// Enterprise AI
Enterprise AI Solutions
Enterprise AI solutions have to clear a higher bar than a prototype: security review, data governance, auditability, predictable cost and reliability at scale. We build AI into enterprise workflows with those constraints designed in from the start, so the system passes review, protects sensitive data and behaves predictably under load.
Is this you?
- AI has to pass security, compliance and procurement review before it ships.
- Sensitive or regulated data means you can't send everything to a public API.
- You need predictable cost and reliability across many teams and high volume.
What you get
Governed AI architecture
Clear data flows, access controls and audit trails so security and compliance teams can sign off with confidence.
Data residency and privacy
Self-hosted or private-deployment options where regulated data can't leave your environment, with retention you control.
Reliability at scale
Model tiering, caching, rate limiting and fallbacks that keep cost predictable and behaviour stable under enterprise load.
Integration with your stack
AI wired into your existing identity, data and observability systems, not a parallel shadow stack.
How we work
- 01
Align on constraints
We start from your security, compliance and data requirements, because they shape every later decision.
- 02
Design for review
An architecture built to pass security and procurement review, with data flows and controls documented up front.
- 03
Pilot safely
A bounded pilot behind evals and guardrails proves value before a wide rollout, with humans in the loop where it counts.
- 04
Roll out and govern
Staged rollout with monitoring, cost controls and the governance to keep AI behaving as the footprint grows.
// FAQ
Frequently asked questions
The constraints. Enterprise AI has to satisfy security review, data governance, compliance, auditability, predictable cost and reliability at scale, all designed in from the start rather than retrofitted. The model is often the easy part; the governance and integration are what make it enterprise-grade.
Yes. Where regulated or sensitive data can't leave your environment, we use private deployments or self-hosted models, keep data inside your boundary, and control retention. For some data, that requirement alone decides build-vs-API, and we design for it explicitly.
Model tiering routes easy, high-volume calls to cheap models and reserves frontier models for hard cases; caching, rate limiting and retrieval design cut redundant calls. We model running cost at real volume up front so finance isn't surprised by the invoice.
That's the point. We wire AI into your existing identity, data, security and observability stack rather than building a parallel one, so it inherits your controls and your teams can operate it with the tools they already use.
Need AI that passes security and scales?
We'll design enterprise AI around your compliance, data and reliability constraints, not in spite of them.