Prototype → governed production
Production-grade, governed, multi-cloud agentic AI for regulated enterprises.
Installed like you mean it.
I take enterprise AI agents to production, and I own the judgment about what they should be allowed to do once they get there. The controls that make a company’s intent enforceable, and the decisions about what that intent is.
How I think
Installing AI is not a software rollout.
It is an alignment problem inside the company. Most failures are not model failures, they are organizations that never decided what the AI was for. I do that deciding, then build the controls that hold the company to it.
The approach →The overlap
The market splits this work in two. I don’t.
Ship the controls, with no opinion on what they should enforce. The gateway goes in; the question of what it should refuse is left to whoever happens to be in the room.
Hold the opinions, but cannot ship. The deck names the risk; no guardrail, eval, or audit trail ever encodes it into production.
I do both: own the judgment about what the system should enforce, and build the governance that enforces it, in regulated production.
For the CEO and CTO
If every AI sales call leaves you more confused, that is the problem.
You do not need more AI vocabulary. You need four answers in plain language, each mapped to a control you can audit:
What is actually real, and what is just this quarter's relabeling.
What it costs, attributed per team, before the bill surprises you.
What the system must refuse, written down, then enforced, not assumed.
What to do first, sequenced, not bought because a vendor was loudest.
The governance layer
Six pillars, one production pipeline.
- 01
AI Gateway architecture & build
One governed entry point for all LLM traffic, auth, cost attribution, caching, multi-cloud routing.
- 02
Guardrails, content safety & PII
Anonymize-vs-block per entity, custom regex, injection detection, separate input/output screening.
- 03
Agent observability
Framework-agnostic OpenTelemetry, tracing, cost, and latency for any team, for free.
- 04
Agent evaluation
Capability + regression suites, code/model/human graders, annotation queues wired into CI.
- 05
Secure agent CI/CD & enablement
Enforce gateway use, required cost headers, mandatory OTel; GitOps agent deployment on Kubernetes.
- 06
Securing coding assistants
Govern Claude Code & Copilot in regulated orgs, managed policy, layered context, DLP.
Outcomes, anonymized
Representative outcomes
Illustrative figures pending real engagement data, fully anonymized.
Why this is different
Practitioner work, not slideware.
- The overlap itself
- I ship the production governance and own the judgment about what it should enforce. Competitors do one or the other.
- Production-proven
- These patterns are shipped in regulated production, not designed in theory.
- Genuinely multi-cloud
- AWS Bedrock AgentCore, Azure AI Foundry, and GCP Vertex AI, federated, not siloed.
- Constraint-first design
- I start from your regulatory, residency, cloud, and cost constraints and design the best fit, not one size.