AI products
What to automate, what to keep human, and how to know it works: applied AI tested with evals at every step, from a studio that runs its own AI product.
Lumen, a concept study
Overview
The hard part of an AI feature is rarely the model. It is deciding what should be automated, what should stay human, and how anyone will tell a good answer from a confident wrong one. That decision comes first here, before a prompt is written.
Everything after it is measured. Evals set the quality bar, guardrails hold it, and monitoring in production says what a change actually did to cost and accuracy. We run our own AI product on the same discipline.
What the service covers
Everything this work
includes, spelled out
Use case
discovery
Most AI projects fail at the choosing stage. We find the workflow where a model genuinely earns its keep, and say plainly where it will not.
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- AI assistants and copilots
- Chat interfaces
- Retrieval over your documents
- Document extraction and summaries
- AI feature audits
Model selection
and prompt design
The right model for the job at the right cost, with prompts treated as engineering artifacts: versioned, tested, owned.
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- Model selection and cost tuning
- Prompt and context engineering
- Fine tuning where it earns it
Evals and
guardrails
A test suite written before the feature, run on every change. It is the difference between a demo and a product.
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- Eval suites and quality bars
- Guardrails and moderation
- Classification and routing
Production
monitoring
Quality drift, cost per request and failure modes, watched after launch, because models change under you.
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- Production monitoring
- Cost per request, watched
- Failure modes reported to a human
How it runs
A demo every Friday, one fixed quote, and you own everything you pay for. The process is the same on every project — this is how it runs for AI products.
The honest scoping call
We tell you which of your AI ideas are real, which are expensive, and which a rules engine would do better.
Evals before features
The measure of good gets written first. Every change afterwards has to beat it.
A thin slice in production
One real workflow with real users behind a guardrail, not a lab demo. Learning starts where traffic starts.
Scale what survives
What holds up gets more investment. What does not gets shut down and written up.
FAQ
Asked before
most first calls
Whichever fits the task and budget: the major hosted models and open weight options alike. The eval suite decides, not the logo.
Guardrails, grounding on your data where it belongs, and evals that measure the failure rate instead of hoping. We will also tell you when a workflow should stay human.
The advisory sprint is one to two weeks and tells you if the build is worth it. Builds are quoted fixed, by phase, like everything else we do.
Helpful, not required. Plenty of valuable AI features run on model knowledge plus your workflow context. Discovery sorts this in week one.
Is this the work
you need done?
Tell us where you are, and we’ll tell you honestly if we’re the right team for it.