A pilot proves a model can do a task. Production proves it can do it every day, for every user, within your security model, at a cost you planned, with someone accountable when it gets an answer wrong.
We take AI use cases into production and run them: integration, permissions, evaluation, monitoring, cost control and support.
Every engagement ends with a supported service, not a demo.
Test sets and regression checks before every release.
Cost per transaction tracked and reported.
Your data is not used to train public models, and access follows your permissions.
The model is usually the smallest part of a production AI service. These six parts decide whether it lasts.
Application, prompts, retrieval, tools and user interface built to production standards, under source control with automated deployment.
Built to be maintained by people who did not write it.
Software, not a notebook.
Connected to identity, ERP, CRM, document stores and service tools through supported APIs.
The AI works inside existing workflows.
Adoption that does not depend on a new habit.
Permission-aware retrieval, input and output filtering, prompt injection defences, logging and data loss controls.
Tested against misuse, not only intended use.
An AI that cannot be talked out of its limits.
Test sets drawn from real cases, automated scoring and human review, run before each release and on a schedule.
Every change is measured against the last version.
Accuracy you can report.
Model and prompt versioning, deployment pipelines, monitoring for drift, quality and latency, and controlled model upgrades.
Model providers change their models. Your service should not change by surprise.
Releases you control.
Managed support, user feedback loops, cost monitoring and a monthly improvement cycle.
Usage and cost reported like any other service.
A service that gets better with use.
How a use case reaches production
What a production AI service carries that a pilot usually does not.
| Concern | Control | Evidence | Cadence |
|---|---|---|---|
| Accuracy | Evaluation sets, human review | Scores per release | Every release |
| Security | Permission-aware retrieval, guardrails | Security test results | Every release |
| Reliability | Monitoring, fallbacks, alerting | Availability and latency | Continuous |
| Cost | Usage limits, caching, model choice | Cost per transaction | Monthly |
| Change | Versioned prompts and models | Release records | Every change |
Implementation estimates usually count the model and the interface. The integration, permissions, evaluation, monitoring and support are where the real effort sits, and they decide whether the use case survives its first year.
We estimate and deliver all of it, and say up front when a pilot needs rebuilding rather than extending.
Take a pilot to productionScope follows a production readiness review.
Yes. We start with a readiness review and either extend the pilot or rebuild parts of it, depending on what the review finds.
No AI is always right. We reduce errors with grounding, evaluation and guardrails, measure the error rate, and design the process so a person checks the decisions that matter.
It depends on usage, model and architecture. We estimate cost per transaction before build and report it monthly after go-live.
Updates are tested against your evaluation set before they are adopted. Model versions are pinned so changes are deliberate.
Your team with our documentation and pipelines, or us under a managed service.
Send us what was built. The readiness review tells you what it would take to put it in front of every user.