AI Systems Architect
Provable AI · deterministic routing · directive/agent receipts
Fast → stable → auditable · local-first workflow
I build AI systems where routing, constraints, and safety are deterministic first — models run last.
Email Kenneth Open Manifold IDE
z = x · collapse(y) · route accordingly — receipts included

Why you can trust what I ship

Lower latency & cost by design

Deterministic tier routing routes “easy” requests to cheaper model tiers that clear a computed threshold. You stop paying frontier prices for trivial calls.

Hard capability gates (before inference)

Truth-table constraints enforce vision/tools/code/local/audited PII support ahead of model calls. Violations are listed as rule-by-rule receipts.

Audit receipts, not vibes

Each decision path exposes the threshold math, surface demand signals, violations, and cost estimates. Reviewers can audit behavior deterministically.

How I think

I treat AI like a control system: identity (what this request is), behavior (what we allow), and state (what happens next). The runtime is the product: routers, gates, tool registries, and agent loops are first-class code.

I aim for a measurable outcome every time: cost, latency, failure-path clarity, and bounded tool access.

Deep dive (dimensions as a moat)

Advanced: Schwarz-D minimal-surface routing boundary

I use a deterministic surface-derived threshold plus manifold demand signals to choose the cheapest eligible tier.

Business impact (plain English): Cuts AI spend and improves response time by routing most requests to smaller tiers automatically.

Proof via visual: (Add GIF/screenshot of a routing receipt showing tier + threshold)

Advanced: Truth-table capability gating (logic gates before inference)

Hard constraints filter tiers that don’t support what the request requires; violations show up explicitly.

Business impact (plain English): Prevents costly retries and “wrong capability” failures; improves reliability.

Advanced: Local-first Manifold IDE with directive/agent receipts

The IDE runs locally, then optionally bridges out. It executes parsed tool_calls deterministically and keeps tool access scoped.

Business impact (plain English): Lets teams build and test AI workflows locally, reducing risk and speeding iteration.

30-second walkthrough (make it real)

  1. Open Manifold IDE.
  2. Click 📁 folder and grant a folder that contains dimensionalprogramming/ai-systems-architect.md.
  3. Click 📥 ingest.
  4. Ask: “Create a refactoring plan using deterministic receipts. Include threshold math and tool-call actions.”

What to expect: AI responses include routing receipts and may execute tool calls (in deterministic + receipt mode).

Optional GIF: (paste a link to your demo GIF here)

If you want this style of AI system…

Email for roles / consulting Try the IDE now