Architecture · Architecture Philosophy
Enterprise AI governance: trust is an architecture decision.
Enterprise AI is useful only when the source, the reviewer, the decision, and the evidence can be reconstructed. My architecture approach treats governance—explainability, evidence, auditability, and human oversight—as structural requirements, not policies added later.
Model strategy: commercial, open-source, and internally hosted
No single model deployment approach is automatically safer or better. The right choice depends on data sensitivity, governance requirements, performance, cost, latency, operational capability, model quality, auditability, and business risk. A governed architecture makes that choice per workload:
- Model routing directs each task to the appropriate model class—commercial APIs for capability, internally hosted open-source models for sensitive or regulated data, smaller models for cost and latency.
- Data sovereignty and privacy rules are enforced at the routing layer, so regulated data never leaves controlled infrastructure.
- Evaluation and benchmarking continuously measure model quality against real workloads before and after routing decisions.
- Human oversight gates consequential outputs; evidence capture makes every decision reconstructable.
- Vendor independence keeps the architecture model-agnostic so the organization is never captive to one provider's pricing or roadmap.
Working examples
- Rooster Gateway — model routing, privacy, resilience, and cost control across model providers.
- Halo Trainer — model evaluation and benchmarking.
- HaloVault — enterprise knowledge, evidence, and semantic search under access control.
- HaloBridge — governed healthcare decision support where auditability is mandatory.
All are internally developed platforms in active development; no customer adoption claims are made.
Why this matters in regulated environments
This approach comes directly from enterprise experience: SOX and ITGC governance at Teladoc Health and healthcare delivery at Apervita. Regulated organizations don't need less AI—they need AI whose behavior they can explain to an auditor, a regulator, and a board.