Most AI projects stall in the gap between "the prototype works" and "we trust it in front of customers." We close that gap by embedding senior engineers into your team and shipping production AI on AWS, with the evals, guardrails, and cost controls that make it safe to scale.
Getting AI to run reliably in production is a different discipline from getting it to work once.
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 that last mile is its own discipline. That discipline is what we do.
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.
Explore →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.
Explore →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.
Explore →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.
Explore →We tune model choice, caching, and serving so the bill is predictable and the experience is fast.
Explore →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.
HIPAA-aware builds and clinical workflows where accuracy and availability 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.
We build where being right most of the time isn't good enough.
HIPAA-aware and AWS-native, brought to procurement early.
Platforms that perform when the stakes and the load are high.
Cloud-native on a foundation your security teams already recognize.
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.
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