Your AI works in the demo.
We make it survive production.
Most AI pilots die between the laptop and the cloud. We take agents and LLM features that already work in a demo and ship them to AWS — reliable, secure, and without the runaway bill. A senior, AI-accelerated team moving at a speed traditional shops can't match.
Built on AWS — cloud-native by defaultof enterprise AI-agent projects never reach production. Not because the AI is bad — because getting it deployed, reliable, and affordable is a hard infrastructure problem. That problem is the entire job.
It breaks and nobody knows why
No tracing, no evals, no alerting. The agent fails silently in front of real users — and roughly 1 in 20 production LLM calls already errors out.
The cloud bill is a black box
Inference and idle compute quietly eat your runway. AI workloads now consume close to a fifth of enterprise cloud spend, most of it unoptimized.
It can't take real traffic
Bursty agent workloads break naive autoscaling. The demo that wowed your investors falls over at 50 concurrent users.
The full DevOps layer that makes your AI hold up.
Six things stand between a demo and a system you can trust in front of customers. We build all of them, on AWS, end to end.
Productionize the prototype
From a notebook or n8n flow to a real, reproducible service — proper architecture, environments, and guardrails.
CI/CD & release
Automated build, test, and deploy pipelines with safe rollbacks — so shipping an update is one click, not a risk.
Security & access
Least-privilege IAM, secrets out of code, encryption, network isolation, and a full audit trail.
Observability & reliability
Tracing, metrics, alerting, retries, and autoscaling that survives bursty agent traffic — you see problems before customers do.
Agentic ops & guardrails
Eval harnesses, prompt/version control, fallback logic, and rate-limit handling — so you can prove the agent works and stop silent failures.
Cost control (FinOps)
We audit inference, routing, and idle compute, then cap and right-size it. Usually the first win — the savings help fund the work.
One clean pipeline, from prototype to live.
No black box. Here's the path every engagement follows — and the path your system keeps running on after we're done.
Prototype
Your working demo — the agent or LLM feature, as it is today.
Build & test
Packaged, version-controlled, and run through an automated CI/CD pipeline.
Deploy to AWS
Secure, isolated infrastructure with least-privilege IAM and autoscaling.
Observe & guard
Tracing, alerts, evals, and cost caps watching it around the clock.
Production
Reliable, secure, and cost-controlled — ready for real users.
A low-risk way to find out if we can help.
Production-readiness check
We review your setup and hand you a prioritized report on what's blocking production. No cost, no obligation.
Scoped plan
A fixed-price proposal: exactly what gets fixed, the timeline, and the price. No open-ended hourly surprises.
Build & deploy
We ship it on AWS, with you in the loop at every checkpoint. 50% up front, 50% on delivery.
Keep it healthy
Optional monthly retainer — we watch it, keep it reliable, and keep the bill down so you can build features instead.
The numbers that change after we ship.
Representative targets for a standard deployment engagement; actual results depend on your starting point.
Fixed scopes. No hourly surprises.
Start small and low-risk. Scale only when it's working.
Every project is billed 50% up front, 50% on delivery. Complex, multi-system work is scoped individually.
The research behind the gap we close.
Short, sourced reads on why agentic AI projects stall, what agentic ops actually means, and where the governance risk really sits — for the technical reader who wants the data, not the hype.
Why most AI agents never reach production
79% of enterprises report adopting agents — only 11% run them live. The data behind that gap, and what closes it.
What is agentic ops?
A clear definition — and why it's a distinct discipline from traditional DevOps, not just a rebrand of it.
The governance gap in AI agents
74% see agents as a new attack vector. Only 13% trust their own governance. Here's what closes that gap.