Understanding diverges
Customer, product, systems, software and testing each interpret the same requirement from their own background. They seem to agree, but the models in their heads differ.
AI requirement model co-builder for engineering teams
Through professional requirement analysis, it automatically generates confirmation questions and visual models, helping customers, product, R&D and testing continuously clarify and form a shared understanding before development.
01 / Problem
Customer, product, systems, software and testing each interpret the same requirement from their own background. They seem to agree, but the models in their heads differ.
The main flow looks clear, but exception paths, boundary conditions, failure handling, state transitions and responsibility boundaries are never defined.
Decisions scatter across meetings, chats and emails, and the final requirement doc and model are never kept in sync.
Gaps and conflicts only emerge during development, integration or testing, causing rework, delays and disputes over responsibility.
02 / Product demo
Every answer is not an ordinary chat — it is filling in an engineering requirement that can be reviewed and confirmed.
When the vehicle enters reverse gear, the system shows the reverse camera. After exiting reverse gear, it restores the previous page.
I identified three main phases: reverse-camera entry, display, and exit recovery.
If the reverse camera fails to start, should the system keep showing the previous page, or show a fault prompt?
Show the previous page and prompt that the reverse camera is unavailable.
03 / Core difference
Drawing starts from “already figured out”; requirement model co-building starts from “not yet fully said”.
04 / How it works
Enter requirement text, Excel lists, or requirement screenshots or images.
Identify features, roles, flows, states, interactions, conditions, exceptions and missing information.
Output confirmation questions together with flow, interaction or state models.
Answer and discuss around the diagram and questions; each clarification updates the same model.
Settle clear descriptions, reviewable diagrams, question status and freezable versions.
05 / Core advantages
Users don't need to figure out all the logic first; the system can analyze from incomplete, scattered input.
Diagrams show known logic, confirmation questions expose unknown info; both drive clarification.
Each answer adds to the current requirement and model, instead of fragmenting into separate chat logs.
Clearly distinguish original-text facts, engineering inferences and pending content, reducing AI auto-completion misdirection.
The analysis logic embeds about 15 years of automotive R&D, systems engineering and cross-team collaboration practice.
Output targets communication, confirmation and review — not just pretty diagrams.
06 / Audience
Systems engineers, requirements engineers, product managers, software engineers, test engineers, systems architects, project managers, customer technical liaisons and R&D managers.
Customer input is incomplete or multiple documents contradict each other
Meetings produce many new requirements that are hard to sync and settle
Product, R&D and testing understand the same feature differently
Complex flows and states are hard to describe in words alone
Before a requirement review, you need to quickly build a shared understanding
Early in a project, you need to identify exception paths and boundary conditions
07 / Developer
Built independently by a product builder with about 15 years of automotive R&D and systems engineering practice, covering software, systems architecture, product, project management, and requirements analysis.
Early invite trial
Enter an invite code to experience the full process from vague requirement to an engineering requirement model.
Enter invite code to startNo invite code? Contact the developer by email to request a trial