Swap any model. Pass any review. Never get held hostage.
A routing layer sits between your app and the providers, so a better or cheaper model is a config change, not a rewrite. Your data stays inside the boundary you set, and the compliance layer is part of the design - not a scramble the week before an audit. The architecture becomes an asset that holds value instead of a trap you pay to escape.
The decision you cannot un-make cheaply later.
The architect role is the shape of the whole system: where data lives, which models touch it, how you swap a provider without a rewrite, and how you satisfy the regulator who will eventually ask. Get it right once and everything built on top is cheaper.
- Vendor-agnostic routing. A layer that lets you switch or mix model providers without rewriting your app - so price and quality stay in your control.
- The data model. Where data sits, what crosses a boundary, and what never leaves your environment.
- Compliance, designed in. The privacy and audit requirements built into the architecture, not bolted on after a failed review.
- Build-vs-buy calls. Honest guidance on what to build, what to buy, and what to skip - so you do not pay to reinvent a commodity.
The same durable foundation, across very different mandates.
Any organization committing real money to AI hits the same wall: the first architecture painted them into a corner, and the cost to escape is a six-figure rebuild. A vendor-agnostic, compliance-first foundation is how that money stays an investment instead of a trap.
The system an auditor will read
A bank cannot ship a black box. The architecture puts the compliance boundary, data residency, and audit trail in the foundation, so a regulator's review finds a documented system instead of a scramble.
The data that can't leave the building
Health systems and agencies hold data that legally cannot go to a public API. A design with a self-hosted or local-model path keeps sensitive records inside the boundary while still using AI on top of them.
The provider that doubled its price
A company that wired itself to one model provider has no leverage when pricing changes. A routing layer turns "rewrite the app" into "change a config" - and lets cheaper models carry the easy traffic.
Two design decisions that were impractical a year ago and are now table stakes.
The hard part of an AI system is not the model - it is the architecture around it: how it connects to your tools and where your data is allowed to go. Two shifts changed what the right answer is, and most builds for small teams are still using the old one.
MCP-first, not hand-wired
The Model Context Protocol (Anthropic, Nov 2024; OpenAI Mar 2025; Google Apr 2025) made tool connections a standard part. Design on it and you can swap systems without rebuilding the core.
Local models for the sensitive path
Open models now run well enough on your own hardware that the private, regulated, or confidential steps never have to leave your walls - a real choice today, not a research demo.
Decide the boundary up front
Which steps go to a frontier API, which stay local, where the standard port sits. Getting that boundary right is the whole design - and it is far cheaper to set before the build than after.
I design MCP-first and route sensitive steps to local models by default - an architecture most consultants serving small teams are not offering yet. Related: what MCP changes for small operators →
Fixed scope. Async. One payment after the audit.
- Scope and audit. You share your constraints - data sensitivity, regulatory regime, existing stack. I return a fixed price and an architecture outline within 24 hours, or a straight no.
- Design the system. Routing, data model, compliance boundary, and the build-vs-buy decisions, written down so your team and stakeholders can review them.
- Prove the critical path. A working slice through the riskiest part - usually the compliance boundary or the vendor switch - so the design is validated, not theoretical.
- Hand off. The architecture document, the working slice, and a runbook your team builds the rest on.
I delivered a multi-vendor AI agent under RBI and DPDP compliance rules in 3 days (Apollo Finvest), with vendor-agnostic routing and a self-hosted option. See the builds →
A vendor-agnostic layer that lets you move even 30% of traffic to a cheaper or local model, or avoid a single six-figure compliance-driven rebuild, returns the cost of the architecture many times over.
Tell me the constraints
Send me your data sensitivity, your regulatory regime, and the AI system you are about to commit to. Within 24 hours you get a free written teardown of it - what I would build, what it would take, and a fixed price - or a straight no.
Get my free teardown →