Service 03
AI & automation.
LLM workflows, retrieval and agents with guardrails, evaluation and a clear offline fallback.
AI that is measured, bounded and replaceable — not a prompt welded to the product.
How we approach it.
We build AI features where the output has to be useful, not just impressive in a demo. That means retrieval over your data, deterministic fallbacks, cost limits, tracing, and an honest answer when the model is not sure.
01
Frame
What decision does the feature improve, and what happens when the model is wrong?
02
Ground
Retrieval and structured data first. The model formats and reasons; it does not invent prices.
03
Evaluate
A small, real test set before launch. We track quality, latency and cost per request.
04
Operate
Tracing, limits and a fallback path so a provider outage is not a product outage.
Questions.
Can you add AI without a paid model API?
Sometimes. For narrow tasks — classification, ranking, retrieval and templates — a deterministic or on-device approach is cheaper and easier to trust. We will tell you when an LLM is not the answer.
How do you stop hallucinations?
Constrain the task, ground answers in retrieved data, validate outputs against a schema and keep a deterministic path for facts such as prices.
What does it cost to run?
We instrument token use and set hard limits before launch. You get a per-feature cost estimate, not a surprise invoice.
Related work
Seen in practice.
AI & automation
Need this built properly?
Tell us the problem and the deadline. We will reply with the shape of a first release and a straight answer on fit.
Booking new projects for Q1 2027