AI engineering
AI & RAG Development
Grounded LLM applications on your own documents: ingestion pipelines, hybrid retrieval, evaluation and observability, with citations you can trust.
Demos are easy; a RAG system that stays accurate on large, changing, permission-scoped data is not. We build the retrieval, evaluation and operations layers that make it dependable.
What you get
- Document ingestion with chunking, metadata and selective vectorisation
- Hybrid retrieval (BM25 + vectors) with authorisation-aware filters
- Grounded answers with citations and context budgeting
- Evaluation harness, tracing and cost controls
Deliverables
- RAG service (FastAPI) with provider-agnostic LLM access via LiteLLM
- pgvector or managed vector store, embedding workers and job tracking
- Retrieval quality evaluation set and dashboards
- Multi-tenant access control and audit logging
- Chat or search front end when required
Stack we use
Want to learn it instead? Production RAG & LLM Engineering
From requirements to production
- 01Requirements
A discovery call, then a written scope with acceptance criteria and a realistic estimate.
- 02Design
Architecture, data model and API contracts agreed before code is written.
- 03Build
Short iterations with demos; code reviewed, typed and documented as we go.
- 04Test
Unit, integration and end-to-end tests; QA passes against the acceptance criteria.
- 05Deploy
Docker, CI/CD and cloud setup with monitoring, then a production handover.
- 06Support
Production bug fixing, performance tuning and improvements after launch.
Ways to work together
Fixed-scope project
A written scope, milestones and a fixed price. Best for a defined feature, integration, migration or redesign.
Monthly retainer
Reserved engineering hours every month for ongoing development, maintenance and production support.
Hourly & advisory
Architecture reviews, code reviews, pairing sessions and second opinions, billed by the hour.
Recent work
W3Colleges: a full redesign that kept every URL
This site is our own case study: a new design system, article reader, listing pages, search, login, hub pages and a CI/CD pipeline, delivered as a child theme without touching WordPress core, while 400+ articles, their URLs and SEO metadata stayed intact.
Questions
Which LLM providers do you support?
OpenAI, Anthropic, Google, Azure and self-hosted models through Ollama, behind one provider-agnostic layer so you can switch later.
Can this run fully on our infrastructure?
Yes. The reference architecture runs on Docker with PostgreSQL, Kafka and a self-hosted model if data must not leave your network.
How do you measure quality?
With an evaluation set built from your real questions: retrieval hit rate, groundedness and answer quality tracked per release.
Start a project
Tell us what you are building.
Share the goal, the current state and your timeline. You get a written reply with questions, a suggested approach and an estimate — no sales call required.