AI-Native Platform Build

• A provider-agnostic LLM layer — swap model publishers and the access path without rewrites
• Conversation and message history modeled as first-class, token-accounted domain data
• A guardrails pipeline with a tool-safety built-in set — the broader content and PII catalog is on the roadmap
• A real agent-orchestration engine with tool execution and human-in-the-loop approval gates — engine built, we co-build your workflow

How it works

A bespoke operations platform should be able to use AI without being rebuilt around any one vendor's model. We make yours AI-native from the start, on seams that are already shipped and tested.

• Bring any LLM. A provider-agnostic layer lets you swap model publishers and the access path independently, and route for cost or availability — without rewriting your application.
• History as data. Conversation and message history is modeled as first-class, token-accounted domain data, so usage and cost are inspectable, not opaque.
• Guardrails that gate. A guardrails pipeline ships with a tool-safety built-in set; a deterministic guard can halt an agent action and wait for human approval.
• A real agent engine. Multi-step automation runs on a real orchestration engine with tool execution and human-in-the-loop approval gates.

What we say plainly: the LLM seams and message history are shipped; the guardrail catalog beyond the tool-safety built-ins is still being built. The agent engine is built and demonstrable, but a productized end-application is not assembled — so we frame agents as engine built, we co-build your specific workflow, never as a turnkey autonomous-agent product. The guards are deterministic, not machine-learning classifiers.

What you own at the end

The AI layer runs in standard .NET you own, in your environment, with no dependency on a runtime of ours. You can re-point the LLM seam at any provider you choose.

FAQ

Can you run a local or private model? We have a provider-agnostic seam and can build an adapter for a local or private model. There is no shipped local-model product today — it is a buildable adapter, stated honestly.

Does the agent engine scale horizontally? It is single-process and in-memory today — a real engine, not a distributed one. We say so up front.

How does this relate to your AI vertical? The AI vertical builds the AI application; this offering is the AI layer that runs on your platform. One portfolio, two front doors.

• Provider-agnostic LLM seams with live, real-provider integration tests across multiple model publishers
• A production MCP server and client plus a runnable, engine-driven agent demo — the engine is real; the productized workflow is co-built