AI deployment for systems that have to run in production
Models are easy. Deployment is the work. We ship AI systems that survive contact with the real business - integrated, governed, and instrumented from day one.
Most enterprise AI never gets past a pilot because the deployment work is harder than the model work. Real systems have to live inside identity, data, security, compliance, and operational realities that a notebook demo never touches.
Our forward deployed engineers do that work. We design the system end to end, integrate it with your data and tooling, wire it into your controls, and ship it into production with the instrumentation, runbooks, and evaluation harness the business needs to rely on it.
We deploy AI in regulated industries, in operationally sensitive environments, and on top of legacy systems that are not going anywhere. The goal is always the same: a system the organization can use in day to day work, measure honestly, and extend over time.
How we approach it
Production from the first commit
We build against the real environment - real data, real identity, real failure modes - not a sandbox that will need to be rewritten before launch.
Evals before vibes
Every workflow ships with an evaluation harness so model and prompt changes are measured, not guessed. Regressions get caught before users see them.
Governance built in
Permissions, audit, data retention, content policy, and human review live inside the system. Compliance is not a paper exercise added later.
Operate long enough to prove it
We stay through incident response, model upgrades, and the first real load. Handoff happens when the system is genuinely stable, not at the demo.
What you get
- A production AI deployment integrated with your data, identity, and tooling
- An evaluation harness tied to the workflow level metrics that matter
- Monitoring, logging, and incident response wired into your existing stack
- Governance and audit appropriate to your compliance regime
- A trained internal team that can own the system after handoff
Frequently asked
- What does AI deployment actually mean in an enterprise context?
- AI deployment is the work of turning a model or agent into a system that runs reliably inside the business. That includes data integration, identity and permissions, evaluation, monitoring, governance, incident response, and the workflow design that makes the AI useful to the people who depend on it. A demo is not a deployment.
- How long does an AI deployment take?
- First production deployments typically land in four to ten weeks, scoped to a single high leverage workflow. Hardening, expansion, and follow on workflows continue from there. We deliberately ship early to get real signal, then compound from a running system.
- How do you handle security, compliance, and data residency?
- These are inputs to the design, not blockers added at the end. We work inside the customer's identity, network, data residency, and audit requirements, and we are comfortable in environments with HIPAA, SOC 2, GDPR, and similar regimes. We do not exfiltrate sensitive data to make AI work.
- Which models do you deploy?
- We are model neutral. We choose frontier or specialized models based on workload fit, cost, latency, and the customer's existing relationships. Most deployments combine multiple models behind a workflow layer we own end to end.
- What happens after launch?
- We instrument the system from day one, run continuous evals, and operate it long enough to prove it under real load. Then we hand it off to the internal team with the runbooks, dashboards, and evaluation harness they need to own it.