Build your own local
intelligence engine.
A completely free, local routing architecture powered by SpaCy, LangGraph & Ollama. Train domain-specific knowledge into local memory, dynamically handle heavy computation, and deploy DLM as your custom backend engine for any chatbot interface. No cloud dependency.
Live Architecture Trace
Select a query type to see how the DLM engine dynamically routes the logic.
1. The Factual Query
User inputs: "What is the definition of FAFSA in college?"
2. NLP & Intent Parsing
SpaCy intercepts the input. It analyzes the semantic vectors, detects zero mathematical operators or numerical entities, and flags the intent as strictly conversational.
3. LangGraph Routing
The router receives the conversational flag. It bypasses the calculation engines entirely and opens a direct node connection to the local memory/inference pipeline.
4. Local LLM Inference
Ollama receives the query. The local language model synthesizes its internal knowledge base to generate a concise, accurate definition of the federal student aid program with zero cloud latency.
5. Human-In-The-Loop (HITL)
In training mode, execution pauses. The implementor verifies that the LLM didn't hallucinate the definition before allowing the system to proceed.
6. SQLite Persistence
The verified factual response and conversational context window are committed to the local SQLite database.
1. The Factual Query
User inputs: "What is the definition of FAFSA in college?"
2. NLP & Intent Parsing
SpaCy intercepts the input. It analyzes the semantic vectors, detects zero mathematical operators or numerical entities, and flags the intent as strictly conversational.
3. LangGraph Routing
The router receives the conversational flag. It bypasses the calculation engines entirely and opens a direct node connection to the local memory/inference pipeline.
4. Local LLM Inference
Ollama receives the query. The local language model synthesizes its internal knowledge base to generate a concise, accurate definition of the federal student aid program with zero cloud latency.
5. Human-In-The-Loop (HITL)
In training mode, execution pauses. The implementor verifies that the LLM didn't hallucinate the definition before allowing the system to proceed.
6. SQLite Persistence
The verified factual response and conversational context window are committed to the local SQLite database.
See it in action.
Watch the DLM handle the conversational pipeline in real-time.
Ready to initialize?
Get the DLM running in 4 simple steps.
Install Local Inference
DLM requires Ollama to run the reasoning and auto-routing engine completely offline with zero latency. Download Ollama at no cost and start a local model first.
Get Ollama