The first version of Nimra.ai came from a very ordinary product-management pain: RFP replies are important, expensive, deadline-driven, and often assembled under conditions that do not exactly promote elegant thinking.
The live product promise is intentionally blunt: drop in an RFP, RFI, or grant, get every requirement identified, turn the document into a compliance checklist, and draft the response from your own materials.
Anyone who has worked around enterprise sales knows the rhythm. A prospect sends a spreadsheet or document with hundreds of questions. Some are straightforward. Some require careful positioning. Some need legal, security, product, engineering, customer success, or finance to weigh in. The answers often already exist somewhere, but "somewhere" can mean an old RFP, a sales deck, a security questionnaire, a contract appendix, a Slack thread, a product requirements document, or one person's memory.
The work is not intellectually empty. A good RFP response can clarify product strategy, reveal gaps, and make the difference between being seriously considered or quietly filtered out. But the process is full of repeated retrieval, formatting, and answer-polishing. It is exactly the sort of work that makes smart people feel like they are spending their day doing Ctrl-F with a deadline hanging over them.
That was the opening for Nimra.
The Product School Version
I started Nimra as a Product School project. The useful thing about building it in that context was the forcing function. It could not just be "AI for RFPs" as a hand-wavy pitch. It needed a user, a workflow, a clear value proposition, and enough product shape to explain why someone would come back to it after the novelty wore off.
The early thesis was simple:
RFP teams do not need AI to sound clever. They need AI to find the right prior answer, adapt it to the current question, and keep the response consistent with the company's actual product and positioning.
That changed how I thought about the tool. The core job was not generating prose from nothing. The core job was reading the whole document, extracting the requirements, preserving organizational memory, and reducing response drag.
The first version focused on a few practical questions:
- What is the question really asking?
- Have we answered something like this before?
- What source material should the answer rely on?
- What answer is safe, specific, and reusable?
- Where does a human need to review because the model is guessing?
That last point matters. RFPs are not a good place for confident nonsense. If a response says your product supports something it does not support, you have not saved time. You have created future pain with a friendly autocomplete interface.
Why RFPs Are a Good AI Problem
RFPs sit in a sweet spot for applied AI because the pain is repetitive but not trivial.
They are repetitive because companies get asked the same categories of questions over and over:
- Security and compliance
- Implementation timelines
- Integrations
- Reporting
- Support processes
- Pricing assumptions
- Data handling
- Product roadmap
- References and case studies
They are not trivial because the answer changes depending on context. The same product capability may need to be framed differently for a school district, a fleet operator, a SaaS buyer, or a procurement department. The right answer is not just factually correct. It is audience-aware, scoped to what was asked, and careful about commitments.
This is where I think a lot of AI products go wrong. They sell generation as the value. In practice, the durable value is usually workflow compression.
For Nimra, the magic is not "write me an answer." The magic is:
Here is the question. Here is the likely prior answer. Here is the source material. Here is a draft that preserves our voice. Here is what needs human review.
That is a much more useful product promise.
The Human Still Owns the Answer
One principle I kept coming back to: Nimra should make a subject-matter expert faster, not pretend the subject-matter expert is optional.
RFPs are full of ambiguity. A question that looks technical may actually be commercial. A question about a product capability may be a proxy for implementation risk. A request for a roadmap commitment may be harmless curiosity or a trap that creates contractual exposure.
That means the product should not be a vending machine for final answers. It should be an assistant that gets a good draft in front of the right person quickly.
The best version of the workflow looks something like this:
- Upload or paste the RFP questions.
- Treat the base RFP, amendments, and addenda as one package.
- Extract requirements and build a compliance checklist.
- Match each question to likely answer categories.
- Retrieve prior answers and relevant source material.
- Draft an answer with citations or traceable context.
- Flag anything that looks uncertain, risky, stale, or dependent on human approval.
- Let the user edit, approve, and save the better answer back into the knowledge base.
That final loop is important. Every RFP should make the next RFP easier.
Product Lessons From Building It
The biggest lesson was that "AI product" is not a feature category. It is a design constraint.
With normal software, bad UX is annoying. With AI software, bad UX can produce incorrect confidence. The interface has to communicate what the system knows, what it inferred, and where the user should slow down.
Nimra forced me to think harder about a few product questions:
Trust is earned in small moments. The tool needs to show why it drafted an answer, not just produce a paragraph and ask for applause.
The knowledge base is the product. A better model helps, but the real advantage comes from a clean library of approved answers, product facts, and source documents.
Review states matter. Draft, needs review, approved, stale, and archived are not administrative clutter. They are the workflow.
Speed without control is not enough. The whole point is to respond faster while reducing risk, not to spray plausible text into a procurement document.
Why I Kept Building
I kept working on Nimra because it felt like a real problem hiding in plain sight. And I hate replying to RFPs. Maybe not hate, but am frustrated by them. Surely we can do better in 2026. And I dislike bureaucratic inefficiency, which RFPs can sometimes be an avatar for.
There are many flashy AI demos that look great for five minutes and then get awkward when you try to use them for actual work. The Product School "winners" for our cohort were eye-wateringly pretty to look at as well as highly functional. RFP responses are the opposite. The work is unglamorous, and the pain is concrete. As in falling onto concrete face first at a full run.
That seemed to be a good place to build.
The Product School version gave me the shape of the product. Later, Carnegie Mellon's Agentic AI course gave me a better language for the system architecture and the agentic workflow underneath it.
But the original motivation is still the same: help teams answer complex, repetitive, high-stakes questions with more speed, more consistency, and less institutional memory trapped in random folders.
That is the promise of Nimra.ai.