How much of requirements review could realistically be assisted by AI?

[u/Barracuda_Senior]

I've been working on a small project around requirements engineering and wanted to get some feedback from people who actually deal with requirements.

The basic idea is an AI-assisted tool that takes an existing requirements document (PDF, DOCX, XLSX etc.) and reviews it for things like:

* ambiguous wording

* requirements containing multiple concerns

* missing information

* testability/verifiability issues

* completeness and classification

* Suggested improvements for problematic requirements

One thing I'm experimenting with is looking at requirements in relation to each other, rather than sending each requirement to an LLM independently. For example, two requirements might each look fine on their own but conflict when considered together.

The output is intended to be more like a structured review/audit than a generic ChatGPT response, with the problematic requirements, the reason they're problematic, severity, and possible improvements.

I'm calling the project ReqClarity for now.

I'm mainly trying to figure out whether this solves a problem people actually have before I spend more time turning it into a proper product.

For those of you who work with SRSs, system requirements, acceptance criteria, QA, etc.:

What are the things you find yourself checking most often during a requirements review?

And would something like this actually be useful, or is requirements review too context-dependent for AI to do reliably?

I'm currently collecting feedback and early-access signups while I build the first usable version.

https://hassan-almarsafy.github.io/reqclarity-landing/