Chat with local AI models that auto-pick the right one per task

Routes each question to the best locally-run AI model for the job, whether it's general chat, code, or images, with nothing sent to the cloud.

How the work actually flows

It branches. Exactly one path is taken.

Pattern: Exclusive Choice (4) · Simple Merge (5)

flowchart TD trig>"chat message sent"]:::trig s0["classify request type"]:::svc s1["route to matching local model"]:::svc s2[("track conversation context")]:::store s3["reply with answer"]:::task trig --> s0 s1 --> s2 s2 --> s3 gx{"× what type of request"}:::gate s0 --> gx p00["send to general model"]:::task gx -->|"text question"| p00 p10["send to coding model"]:::task gx -->|"coding request"| p10 p20["send to image model"]:::task gx -->|"image request"| p20 jn{"○ merge before reply"}:::gate p00 --> jn p10 --> jn p20 --> jn jn --> s1 out[/"ai reply from best matched model"/]:::out pay{{"private ai assistance without data leaving business"}}:::pay s3 --> out out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
Starts itA stepAn outside serviceA record or sheetOne path onlyPaths rejoinResultPayoff
Build size
Standard

A mid-size build with several tools working together.

Business functions
AI Agents & Autonomous SystemsAI Chatbots & AssistantsKnowledge Base & RAG
Connects
Ollama

The problem it solves

You want the benefits of AI assistance without sending your company's data to an outside service, but running multiple specialized models locally means someone has to know which one to use for which request. Picking the wrong model wastes time and gives worse answers. Most teams don't have someone who can make that call every time.

Who it fits

Privacy-conscious businesses and teams running their own local AI models who want one simple chat interface.

How it works

  1. Starts when someone sends a message through the chat interface
  2. AI reads the request and figures out what kind of task it is
  3. Routes text questions, coding requests, or image analysis to the right local model automatically
  4. Keeps track of the conversation so follow-up questions stay in context
  5. Replies with the answer, entirely on infrastructure you control
What you get

One chat window for all your local models

You send one message and it routes automatically to the right local AI model, whether you're chatting, coding, or reviewing an image.

What you get

A chat reply generated by the best-suited local AI model for the request, with no data leaving your systems.

What you need

A self-hosted Ollama installation with your chosen local models installed.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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