LLM application development
End-to-end feature work: prompt and context design, streaming interfaces, structured output, retries and timeouts, and the unglamorous state handling that makes a model feel reliable in a product.
LLM, RAG and multi-agent systems built into your product.
AI Engineering
Getting a language model to produce something impressive takes an afternoon. Getting it to be correct, fast, affordable and safe on real user data takes engineering. We build production LLM systems: retrieval pipelines that return citations, agents that call your tools and recover from failure, routing that sends each request to the cheapest model that can handle it. Every system comes with an evaluation harness so you can prove a change made things better instead of hoping it did.
What you get out of it
- An AI feature that holds up against real user inputs, not just curated demo prompts
- Answers grounded in your own data, with citations back to the source document
- Measurable quality: a regression suite that scores every prompt, model or retrieval change
- Controlled spend through model routing, caching and fallbacks that degrade gracefully
Capabilities
What the work involves
The concrete engineering that makes up a ai engineering engagement.
Retrieval over private data
RAG pipelines with deliberate chunking, hybrid keyword and vector search, reranking, and citation-backed answers. Permission filtering applied at retrieval time so users only ever see what they are entitled to.
Multi-agent orchestration
Planner and worker topologies, tool and function calling, shared state, retries and compensation. Explicit termination conditions and step budgets so an agent loop cannot run away.
Evaluation and observability
Golden datasets, LLM-as-judge with human spot checks, faithfulness and retrieval-hit metrics, plus tracing on every span so you can see the exact context a bad answer was generated from.
Model strategy and routing
An honest read on prompting, retrieval, fine-tuning or distillation for your case — then routing across providers with fallback, so one vendor outage or price change does not take your feature down.
Safety and data handling
PII detection and redaction before data leaves your boundary, prompt-injection defences on retrieved and user content, output policy checks, and retention rules aligned to your compliance position.
Deliverables
What lands in your repository
Everything we produce is yours, in your accounts, documented well enough for your own engineers to carry forward.
- Production AI services in your repository, containerised and deployable by your team
- A retrieval pipeline with documented chunking, indexing and refresh strategy
- An evaluation harness with golden datasets, wired into CI with scored thresholds
- Tracing and cost dashboards covering token spend, latency percentiles and failure rates
- A written model strategy: what runs where, what it costs, and the fallback path
- Handover documentation and a working session with your engineers
Technology we reach for
Chosen per project against your constraints and what your team can maintain — never because it is new.
- Python
- TypeScript
- OpenAI
- Anthropic Claude
- Llama
- LangGraph
- LlamaIndex
- pgvector
- Qdrant
- Pinecone
- FastAPI
- Ray
- OpenTelemetry
Questions
AI Engineering, answered directly
Retrieval is the right default when the requirement is factual grounding in data that changes — documentation, records, tickets, catalogues. Fine-tuning helps when you need a consistent format, tone or classification behaviour that prompting cannot hold reliably. They solve different problems and are often combined: retrieval supplies the facts, fine-tuning shapes the response. We prototype the cheaper option first and only fine-tune when evaluation scores show prompting has genuinely plateaued.
We build a golden dataset of real inputs with expected outputs, then score each release on task success, faithfulness to retrieved sources, latency and cost per request. Automated scoring carries the bulk of the load, with human review on a sampled subset. You set the threshold before launch, and the same suite runs in CI so a prompt tweak or model upgrade cannot quietly regress quality.
Access control is enforced at retrieval time, so a user’s query can only match documents they are already permitted to read — never filtered after generation. Sensitive fields are detected and redacted before anything is sent to a model provider. We use zero-retention API configurations where available, keep indexes inside your cloud account, and can run open-weight models in your own infrastructure when the data cannot leave it.
It depends on request volume, context size and how often you need a frontier model. The largest savings usually come from architecture rather than negotiation: route straightforward requests to smaller models, cache stable context, retrieve fewer and better chunks, and avoid regenerating what has not changed. We model cost per request during the prototype phase so the unit economics are known before you commit to a launch.
Next step
Tell us what you are trying to build.
Send us the problem, the constraints and the deadline. We will come back with a technical approach, a shape for the team, and an honest view of what is achievable.