We take automation from staging to production with no migrations and no rewrites. We deploy on your cloud, with monitoring, guardrails and human review where it matters — and we stay on to keep it running once it's live.
A model that runs in a demo and a process your team uses every day are two different things. We close that gap: we package, deploy, instrument and hand over automation that's ready to operate at your real business volume.
We deploy on your existing infrastructure — AWS, GCP, Azure or your on-prem servers — without pulling you out of your ecosystem. Containers, CI/CD and infrastructure as code so every release is repeatable and reversible. Your data never leaves the perimeter you already control.
We instrument logs, metrics and traces end to end: latency, throughput, cost per run, error rate and model output quality. Dashboards and alerts so you can see how the process behaves in real time — not only when something breaks.
Business rules, input/output validation, spend limits and anti-hallucination controls that stop the model before it makes a wrong call. Every output passes automated checks before it touches your systems or reaches a customer.
At critical points, a person approves before anything executes. Review queues, confidence thresholds and automatic escalation when the model isn't sure. You start with heavy oversight and we release the automation as production confidence earns it.
We map your cloud, your integrations, your permissions and your security requirements. We define what gets deployed, where, and against which SLAs. The goal is to deploy on what you already have — no forced platform migration, no vendor switch.
We containerize the solution, build the CI/CD pipeline with infrastructure as code, and configure secrets, environment variables and rollbacks. Every deployment is versioned and can be rolled back in minutes if something doesn't go as planned.
We go live in a controlled way: first a canary or a slice of traffic, with guardrails and humans-in-the-loop at their highest. We watch real metrics against your data and tune before opening the flow to 100%.
Once stable, we document everything, train your team and define the support plan. You're left with a maintainable process: you know how to monitor it, how to scale it, and who to call if an incident shows up.
No. We deploy on your current stack — the same cloud, the same databases and the same integrations you already use. We adapt to your ecosystem; you don't adapt to ours. If a migration adds value down the road, we'll raise it as a separate decision, never as a condition to get started.
Three layers: guardrails that validate every input and output against your business rules, humans-in-the-loop that approve at critical points, and quality monitoring that catches model drift. You start with heavy oversight and we release the automation only when real metrics back it up.
Every release is versioned and rolls back in minutes. We roll out gradually (canary or by traffic percentage) precisely to contain the blast radius of any problem. And our post-deploy support plan defines who responds and how fast when an incident hits.
As much as you need. We define a post-deploy support plan with continuous monitoring, agreed response times and iterative improvement. We also document everything and train your team so the process is maintainable in-house — not dependent on us forever.
In a call under 30 minutes we review your stack, your integrations and your security requirements, and tell you straight what can be in production in under 30 days — with no migrations.
