Filling RFPs sucks: it’s the least favorite part of every Sales Engineer’s (SE) job.
They’re long. Repetitive. Rushed.
But they’re also high stakes. Turning around quickly can unlock a POC with a major enterprise. Half-assing one can kill a deal before it starts.
Back in the 2010s, a few sharp founders saw the opportunity in RFPs: they were painful, but predictable. And predictability is fertile ground for automation.
So they built the first generation of RFP tools: Loopio, RFPIO (later renamed Responsive), and a few others. These tools created a new software category: RFP Automation, turning a manual grind into a streamlined workflow.
For most of the next decade, the category drifted into a sleepy backwater.
Founders moved on. Investors lost interest. Everyone assumed the problem was solved.
Then came ChatGPT.
And for the first time in a decade, innovation returned to this category turning RFP Automation into AI RFP Automation
AI RFP Automation tools use GenAI to complete RFPs.
It’s that simple.
But, let’s unpack that: Legacy RFP Platforms like Loopio & RFPIO relied on curated answer libraries. These libraries were painful to maintain and quick to go stale, often leading to outdated or irrelevant answers.
AI RFP Automation ditches the library model. Instead, it uses GenAI to generate answers from a dynamic knowledge base, pulled from integrations with your source of truth: Docs, Wikis, call transcripts, and more
There are now several tools in this space. They all aim to solve the same problem. But how they solve it differs—and that makes all the difference.
To make sense of the landscape, we sort tools using two key questions:
These questions reveal three distinct types of AI RFP tools:
You can visualize the landscape this way:
The best way to understand Legacy RFP Platforms is to understand who they’re built for.
They’re built for enterprise teams juggling a high volume of complex RFPs, often across clunky procurement portals (ever heard of Ariba?).
Things get so operationally complex that companies hire full-time roles just to manage it: Proposal Managers, RFP Leads, even full RFP teams.
These Proposal Managers aren’t product, security, or legal experts.
They’re more like Air Traffic Controllers, coordinating experts to ship RFPs on time.
Naturally, these Proposal Managers care A LOT about:
Legacy RFP Platforms were built with the needs of this exact ICP in mind—and they’re excellent at these two things.
But that’s also where they stall out.
Vendors in this category have layered on “AI answers.” But they’re stuck in an innovator’s dilemma. Their core customers want answer libraries. And those libraries—shallow, stale, and bloated—are a terrible foundation for GenAI.
The result: an AI experience that often underwhelms.
Best For:
Enterprise sales teams dealing with high volumes of RFPs, especially in regulated industries where a large percentage of questions require legal sign-off.
Key Users:
Pros:
Cons:
With over 1,400 global customers, Loopio is one of the two leading Legacy RFP Providers.
Loopio’s user experience centers on its answer library: a repository of pre-approved answers built and maintained by your team’s subject matter experts. Naturally, there’s a significant onboarding stage; you have to build the library before the tool becomes useful. To ease this cold start, Loopio offers integrations with sales enablement tools like Seismic and Highspot. But it doesn’t yet support ingestion from dynamic knowledge sources like Slack or Gong, where product answers are already being shared daily.
Once your library is in place, Loopio makes the process smooth. You upload your RFP—.docx, .xlsx, or .pdf—and the system auto-detects embedded questions, organizing them into a clean, table-like format.
From there, you can hit the “Magic” button to let Loopio generate an AI answer. But the quality of those answers depends entirely on the depth and freshness of your answer library.
One of Loopio’s standout features is its ability to export the completed RFP in its original format. This is a major time-saver, sparing teams from the tedious copy-paste loop that haunts most RFP workflows.
Loopio also integrates with Google Sheets and Microsoft Excel, but only for importing an RFP from these systems into Loopio. All actual work happens inside Loopio. That means you’ll need to collaborate through Loopio’s own interface—not through native tools like Docs, Sheets, Word, or Excel.
Pricing:
Responsive has over 2,000 customers, including 20% of the Fortune 100. If you are a Fortune 100 company, chances are, you’ll love it.
Like Loopio, Responsive’s experience revolves around its answer library: a repository of pre-approved answers curated by your company’s subject matter experts. And like all library-based platforms, onboarding requires heavy upfront effort to build and organize that library.
To ease the lift, Responsive integrates with sales enablement tools like Seismic and Highspot, as well as cloud storage platforms like Box, OneDrive, and Dropbox.
However, it does not support ingestion from noisier but more dynamic knowledge sources like Slack or Gong, where product context often lives in real time.
Once the library is in place, you upload your RFP (.docx, .xlsx, or .pdf), and Responsive auto-detects embedded questions, organizing them into a table-like format.
From there, an Auto-Respond feature pulls up candidate answers from your library. But unlike a true GenAI experience that generates new responses, Auto-Respond functions more like a search tool: helping you locate existing answers to manually refine and insert.
As with Loopio, most of the RFP workflow happens inside Responsive’s platform.
However, Responsive does offer a bit more flexibility: you can interact with your answer library from within Microsoft Excel or Google Sheets.
In theory, that means you could complete simpler RFPs inside those native tools.
In practice, though, Responsive is still designed to be used primarily within its own interface.
Pricing:
The best way to understand Standalone AI RFP Platforms is to understand who they’re trying to displace: Legacy RFP Platforms.
Their core insight?
Answer libraries are broken. They’re hard to maintain, stale, and a bad fit for fast-moving product teams.
Generative AI offers a better path: skip the library and generate answers on the fly.
But here’s the twist: Standalone AI RFP tools are still going after the same enterprise customers as the Legacy Platforms.
So they adopt a similar philosophy:
“You fill the RFP in our platform, using our collaboration features.”
That means built-in features like question assignment, reminders, status reports, and approvals.
The problem? Legacy tools have been refining collaboration features for nearly a decade. They also:
In short, Standalone AI RFP Platforms are miles ahead on data integrations and AI, but still catching up on collaboration, formatting, and compliance.
As an Enterprise buyer, choosing between Legacy Platforms & Standalone AI Platforms comes down to what you value more.
Best For:
Mid-market and enterprise teams with a moderate RFP volume that don’t require exact, pre-approved responses.
Key Users:
Pros:
Cons:
1up was one of the first companies to move away from answer libraries. Instead of curating a library, you train it on your product by connecting it to your internal knowledge sources (Google Drive, Confluence, etc.)
Once trained, the workflow is simple:
That’s it. No library to build. No stale answers to weed out.
Of course, there’s still an onboarding process: you’ll need to connect your data sources and wait for 1up to ingest your data, but it’s far easier than manually curating a massive answer library like Loopio or RFPIO.
That said, collaboration is a weak spot.
You have to work inside 1up’s own UX, which means learning yet another tool. And unlike Legacy Platforms, 1up doesn’t offer robust collaboration features. That makes change management harder for teams that rely on reminders, approvals, and cross-functional coordination.
Exporting is also basic. Formatting responses into the final RFP often requires manual cleanup.
One bright spot: 1up includes a Slackbot (plus Google Chat and Microsoft Teams bots) for day-to-day Q&A. It lets you query product knowledge from Slack without logging into the main app. Perfect for generating an answer in the flow of work.
Pricing:
1up offers tiered packages based on RFP volume and data usage:
Quilt shares the same core design philosophy as 1up:
So rather than repeat the overlap, let’s focus on what sets Quilt apart.
Quilt has added more functionality for complex question types (e.g., multiple choice or multi-select fields).
You can also use tags to limit which parts of your knowledge base the AI draws from, which is helpful for tailoring answers to specific use cases.
Interestingly, Quilt also retains a lightweight answer library—not for general reuse, but to support verbatim answers that have been pre-approved by legal. It's a smart compromise for teams in regulated industries.
Overall, Quilt shines when it comes to AI answers and knowledge integrations.
But like 1up, it still lags behind Legacy Platforms in export formatting fidelity and complex question detection
Pricing:
Embedded AI Assistants take a fundamentally different approach to filling RFPs than the Legacy Platforms or Standalone AI platforms.
First, they’re built for Sales Engineers at SMB and mid-market companies—not Proposal Managers at enterprises. These users don’t want to maintain answer libraries. So these tools ditch the library model from Legacy Platforms entirely and rely completely on AI to generate answers on the fly.
Second, SEs prefer to collaborate using the tools they already live in—Slack, Google Workspace, Microsoft Office—rather than learning to work inside a standalone RFP tool. That’s why these assistants embed directly into platforms like Google Sheets, Docs, Slack, Word, or Excel, instead of building their own collaboration UI.
The result? No need to import, export, or format files across platforms.
Third, these tools don’t just fill RFPs. They’re tackling a broader set of tactical SE pain points. That means they prioritize knowledge sourcing over verbatim, pre-approved answers—the latter being critical for compliance-heavy enterprise teams, but less relevant in fast-moving mid-market and SMB teams.
In short: they trade depth in collaboration and compliance for speed, simplicity, and first-class AI answers.
That tradeoff works great for their ideal customer. It doesn’t work well for enterprise teams with Proposal Managers.
Best For:
SMB and mid-market sales engineering teams with low to moderate RFP volume
Roles these tools are best for:
Pros:
Cons:
HeySam (aka “Sam”) is a collection of AI agents designed to augment Sales Engineering teams.
It learns your product, positioning, and pipeline by shadowing your best SEs on calls—either by joining meetings or integrating with Gong. This way Sam captures what they call the “tribal knowledgebase”: the stuff that never makes it into docs.
You can train it further by connecting your docs, Slack, CRM, Notion, sales collateral, or demo videos.
Once trained, Sam becomes your AI Sales Engineer that you can use in:
Unlike other vendors, Sam isn’t just for RFPs. It’s built to take the tactical load off SEs—across the entire presales motion.
When it comes to filling RFPs, HeySam has built native integrations for Google Sheets and Docs, not just a Chrome extension. That means Sam can insert answers directly into your working file, no copy-pasting required.
The tradeoff? Sam doesn’t yet support Microsoft Word or Excel since they build custom extensions, one platform at a time.
Pricing:
HeySam publishes pricing on their website:
Tribble is an AI agent for GTM teams. Like others in the Embedded AI Assistants category, RFPs are just one of the things it does, not the core identity.
Its main UX is a Chrome extension that works across Google Docs, Sheets, Word, and Excel.
Tribble also includes lightweight collaboration features, like the ability to tag a teammate for review. It sends the request to Slack, where the reviewer can approve, reject, or suggest edits. This is a neat way to avoid the heavy approval workflows built into Legacy and Standalone AI Platforms.
While Tribble avoids answer libraries, it does offer a middle ground: inserting verbatim responses from a source document. The experience isn’t as seamless as what you’d get from a Legacy Platform, but it’s a useful compromise.
Zooming out: Compared to other embedded agents which show up across more surfaces like live sales calls, documentation, and community, Tribble is more narrowly focused on RFPs via Chrome extension and internal Q&A via Slack.
Pricing
Sifthub is another Embedded AI Assistant that combines a Slackbot and a Chrome extension to help fill RFPs.
Where it differs from Tribble and HeySam is in its attempt to mimic some of the project management workflows you'd expect from a Standalone AI RFP Platform—like question tracking and collaboration—without requiring users to leave the Chrome extension.
They’ve also built a Microsoft Add-In, making it easier for Word and Excel users to work natively with Sifthub.
One standout feature is the BuyerIQ Agent, which can auto-generate proposals, solution briefs, and executive summaries tailored to a specific buyer and sales stage. This shows Sifthub’s broader ambition: to serve the full sales cycle, not just presales.
Pricing
There’s no perfect tool. Just the right one for your team, your volume, and your workflow.
Legacy Platforms shine when compliance and coordination matter most, but rely on brittle libraries, longer time-to-value, and an all-in-one UI that adds to the learning curve.
Standalone AI Platforms ditch the library in favor of GenAI and strong data integrations, but still require you to import / export RFPs from a third-party interface, where final RFPs are often misformatted, leading to manual cleanup.
Embedded AI Assistants skip the import/export dance entirely, delivering AI answers inside the tools your team already uses. They trade process and collaboration depth for speed, simplicity, and flexibility.
Know what matters to your team, and the right fit will usually be obvious.
Generally? No.
But Embedded AI Assistants unlock a new possibility: because they do more than just fill RFPs—powering Slackbots, sales call copilots, even help center bots—many teams layer them alongside a Legacy or Standalone platform. That way, SEs can take the first stab at an RFP, and product knowledge spreads across more surfaces for the whole GTM team.
