including whatever your team already runs.
Languages
- TypeScript
- JavaScript
- Python
- PHP
- SQL
Frontend
- React
- Next.js
- Vue 3
- Vite
- Tailwind CSS
- Sass
- Material UI
- Chakra UI
- IBM Carbon
- Bootstrap
- Framer Motion
Backend & APIs
- Node.js
- Express
- FastAPI
- Django
- OpenEMR (PHP)
- Vercel Functions
- REST
Data & Storage
- PostgreSQL
- MySQL
- MongoDB
- Redis
- Supabase
- SQLite
- AWS S3
AI & Vector Search
- OpenAI
- Anthropic Claude
- RAG
- pgvector
- Pinecone
- Qdrant
- ChromaDB
- sentence-transformers
- Vercel AI Gateway
Healthcare Standards
- FHIR R4
- HL7v2
- CDS Hooks
- OpenEMR
- OpenEHR
- MLLP
Cloud & DevOps
- AWS
- Vercel
- Docker
- GitHub Actions
- Jenkins
- nginx
- Caddy
- HashiCorp Vault
- Tailscale
Automation & Integrations
- n8n
- Make
- Zapier
- Twilio
- Epic
- Salesforce
- BlueFolder
- QuickBooks
- Acuity
- Google Maps
Auth, Payments & Comms
- Clerk
- Azure AD
- Auth0
- Authentik
- JWT
- Stripe
- Brevo
- Resend
- 8x8
- PostHog
Frequently asked questions
How do you keep AI answers trustworthy?+
We ground responses in your own sources with retrieval (RAG) and citations, so answers are traceable to a document rather than invented. Before launch we run evaluation harnesses against a curated question set to measure accuracy.
Can you keep sensitive or patient data private?+
Yes. We use zero-data-retention model routing, sign BAAs with providers where needed, and can self-host vector stores and embeddings so sensitive data never leaves your control. De-identification is an option for PHI.
Which AI models and providers do you use?+
We're model-agnostic — OpenAI, Anthropic Claude, and others through a gateway that lets us route, fall back, and swap providers. We choose based on accuracy, cost, and privacy for your use case.
How do you measure whether the AI is 'good enough' to ship?+
We build an evaluation set of real questions with expected answers, score the system against it, and keep a human-review step for high-stakes outputs. We don't ship on vibes.
Can the assistant work over our own documents or EHR data?+
Yes — that's the point of RAG. We ingest your documents, policies, or records (with access controls), so the assistant answers from your knowledge, not the open internet.
Have a project like this?
Tell us what you're building and we'll show you how we can help.
Start a project