Agents that know who your products are for
AI agents write requirements, test cases, code and documentation in minutes. rygg gives them the knowledge about users and contexts of use that they cannot get from code or tickets, and every suggestion shows where it comes from.
Situation
- Agents write quickly, but they do not know who uses your products, under what conditions and to what end.
- Without that knowledge they fill gaps with plausible assumptions. Reviewing those assumptions takes the time the agents saved.
- Documents and wikis can be searched by agents, but text alone does not tell them what is verified, what is outdated and what contradicts something else.
Questions the graph answers
Every answer shows its source and whether it is evidence or assumption.
- Who uses this function, under what conditions, with which goal?
- Which user requirement does this test case cover?
- Which terms do users use for this task?
- Which use errors are known for this kind of interaction?
- Is this statement verified, and by whom?
- Which human source backs this suggestion?
What rygg provides
- The graph as a knowledge layer for your agents, through an API, for example as an MCP server
- Provenance for every answer: source nodes from ontology, context and evidence
- Approval by people: agent suggestions enter the graph only after a human has verified them
- Defined terms from the ontology, so agents and teams use the same words
- Operation, maintenance and monitoring of agent queries
Who works with it
- Software and systems engineering
- Test and validation
- Technical documentation
- Service and support
- Platform and AI teams
What you can measure
Examples. We agree on metrics we can influence, not on your revenue.
- Share of agent suggestions backed by provenance
- Share of suggestions approved without changes
- Questions agents could not answer, turned into research questions
- Time from agent suggestion to verified result
How it starts
We start with one team and one type of agent task, for example test cases from requirements. The pilot shows how often agents query the graph and how many suggestions pass review.
Package: Pilot · 4 to 8 weeks
The same graph serves
A shared model of what the organization does, for whom and why, from apprentice to board.
Explore the 10 questions 02Human-centered Product DevelopmentActivities and decisions in product development focused on outcomes for users, and through them on economic results.
Explore the 7 questions