AI Operations Control Plane

Routes every AI request to a local model or to Claude, and enforces data policy, spend limits and human review around the answer.

Live demo: ai-ops-control-plane.netlify.app. The public site runs in Portfolio Mode: routing, governance and cost logic run for real on every request, while model execution is simulated, with no API keys and no access to any machine (how it differs from a local install).

Not every AI task should go to the most powerful cloud model. Confidential work can stay on a local model at zero API cost; complex reasoning can justify a stronger cloud model; simple work shouldn't pay for intelligence it doesn't need. This project is the layer that makes that call for every request, explains it, and records it.

Engineering decisions worth a look

• Fail closed. If policy requires local execution and the local model is down, the request is blocked, not sent to the cloud. Fallback chains only contain models that already passed governance, and nothing is retried silently.
• Budget Guard reserves the worst case before every paid call. Estimated input plus max_tokens at list price is reserved in the ledger synchronously, so concurrent requests can't race past the budget; provider-reported usage settles it afterwards. When cloud doesn't fit, a capable local model takes the request.
• Free isn't automatically best. Routing weighs privacy, cost, task fit, reasoning and latency, and quality above a task's requirement counts at a reduced rate. A $0.0002, 2-second Haiku call can beat a 15-second local run on public data, while confidential data stays local regardless.
• Honest labels. Every answer is marked LOCAL (real on-device inference), LIVE API (real paid call) or DEMO (simulated). Simulated output is never evaluated, recommended or counted as spend.
• A public demo that can't spend or reach the laptop. One capability check decides what a deployment may do and fails closed on any hosting signal. The public server reads no provider credentials and rejects live-execution requests with 403.

Verified

• 173 automated tests (Vitest): routing and governance logic, providers over mocked HTTP, services on in-memory SQLite, and portfolio-mode guarantees under hostile settings. Tests cannot reach paid APIs.
• Real local inference: 25 runs through the app on Gemma 4 12B and Qwen 3.5 9B (Ollama on an M3 Pro, 18 GB), averaging 18 s at about 17 tokens/s, $0.00 API cost.
• One live Anthropic call: Claude Haiku 5.5 on a public summarization task, 518 input / 227 output tokens as reported by the provider, $0.000165, 2.5 s, one attempt, no fallback. That is the project's entire paid API spend.
• Public deployment checked from the outside: npm run verify:portfolio (35 checks) and browser QA at four screen sizes against the live site.

Stack: Next.js 16 (App Router) · React 19 · TypeScript · Tailwind CSS 4 · SQLite · zod · Vitest · Playwright · Anthropic SDK · Ollama · Netlify

Local AI	Real inference through Ollama on the developer's laptop (Gemma 4 12B and Qwen 3.5 9B on an M3 Pro). Any installed Ollama model is discovered and registered automatically.
Cloud AI	Anthropic Claude Haiku 5.5, Sonnet 5.5 and Opus 5.5 through the official SDK. OpenAI and Gemini adapters are included and activate when configured.
Default mode	DEMO: local models run for real, cloud models are simulated by MockProvider and labeled as such. Paid calls are opt-in.
Public demo	ai-ops-control-plane.netlify.app in Portfolio Mode: same routing and governance, simulated execution, no keys, no access to any machine.

What it does

For every request the control plane:

1. Classifies the task (type, complexity, business risk) with deterministic, explainable rules.
2. Scans for sensitive data (emails, phone numbers, government IDs, payment cards, credentials, health and compensation data, confidentiality markings) and sets the data classification. A declared label can raise the level, never lower it.
3. Applies governance policy to every registered model: which models may receive this data at all.
4. Checks availability: is Ollama up, which model is resident in memory, is the cloud provider configured.
5. Scores every eligible model on privacy, cost, task fit, reasoning and latency, weighted by the chosen priority mode, after a quality threshold that depends on complexity.
6. Guards the budget: reserves the worst-case cost before any paid call and reroutes to a capable local model when cloud doesn't fit.
7. Executes with a pre-approved fallback chain, never retrying the same model.
8. Evaluates the output, holds it for human review when policy requires, and records a full trace.

This is the beginning of the README. Read the rest at the original. Read the full README ↗