This article was written by Pedro Vilhena, Senior Product Manager at SPREAD AI. With experience building and scaling software products across B2B SaaS and AI platforms, Pedro blends hands-on delivery with strategic product thinking. He leads the Requirements Manager product and AI initiatives, helping enterprise engineering teams turn past project knowledge into scalable, AI-powered decision support.
Every company handling engineering-to-order tenders shares a version of the same story.
When a new RFQ arrives, only the most experienced engineers can look at it and know whether the requirements are achievable, how to categorize them, and where the risks hide. In the best case, they're available, have enough time, and have seen similar specs before. They remember the project from 2019 that had that exact fault tolerance issue. They recall which supplier solved the thermal problem last time.
In the worst case—and this is more common than anyone admits—every tender gets treated like it's the first one. No historical context. No pattern recognition. Just starting from scratch, again.
This intuition is invaluable. It's also unscalable, undocumented, and walking out the door every time someone retires.
When a new RFQ lands on your desk, the challenge isn't that the requirements are impossible to understand. It's that understanding them well requires context you can't easily access. Which of these specs have we met before? Where did we struggle? What did we learn? Which requirements need immediate attention because we've never built anything like them?
Most companies answer these questions through hallway conversations, tribal knowledge, and the heroic memory of senior staff. The process works—until it doesn't. Until your best engineer retires. Until the tender deadline is too tight for the "let me ask around" approach. Until you realize you've been quoting conservatively on requirements you've mastered dozens of times, while overlooking risks that should have been obvious.
The knowledge exists. It's locked in disconnected systems, archived documents, and people's heads.
When we built Requirements Manager, we started with a simple premise: the best predictor of whether you can meet a requirement is whether you've successfully met a similar one before.
This reframes tender analysis completely. Instead of treating each RFQ as a blank slate, you're asking: What does our history tell us about these requirements? That question, answered systematically across hundreds of past projects, transforms how you work. Requirements that look risky become confident wins when you can point to successful precedents. Gaps become visible before you commit. BID/No-BID decisions shift from gut feel to evidence.
AI makes this possible in ways that weren't feasible five years ago—not by replacing engineering judgment, but by surfacing the right historical context at the moment of decision. McKinsey found 39% time savings in requirements work. BCG reports 20–30% productivity gains when AI handles decomposition and compliance. The companies figuring this out now will set the pace for everyone else.
But here's what most tools get wrong.
Most requirements tools stop at document similarity. They'll tell you "this spec looks like something from Project X." That's useful, but it's incomplete. Because knowing you quoted something similar doesn't tell you whether you delivered it successfully, what problems emerged in production, or what you learned along the way.
The difference between document search and institutional memory is the difference between "we've seen this before" and "we know how this ends."
This is where SPREAD is fundamentally different.
Because SPREAD already manages your product data across the entire lifecycle—from R&D through Production to After Sales—Requirements Manager doesn't just match documents. It connects requirements to what actually happened in reality.
When you see a similar historical requirement, you're not looking at a quote or a proposal. You're looking at a digital twin of the actual product that was built, shipped, and supported. You can trace from "we committed to this tolerance" all the way through to "here's what we learned in production, here's what After Sales dealt with, here's what we'd do differently."
That's not document retrieval. That's organizational learning, made queryable.
This alone transforms how you evaluate new tenders. You immediately know which requirements are proven ground, which are new territory, and which deserve closer scrutiny—with full traceability to real outcomes, not just archived paperwork.
Every organization asks different questions about their requirements. One company needs risk scoring against internal criteria. Another needs automatic categorization against industry standards. A third wants compliance flags or cost indicators based on their specific logic.
Traditional software forces you to wait for vendors to build each feature—or pay for expensive customization.
With Specialized Requirements AI Agents, you define the analysis you need. A question, structured output categories, your business logic. Each agent produces a field that flows through your requirements table and exports. The AI runs your analysis on every requirement, automatically.
The core platform—semantic matching, similarity detection, lifecycle traceability—stays constant. But the intelligence layer becomes yours to configure. Your categories. Your risk criteria. Your way of working. No vendor dependency, no customization projects.
We're not trying to automate engineering judgment. We're trying to make sure that judgment is informed by everything your organization has actually learned—not just documented, but delivered—surfaced at the right moment, analyzed the way you need it.
The companies that figure this out will quote faster, win more strategically, and compound their expertise with every project. The ones that don't will keep relying on memory, heroic individuals, and luck.
Your competitive advantage is already there. It's in every project you've shipped, every problem you've solved, every lesson you've learned.
It's just waiting to be unlocked by SPREAD Engineering Intelligence.