LLMs That Deliver Real Value
ChatGPT, Claude, open-source models—all have a role. We architect LLM applications that are reliable, cost-controlled, and actually solve business problems. Not hype. Results.
What we deliver with LLMs
Use-case triage
We separate tasks a model does well from tasks that need retrieval, rules, or a human, and we say no to the ones that will not survive contact with real users.
Retrieval that actually retrieves
Chunking, embeddings, hybrid search, and reranking tuned against a labelled question set, with citations so answers can be checked.
Evaluation harness
A versioned set of real questions with expected answers, run on every prompt or model change, so quality is measured rather than assumed.
Prompt and configuration control
Prompts in version control with review, model and temperature pinned per use case, and a rollback path when a provider changes behaviour.
Cost and latency routing
Cheap models for classification and extraction, larger models only where reasoning is needed, with caching for repeated questions.
Guardrails and governance
PII handling, prompt-injection defences on retrieved content, output validation, logging for audit, and a clear answer to what data leaves the boundary.
How we build with LLMs
Every engagement starts with a question we can score, not a demo. We collect twenty to fifty real user questions with acceptable answers, and that set becomes the yardstick. Without it, prompt changes are guesswork and nobody can tell whether the new model version made things better or worse.
Retrieval generally matters more than prompt wording. We spend the effort on chunk boundaries, metadata filters, and reranking, then keep the prompt simple. Cost is designed in from the start: most production volume lands on a small model, with escalation only when the cheap path is unsure.
LLMs in production: where it breaks
The same handful of problems show up on almost every engagement. Naming them early is cheaper than discovering them at 2am.
No evaluation set
Without a scored baseline, quality regressions ship silently. A small labelled set plus automated runs makes every change measurable.
Context stuffing instead of retrieval
Pasting whole documents inflates cost and hides the relevant paragraph. Retrieval with citations is cheaper and far more accurate.
Untrusted content as instructions
Retrieved documents can carry injected instructions. We treat retrieved text as data, sanitise it, and constrain what tools the model can call.
When to use LLMs
Support and operations teams where first-line answers are repetitive and documented.
Policy and contract corpora that people search badly and ask about constantly.
Internal copilots over systems where accuracy matters more than fluency.