A fact-grounded knowledge graph that covers code and business information together
It keeps only source-grounded facts: no dead code and no unshipped plans.
Use it as a single source of truth when assigning work to AI agents.
Keep AI agents aligned with your live code and product context.
Are you explaining your work context again every time you ask AI to help?
Are you building a private knowledge graph that is too complex to share with your team?
Try Platty.
One click creates a work knowledge graph that keeps evolving with your code.
Talk to AI naturally with language or code without restating context
Ask questions like a teammate would, and Platty helps AI understand the product context behind them.
It understands your codebase, conventions, and legacy patterns so agents can write code that fits.
Make the AI agents you already use more accurate and efficient
Give AI agents the company context they need to understand day-to-day work.
Give AI agents the engineering context they need to respect code structure, conventions, and constraints.
Automate planning, technical design, coding, and QA workflows with stronger agent context.
Start a work knowledge graph for free, and let it update as your work changes
No ontology work is required. In a few hours, Platty can build a code graph that understands the current service.
When code changes, the graph changes too. Facts validated through AI conversations accumulate on top of the graph.
Handoffs are easier because Platty preserves company knowledge as the product evolves.
How it is built
Analyze, structure, and hand off in one flow.
A code graph that links the logical structure of every repository through static analysis
It has a much lower error surface than LLM-generated RAG.
Because it uses only live code, it reflects the current service state.
Analysis runs locally, so source code does not need to leave your environment.
The code graph is reorganized into business and engineering views
The business view captures user experience, service logic, and product terminology.
The engineering view links screens to APIs, features, and databases.
The result is a graph that connects product, business, and engineering context.
Natural-language sources and data are validated against the code graph
If a Notion or Slack claim does not match the code graph, Platty treats it as unverified.
Verified facts accumulate in storage connected to the code graph.
Are you a founder, product lead, or engineering leader improving how the whole team works?
Contact us to discuss team adoption.