Generative AI & LLM Systems
Retrieval, agents and assistants grounded in your own content.
The approach
The hard part of an LLM product is rarely the model. It's retrieval quality, evaluation and cost control. We build systems that cite their sources, degrade gracefully, and stay affordable at volume.
What you get out of it
- Answers grounded in your documents, with citations
- Evaluation harness so changes are measurable
- Token and latency budgets you can actually hold
Inference pipeline
What you receive
- RAG architecture
- Vector store & ingestion pipeline
- Prompt & eval framework
- Guardrails and cost controls
Typical stack
Indicative, not fixed. We pick for what your team can maintain after handover.
Every two weeks, something you can open.
Six phases. Each one ends in working software, in an environment you can log into and judge for yourself. Never a status report as the only evidence.
Discover
We map the problem, the constraints and the people. You leave with an architecture direction, a scope and an estimate you can hold us to.
Design
Flows, interfaces and data models get settled while they are still cheap to change. Prototypes meet real users before anyone writes code.
Build
Two-week increments, each ending in a demo. Working software in an environment you can log into. Not a status report.
Harden
Load testing, security review, accessibility pass, and the failure cases nobody enjoys writing. This is the step most projects skip.
Launch
Staged rollout, monitoring live, rollback tested in advance. Someone is watching the graphs on the day.
Evolve
Support under an agreed SLA, and a next round driven by what usage data actually shows.
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Need generative ai & llm systems?
Send over the problem and any constraints you already know about. We will come back with an approach, a rough shape of the work and an honest view on feasibility.