From Prototype to Production
We take a working concept and harden it: real data pipelines, error handling, fallback behavior, and the integration work that connects it to your existing product.
Most AI projects stall in the gap between "the prototype works" and "we trust it in front of customers." We close that gap. Logiciel embeds senior engineers into your team and ships production AI on AWS, with the evals, guardrails, and cost controls that make it safe to scale.
Your team built a proof of concept and it impressed everyone in the demo. Then it hit reality. The model is right most of the time, but "most of the time" isn't good enough for a regulated workflow. Costs spike in ways nobody predicted. There's no way to tell whether a change made things better or worse. And the path from notebook to production keeps slipping.
This is the most common place enterprise AI dies. Not because the idea was wrong, but because getting AI to run reliably in production is a different discipline from getting it to work once. That discipline is what we do.
We work across the full path to production , and meet you wherever you are on it.
We take a working concept and harden it: real data pipelines, error handling, fallback behavior, and the integration work that connects it to your existing product.
We build retrieval systems on your own documents and data so the model answers from your truth, not its training set, with the grounding and citations your users need to trust it.
We help you decide where an agent earns its keep and where a deterministic workflow is the safer bet, then build the one that fits.
We put measurement around your AI so you can prove a change helped, catch regressions before users do, and keep the model inside the lines your business and your regulators require.
We tune model choice, caching, and serving so the bill is predictable and the experience is fast.
We embed. Our engineers join your workflow as an extension of your team , not a black-box vendor you throw requirements over a wall to.
A first project sized so a buying committee can de-risk it , clear scope, clear owner, clear exit.
Working software against your data in weeks. you judge us on shipped output, not slideware.
Scale the team and the surface area once the value is proven , on your terms.
We do most of our work where the cost of getting AI wrong is high.
HIPAA-aware builds and clinical workflows where accuracy and auditability aren’t optional.
Modernizing legacy systems and putting AI to work on grid and market data.
Pricing, valuation, and operations use cases on platforms that perform at scale.
As an AWS Partner, we build cloud-native on a foundation your security and procurement teams already recognize.
40%
Lower model serving cost after tuning, caching & routing
12 wks
To a HIPAA-ready assistant shipped into a clinical workflow
50+
Clients across North America 12+ years of production software
Most first engagements show working output in weeks, not quarters. The exact timeline depends on data readiness and integration surface, which we scope in the consult.
We work with it. Our model is to embed senior engineers as an extension of your team and leave you with capability and code you own, not a dependency.
Yes. We build HIPAA-aware in healthcare and AWS-native across the board, and we bring our security posture to procurement early so we don’t stall your timeline.
It depends on scope. We’ll give you a clear range in the consult and structure the first engagement to prove value before you commit further.
Or an idea that needs a team who has shipped this before? Let’s talk through your use case, your constraints, and what it would take to get to production.