6 Learnings from Building AI Software for Proposals

Building AI software for proposals is not easy.

The Seev team has been in the trenches for a while now, working with proposal teams who live and die by RFP deadlines. Along the way, we’ve learned lessons the hard way. Some features looked good on paper but failed in practice. Some turned into rabbit holes. Others quietly became the backbone of the whole platform.

Here are six of our most important learnings over the last year.

1. Summaries Are an Endless Source of Value

Forget the flashy features. Summaries are where the real magic happens.

Not fluffy one-paragraph recaps, but structured, reliable summaries that become a daily habit. The kind that explains where each point came from with references, expand when you need more detail, and over time start to feel like “home.”

Every time we improve summarization, customers immediately notice the time savings. It outperforms fancy formatting tricks ten to one. If you want AI that proposal teams adopt, start with summaries. They never stop paying dividends.

2. Form Filling Is a Rabbit Hole

We built it. We regretted it.

On paper, automatic form filling sounds like a dream. Point the AI at a government portal and watch the fields populate. It is a compliance nightmare. One wrong entry and you are out. And even if the AI fills the form, someone still must check every box. You spend the same time, but with higher risk.

The result? No time saved, more liability, and customers who trust the system less. Form filling is the definition of a rabbit hole. We have the scar tissue to prove it.

3. Technical Writing Is the Next Frontier

Most AI for proposals today is stuck at the shallow end, rephrasing RFP requirements. That is not enough.

The real frontier is technical writing: going from the client’s problems and needs to a solution-oriented narrative that holds water. Think methodologies, approaches, and solution architectures that evaluators believe in.

This is especially vital in engineering, where proposals are judged on depth and rigor. We are building this now. When it lands, it will not just be about saving time. It will be about raising the ceiling of what AI can contribute to a proposal.

4. Compliance Will Never Be Perfect (And That’s Fine)

Every proposal manager dreams of a perfect compliance matrix. The reality is that it will never exist.

RFPs contain contradictory instructions, unclear wording, and requirements so nested they confuse even experienced humans. No AI can turn that into a flawless checklist. But it does not need to be perfect to deliver massive value.

Our customers have caught compliance gaps with AI that they missed themselves. These mistakes would have cost them hundreds of thousands of dollars. AI is not a silver bullet, but as a second set of eyes, it pays for itself instantly.

5. Chat Interfaces Are Still King

We have tested everything: widgets, custom editors, point-and-click menus. None of them beat chat.

A chat interface is flexible, intuitive, and fits the way people naturally interact with AI. You can layer on helpful tools like context selection, inline references, and task shortcuts. But the foundation is conversation. Proposal work is fluid, and chat remains the only interface that can keep up with that fluidity.

There will always be UX innovations, but chat is still the anchor for AI in proposals.

6. Building the Right UX on Top of Chat Is Harder Than It Looks

Chat may be the anchor, but building a useful UX on top of it is anything but straightforward.

Basic features like “insert generated text here” or “reword this paragraph” are easy. The real challenge is creating an environment where proposal teams can manage AI context effectively, while still getting clear signals, visibility, and freedom to control the process.

We have iterated through at least ten different approaches to this. We experimented with how users select and work with context, how different content types should be embedded, and how navigation through the writing process should flow. Many of those attempts fell short, but each taught us something.

What we have today is the product of all those iterations: a chat experience that is not just conversational, but genuinely useful for complex, collaborative proposal writing. Getting there was hard, but it is what turns chat from a novelty into a serious productivity tool.

Closing

Building AI for proposals is humbling. You learn quickly that some features are traps, others are breakthroughs, and everything must earn the trust of users who cannot afford mistakes.

What we’ve learned is simple: if an AI feature doesn’t earn its keep in the heat of a deadline, it won’t survive. Proposal managers don’t care about demos, they care about getting through the pile of RFPs on their desk. That’s the bar, and it is higher than most people think.

👉 That is the philosophy shaping Seev, and it is why our AI is not a demo gimmick but a system that proposal teams use under pressure.