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How ConCRG Works

ConCRG has two distinct phases: Training (learning your app) and Assist (helping your users).

Phase 1: Training​

During training, ConCRG builds a knowledge graph of your application using up to four parallel sources — each contributing a different angle of knowledge.

Training Flow

Each source produces structured facts about your app that are merged into a single knowledge base. The more sources you run, the richer the graph.

Phase 2: Assist​

In assist mode, ConCRG answers user questions grounded in the knowledge graph it built during training.

Assist Flow

Every response is grounded in your specific app's knowledge — not generic LLM training data. This prevents hallucinations about your product.

The Knowledge Graph​

The knowledge graph is a rich, structured model of your entire application. It captures:

  • Pages — every route, its UI elements, required roles, and navigation relationships
  • Workflows — multi-step sequences users can follow to complete tasks
  • Roles — who can see and do what across the app
  • Relationships — how features, pages, and data models connect to each other

All of this is built automatically during training and kept current as your app evolves.

Key Principles​

The Concierge Principle​

ConCRG is a guest in your application. It never competes with your UI — it appears only when needed, contains its interface in an isolated layer, and is always dismissable. Read more →

Autonomous Learning​

No content authoring required. ConCRG learns your app from its source — the running interface, your frontend code, and your documentation. Knowledge stays current because the source stays current.

Grounded Responses​

All AI responses are grounded in the knowledge graph, not in the LLM's training data. This prevents hallucinations about your specific product.

Adaptive by Design​

The same question gets a different answer depending on who is asking, what page they're on, and what they've done before. Read more →

Next Steps​