Services Cloud and deployment
Keep AI inside the boundary you already defend.
We deploy AI inside the cloud boundary your security and compliance teams already cover, so production does not trigger a fresh vendor risk review.
25+ years in enterprise IT, including Fortune 500 pharma and healthcare data platforms.
No preparation needed. You leave with next steps in writing.
Assess, build, embed
- 25+ yrs enterprise IT
- Fortune 500 regulated-data environments
- Founder-led
Keep AI inside the boundary your security and compliance already cover, so putting it into production does not turn into a fresh vendor risk review: your own AWS, Azure, or GCP account, your VPC, your audit trail.
The problem
Where AI runs decides who carries the cost when it goes wrong.
Hand your data to a vendor's shared environment and you have lost the one thing a regulated business cannot lose: control of where sensitive data lives and who can reach it. You have also bought a recurring bill you did not plan for, in vendor risk reviews, contract negotiation, and questions from customers about a boundary you do not own. Production AI has to run where your security and compliance already hold, not on someone else's server.
What we do
Production-grade, inside the boundary you already fund.
- Deployment lands in your own AWS, Azure, or GCP account, inside your VPC and your existing audit boundary, so nothing new has to be assessed from scratch.
- Releases stop being events: infrastructure as code and CI/CD, including Azure DevOps, so a deployment is repeatable and reviewable rather than a careful evening.
- Your data never leaves your environment, which removes the whole category of questions a customer or examiner would otherwise ask about a third party.
- On-premise or private deployment when your requirements call for it.
In practice
Reference architecture, by cloud, built on what you already run.
- AWS
- Nothing new to procure or assess, because the control layer is made of services your account already provides. Deployed in your account and VPC, using IAM roles for least-privilege access, Secrets Manager for credentials, and CloudTrail and CloudWatch for audit and logging.
- Azure
- The security review is of your own tenant rather than of a third party, which is usually the difference between a full vendor assessment and an internal check. Deployed in your subscription, using Entra ID and managed identities, Key Vault for secrets, and Azure Monitor, with Azure DevOps pipelines for repeatable deploys.
- GCP
- Same shape, with no extra vendor in the path to review or pay. Deployed in your project and VPC, using Cloud IAM, Secret Manager, and Cloud Logging.
- Where the evidence lands
- Evidence that survives a challenge is worth more than evidence that merely exists, and the difference is one configuration flag. Audit artifacts are written to write-once storage inside your own account: S3 Object Lock in compliance mode, or an Azure immutable blob container with a locked time-based retention policy. Compliance mode is the one that matters, because governance mode can be overridden by a privileged user and is therefore weaker evidence. We tell you which one we configured and why.
- Shared-responsibility boundary
- The most expensive gap in a deployment is the one both parties assumed the other covered. Your cloud provider secures the underlying infrastructure. We deploy and configure the AI system inside your account. You keep ownership of the account, the data, and access. We write down where our configuration work ends and your operations begin.
What you get
What we stand up and hand over.
- Your governed agents and their controls deployed in your own AWS, Azure, or GCP account, inside the boundary your security team already covers
- Infrastructure as code and CI/CD, so every deployment is repeatable and reviewable rather than a person following notes
- Secrets brokered through your cloud secret manager, and identity mapped to your provider with least-privilege roles, never embedded in code
- Logging and audit trails that live inside your environment, so producing them later costs a query rather than a vendor request
- A handover runbook, so your team can run it without a retainer, with optional ongoing senior operations if you would rather not
How we deliver it
Senior engineering, not a handoff.
This comes from decades of Fortune 500 cloud migration and integration work on Azure and AWS, which mostly buys you the mistakes we are not going to make on your budget. We stand the system up in your environment, wire it to your stack, and leave you with infrastructure you own, can audit, and can operate without us.
How a governed agent runs
Where this shows up
See the work in context.
Is this you
- Who this is for
- Best for regulated businesses that need AI to run where their security and compliance already hold, without funding a second boundary to defend.
- Who it is not for
- Not for teams comfortable running production AI in a vendor's shared, multi-tenant environment.
Questions
Questions leaders ask.
- Does our data ever leave our environment?
- No. The system and its agents run in your own cloud account, inside your VPC and audit boundary. Access is scoped and logged, the data stays with you, and the question a customer security review asks about third-party data handling has a one-line answer.
- Which cloud do you support?
- AWS, Azure, and GCP, using each provider's native identity, secrets, and logging, so the capability is built from budget you are already committed to rather than a new line item. We also do on-premise or private deployment when your requirements call for it.
- Who owns and runs it after deployment?
- You own the account, the infrastructure as code, and the audit trail, so nothing here becomes a dependency you have to keep paying for. You can operate it with the runbook we hand over, or keep us on for ongoing senior operations if the time is worth more to you than the retainer costs.
If this is on your plate, let's talk.
A twenty minute intro call is the simplest next step: we work out which step fits, and you leave with one specific thing to act on. If you want the senior read on your business rather than a routing conversation, that is the AI Leverage Briefing.
Within one business dayYour scope is settled, in writing.
What you walk away with Prioritized 90-Day Roadmap · Risk Register · Governance and Compliance Gap Assessment · Safe-to-Deploy Read
25+ years in enterprise IT, including Fortune 500 pharma and healthcare data platforms.
If AI is not the right tool for your problem, you will hear that from us.