Applied LLM systems: RAG over your data, agentic workflows, evaluation harnesses, prompt-engineering at scale.
What this covers
A language model demo is easy and a language model system is not. The gap between the two is almost never the model. It is the retrieval that returns the wrong three paragraphs, the prompt that was edited nine times with no record of which edit helped, the agent that had permission to do something it should never have been able to do, and the absence of any test that would have caught the regression before a user did.
Applied LLM work is therefore mostly ordinary engineering pointed at an unusual component. The model is non-deterministic, its failures are fluent and confident rather than loud, and it will happily produce a plausible answer from irrelevant context. Everything around it — how documents are chunked and retrieved, what the model is allowed to call, how its output is checked, how a change is measured — is what decides whether the system is trustworthy.
That is where the work goes: grounding the model in your data so answers can be traced to a source, giving agents a bounded set of tools rather than open-ended reach, and building an evaluation harness early enough that it shapes the system instead of auditing it after the fact.
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The shape of it
How we approach it
Establish what a correct answer actually looks like, and who decides. Systems fail here more often than in the model.
Get the right context in front of the model: how sources are chunked, retrieved, ranked, and cited back to the reader.
Build the harness of real cases and expected outcomes, so every later change can be measured rather than argued about.
Bound the tools, handle the failure modes, add the logging and guardrails, and make the whole thing observable in production.
Principles
Most answers a model gets wrong are answers it was never shown. Fixing what reaches the context window is cheaper, faster to iterate on, and easier to explain than changing the model’s weights — and it keeps the source of an answer inspectable.
Without one, every prompt change is a matter of opinion and every regression is discovered by a user. A harness of real cases with known-good answers turns "this feels better" into a number that can be compared across two builds.
An agent is a program that decides what to call next. Scope its tools to what the task genuinely needs, make destructive actions explicit rather than incidental, and log every call — so that when it does something surprising, the trace explains why.
They are reviewed, versioned, and changed for stated reasons. A prompt edited live in a console is an undocumented production change; the fact that it is written in English does not make it configuration.
Is this you?
A short walkthrough for AI Development — an engineer explaining the approach, or a screen recording of the system being discussed.
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We'll tell you straight whether this is the right thing to spend on.