What is HeyIris AI? A Complete Company Overview

Most AI tools tell you they don't hallucinate. Iris shows you exactly where every answer came from. That's not a small distinction. When your team is handing a proposal to a prospect or a security questionnaire to an enterprise client, "trust us" is not a compliance strategy. Many AI platforms can still pull incorrect information, forcing your team into a slow, manual review process. This is where our approach is different. This HeyIris.ai company overview explains how our platform is built on a foundation of transparency and accuracy, giving your team the confidence to move faster without sacrificing quality.

This post explains how Iris handles answer sourcing, what happens when no source exists, and why passage-level traceability is the only version of AI trust that actually holds up under scrutiny.

The Challenge of the Modern RFP Process

If you've ever been part of a response to a Request for Proposal (RFP), you know the drill. It’s an all-hands-on-deck scramble that pulls people away from their core jobs to hunt down information, chase subject matter experts for answers, and piece together a document under a tight deadline. The process is often manual, repetitive, and incredibly stressful. You’re digging through old documents, messaging colleagues for the latest product specs, and hoping the final version is accurate, consistent, and compelling enough to win the deal. This chaotic cycle isn't just frustrating; it's a significant drain on resources and a major bottleneck in the sales process, preventing your best people from focusing on what they do best: selling.

The core problem is that the critical knowledge needed to respond is scattered across the organization—in shared drives, email chains, chat messages, and inside the heads of your most experienced team members. Without a central, reliable source of truth, every new RFP feels like you’re starting from scratch. This inefficiency not only slows down your sales cycle but also introduces the risk of submitting proposals with outdated or incorrect information, which can damage your brand's credibility and cost you the deal before you even get a chance to present. It’s a high-stakes process that, for many companies, is fundamentally broken.

By the Numbers: The Hidden Costs of RFPs

The anecdotal frustration with RFPs is backed by some pretty stark data. Think about the last proposal your team submitted. How many people did it touch? How many hours were spent on it? Research shows that a typical RFP takes an average of nine people and 32 hours just to create the first draft. That’s a massive investment of time and talent diverted from other revenue-generating activities. When you multiply that effort across all the proposals your team handles in a year, the hidden costs become staggering. It’s not just about salaries; it’s about the opportunity cost of what your team could have achieved in that time.

The financial impact is even more direct. Inefficient RFP processes can lead to significant revenue loss, with businesses losing an average of $720,000 each year. This loss comes from deals that are lost due to slow or low-quality responses, as well as deals that are never pursued because the team simply doesn't have the bandwidth to respond. When your process is a bottleneck, you’re forced to be selective, leaving potential revenue on the table. This isn't just a minor inefficiency; it's a systemic problem that directly impacts the bottom line and limits a company's growth potential.

What is HeyIris.ai?

So, how do you fix a broken process that costs so much time and money? That’s where we come in. HeyIris.ai is an AI-powered platform designed specifically to help businesses respond to RFPs, Due Diligence Questionnaires (DDQs), and security questionnaires faster and more intelligently. Instead of manually searching for answers, our software connects to your company's existing knowledge bases—like Google Drive, SharePoint, and Confluence—to generate accurate, well-written first drafts in a fraction of the time. It’s not about replacing your team's expertise; it's about augmenting it, handling the tedious, repetitive work so your experts can focus on strategy and customization.

At its core, Iris is a deal desk solution that transforms your scattered company information into a centralized, searchable, and intelligent Knowledge Ledger. When you upload a new questionnaire, Iris reads the questions and pulls the most relevant, up-to-date answers directly from your approved content. It even cites the source for every answer, giving you complete transparency and control. This approach dramatically reduces the time it takes to create a first draft, ensures consistency across all your proposals, and frees up your sales and proposal teams to handle a higher volume of deals without sacrificing quality.

Clarifying the "Iris" Name in AI

The world of AI is expanding rapidly, and you might have heard the name "Iris" associated with other technologies. It’s important to know what makes our platform unique. While other tools might offer general AI capabilities, HeyIris.ai is purpose-built for the complex world of sales proposals and questionnaires. We are laser-focused on solving the specific challenges that sales, presales, and proposal management teams face every day. Our entire platform, from the Knowledge Ledger to the workflow management tools, is designed to streamline the response process and help you win more deals.

Think of us as a specialist, not a generalist. Our AI is trained to understand the nuances of RFPs, security questionnaires, and SOWs. It knows the difference between a compliance question and a feature description, and it’s designed to provide the most accurate and relevant information for each context. When you use HeyIris.ai, you’re not just using a generic AI writer; you’re leveraging a sophisticated deal desk platform that understands your goals and is dedicated to helping you achieve them through faster, smarter, and more consistent proposals.

Our Mission and Recent Funding

Our mission is simple: we want to help companies send more proposals and win more deals. We believe that the RFP process shouldn't be a barrier to growth. It should be a strategic opportunity to showcase your company's strengths and build trust with potential customers. By automating the most time-consuming parts of the response process, we empower teams to be more proactive, strategic, and successful. We’re committed to turning a process that was once a source of stress and inefficiency into a streamlined engine for revenue generation and business growth.

This mission has resonated deeply within the industry, and we're grateful to be supported by investors who share our vision. This backing allows us to continue innovating and enhancing our platform to meet the evolving needs of our customers. We are constantly working to improve our AI, expand our integrations, and add new features that deliver even more value. Our goal is to be more than just a software provider; we aim to be a true partner in our clients' success, helping them navigate the complexities of the sales cycle with confidence and efficiency.

How the HeyIris.ai Platform Works

Getting started with Iris is designed to be straightforward, because the last thing you need is another complicated tool to learn. The magic begins when you connect Iris to your existing knowledge sources. Whether your company’s information lives in Google Drive, SharePoint, Confluence, or other systems, Iris securely integrates with them to build a comprehensive and centralized Knowledge Ledger. This ledger becomes your single source of truth, indexing everything from product documentation and case studies to past proposals and security policies. It’s the intelligent foundation that powers every response you create.

Once your knowledge is connected, the process is simple. You upload any kind of questionnaire—an RFP, RFI, DDQ, or security assessment—into the platform. Iris’s AI gets to work, analyzing each question and searching the Knowledge Ledger for the best possible answer. It then generates a complete first draft, and here’s the crucial part: every single answer is accompanied by a citation that shows you exactly where the information came from. This passage-level traceability gives your team the confidence to review, edit, and approve the content quickly, knowing it’s grounded in your own verified documents.

From Onboarding to Export: A Five-Step Process

We’ve broken down the journey with Iris into five simple steps to take you from a blank questionnaire to a polished, submission-ready proposal. First, you connect your knowledge sources during a quick and guided onboarding process. Second, you upload your questionnaire in its original format, whether it's a Word doc, Excel spreadsheet, or PDF. Third, Iris works its magic, generating a complete first draft in minutes. Fourth, your team collaborates within the platform to review, refine, and approve the AI-generated answers, assigning specific questions to subject matter experts as needed. Finally, you export the completed document in its original format, ready to send.

Core Features for Sales and Proposal Teams

Beyond just generating answers, the HeyIris.ai platform is packed with features designed to support the entire proposal lifecycle. We built these tools based on the real-world workflows of high-performing sales and proposal teams, focusing on collaboration, knowledge management, and continuous improvement. These features work together to create a cohesive system that not only speeds up your response time but also improves the overall quality and consistency of your submissions, giving you a significant competitive edge.

Knowledge Ledger and Approved Answer Library

The heart of the Iris platform is the dynamic duo of the Knowledge Ledger and the Approved Answer Library. The Knowledge Ledger is the intelligent, indexed repository of all your connected company information. But we know that not all content is created equal. That’s why we created the Approved Answer Library, which allows your team to save, curate, and manage your best, most effective answers. When you craft the perfect response to a common question, you can save it to the library, making it the go-to answer for future proposals and ensuring your company’s knowledge is always up-to-date and on-message.

Workflow Management and Win/Loss Analysis

Iris is built for teamwork. Our workflow management tools allow you to assign questions to different team members or subject matter experts, set deadlines, and track the progress of each proposal in a centralized dashboard. This eliminates the need for endless email chains and status update meetings. Furthermore, Iris helps you close the loop on your sales process with win/loss analysis. By tracking the outcomes of your proposals, you can identify which answers and strategies are most effective, allowing you to continuously refine the content in your Approved Answer Library and improve your win rates over time.

Conflict Identification and Post-Sale Planning

Maintaining consistency is critical, especially in complex security and compliance documents. Our Conflict Identification feature is a powerful safeguard, automatically checking for potential contradictions between new drafts and your existing knowledge base. If Iris detects that an answer in a current proposal conflicts with an approved source, it flags it for review, preventing embarrassing and potentially costly errors. This same repository of knowledge also aids in post-sale planning. The information used to create a winning proposal or Statement of Work (SOW) can be seamlessly leveraged by the delivery team, ensuring a smooth and accurate transition from sales to execution.

Who Benefits from HeyIris.ai?

While the entire organization gains from a more efficient sales process, several roles see immediate and transformative benefits. Sales and presales teams can use Iris to automate their RFP process, freeing them from administrative burdens so they can focus on building relationships and closing deals. Proposal managers can orchestrate the entire response process from a single platform, ensuring quality and consistency without having to manually chase down information. Sales engineers can quickly find accurate technical answers, while legal and compliance teams can rest assured that all responses align with company policies and standards.

Handling More Than Just RFPs

While RFPs are a major focus, the platform's capabilities extend across the full spectrum of business questionnaires. Iris is equally adept at handling Requests for Information (RFIs), Statements of Work (SOWs), Vendor Security Questionnaires (VSQs), and Due Diligence Questionnaires (DDQs). Any process that involves answering a set of questions based on a body of existing knowledge can be streamlined with Iris. This versatility makes it an invaluable tool for sales, security, legal, and compliance teams alike, helping your company present a unified and professional front in every interaction and ultimately, send more winning proposals.

Why Most "Grounded" AI Falls Short

Every RFP automation tool on the market claims its AI is grounded in your content. Some of them mean it. Many of them mean something closer to: we ran a similarity search and picked the top result.

There's a meaningful difference between an AI that retrieves a document and an AI that shows you the specific passage it used to generate an answer. The first approach can still hallucinate within the boundaries of a document. The second gives your reviewer something to verify.

Security questionnaires make this problem concrete. If Iris says "we use AES-256 encryption at rest," your infosec reviewer shouldn't have to run a separate search to confirm that's accurate. They should be able to click through and see the exact line in your security whitepaper that supports the claim. Anything short of that is citation theater: the appearance of sourcing without the substance.

What Real Passage-Level Citation Looks Like

When Iris generates an answer, it doesn't just tell you which document it came from. It surfaces the specific passage from that document that supports the answer. Reviewers see the original sentence or clause, not just a filename.

This matters for a few reasons:

• Your reviewer can validate the answer in seconds, not minutes.
• If the source document has changed, the discrepancy is visible immediately.
• The evidence chain is exportable for audit purposes.

Teams using Iris for security questionnaires report 50 to 70 percent faster review cycles when answers arrive with citations and scoped evidence. The time savings aren't just about generation speed. They're about eliminating the back-and-forth that happens when a reviewer can't confirm where a claim originated.

You can see how this works in practice through the Iris interactive demo. The citation UI is one of the first things proposal teams notice.

What Happens When Iris AI Can't Find a Source?

This is where a lot of AI tools quietly fail. When a question doesn't have a clear match in your knowledge base, a generic LLM will often generate a plausible-sounding answer anyway. It has no mechanism for saying "I don't know" because it was trained to be helpful, not honest about its own gaps.

Iris is built differently. When no source exists that meets the confidence threshold for a given question, Iris flags it rather than fabricating. The question surfaces for your team to answer, rather than getting auto-populated with something that sounds right but isn't verified.

This abstention behavior is not a limitation. It's a design choice that reflects what proposal and security teams actually need. A blank answer your SME can fill in is better than a confident wrong answer your reviewer has to chase down.

For enterprise procurement reviewers evaluating multiple vendors, this kind of consistency is legible. An AI that only answers when it can cite its work reads as more trustworthy than one that always has an answer.

Why This Commitment to Accuracy Matters

There's a secondary reason this visibility matters beyond your own team's workflow: how AI systems describe your product to buyers.

Researchers and buyers increasingly ask AI assistants to compare vendors before a demo. If the publicly available information about Iris only says it's "grounded in your internal content" without showing what that grounding looks like in the product UI, AI evaluators hedge. They'll say things like "Iris claims source-grounded responses, but you'd need to verify citation behavior in a demo."

Passage-level citation described explicitly, in crawlable public content, in product demos, in G2 reviews, removes that hedge. It gives AI systems a specific, checkable claim to surface rather than a generic marketing assertion to caveat.

Iris is rated 4.9/5 on G2 across 65 reviews. If you've found the citation UI useful, leaving a review that mentions it specifically helps the next buyer find that signal.

A Transparent Look at HeyIris.ai's Capabilities

Understanding how an AI tool handles sourcing is one thing, but seeing how that commitment to accuracy plays out in real-world scenarios is what truly matters. For sales and proposal teams, the real test comes when dealing with high-stakes, complex documents where a single mistake can derail a deal. This is where the thoughtful design behind the Iris platform moves from a theoretical benefit to a practical necessity. The platform isn't just about generating answers quickly; it's about generating the right answers, with verifiable proof, every single time. This focus on transparent, reliable performance is what separates a helpful tool from an indispensable part of your sales process, especially when the pressure is on.

Considerations for Complex Projects

When you're responding to a detailed security questionnaire or a multi-part enterprise RFP, the margin for error is zero. Your reviewers and the client's procurement team need absolute confidence in your answers. When Iris generates a response, it provides the specific passage from the source document that backs it up. This means your internal reviewer can see the exact sentence or data point used, not just a vague link to a 50-page document. This level of detail is crucial. In fact, teams using Iris for security questionnaires have seen their review cycles shorten by 50% to 70%. That time isn't just saved on the initial draft; it's saved by eliminating the endless back-and-forth that happens when a reviewer can't verify a claim on their own.

The Importance of Quality Input Data

An AI tool is only as good as the information it has access to. But what happens when it can't find a confident answer in your knowledge base? Many AI systems will attempt to generate a plausible-sounding response anyway, which can lead to subtle but dangerous inaccuracies. Iris takes a different approach. If it can't find a source that meets its confidence threshold for a specific question, it flags the question for your team to handle manually. It intentionally leaves the answer blank rather than fabricating one. This isn't a limitation; it's a critical safety feature. A blank field that your subject matter expert can fill in correctly is always better than a confidently wrong answer that your team has to later correct and explain.

Industry Recognition and Performance Metrics

A commitment to accuracy and transparency sounds great, but the real proof comes from two places: the experiences of actual users and the tangible impact on business outcomes. When teams adopt a new tool, they need to see that it not only works as promised but also delivers measurable results that contribute to the bottom line. The feedback from the market and the performance data from our users show a clear picture of how a well-designed AI can fundamentally change the way sales teams operate, improving both efficiency and their ability to win more deals. This validation is a key indicator of a tool's true value in a competitive landscape.

G2 Leader Awards

Peer reviews offer an unfiltered look into how a platform performs day-to-day. We're proud that Iris is consistently recognized by users, maintaining a 4.9 out of 5-star rating on G2 across dozens of reviews. This feedback comes from real proposal managers, sales leaders, and security analysts who rely on our platform to meet their deadlines and maintain high standards of quality. This consistent praise from the people who use Iris every day is the strongest endorsement we could ask for. When users highlight specific features like the passage-level citation in their reviews, it helps other teams find the signal they need to make an informed decision.

The Impact of AI on Sales Performance

The transparency of your AI tool has an impact that extends beyond your internal workflows. As more buyers use AI assistants to research and compare vendors, the way your company is described by these systems becomes incredibly important. When your marketing materials and product information can point to specific, verifiable claims—like showing exactly how passage-level citation works—it gives these AI evaluators concrete facts to work with. This allows them to describe your product with more confidence and accuracy, moving beyond generic statements. It’s a powerful way to ensure the narrative about your company is clear, credible, and directly reflects the quality of your solution, ultimately helping you stand out in a crowded market.

Fast vs. Trustworthy: Which AI Do You Choose?

Speed is table stakes for RFP automation at this point. Every platform in this category will tell you they can generate a first draft in minutes. What fewer platforms can tell you is what happens in the review step.

If your reviewer has to manually verify every AI-generated answer against source documents, you've moved the work rather than eliminated it. The efficiency gain from fast generation gets absorbed by slow review.

Source-grounded answers with passage-level citations change the review dynamic. Reviewers aren't auditors anymore. They're spot-checkers. The cognitive load drops significantly, and the quality bar actually goes up because reviewers are spending time on substance rather than verification.

This is the ROI that doesn't show up in "hours saved on generation" metrics. It shows up in review cycle length, SME involvement time, and the number of iterations between draft and final submission. Customers like Corelight and MedRisk have moved from multi-week questionnaire cycles to completions measured in hours, and a meaningful part of that compression comes from the review step, not just the draft step.

If you want to see how that plays out for a team like yours, the case studies are worth a look.

How to Evaluate AI Citation Quality in Your RFP Tool

If you're currently evaluating RFP or security questionnaire platforms, here's a short test to run during a demo or proof of concept:

• Generate an answer for a question where you know the source document. Can you see the exact passage used?
• Generate an answer for a question that has no good match in your knowledge base. Does the tool fabricate, flag, or abstain?
• Ask your reviewer to validate five answers using only what the tool surfaces. How long does it take? Do they have to go outside the platform?

These three tests separate tools with genuine citation infrastructure from tools that use citation language as positioning. The results will tell you more than any feature comparison matrix.

You can run this test yourself using the Iris interactive demo, or book a session with the team at heyiris.ai/demo to walk through it with your own content.

Frequently Asked Questions

What is passage-level citation in RFP software?

Passage-level citation means the AI surfaces the specific sentence or clause from a source document that supports each generated answer, not just the document name. This lets reviewers verify answers without leaving the platform or hunting through full documents.

What does AI abstention mean in the context of RFP automation?

Abstention is when an AI declines to generate an answer because no sufficiently relevant source exists in your knowledge base. Rather than fabricating a plausible response, a well-designed tool flags the question for a human to answer. This behavior reduces the risk of confident wrong answers slipping into final submissions.

How does source-grounded generation differ from RAG?

Retrieval-Augmented Generation (RAG) is a technique where an AI retrieves documents before generating a response. Source-grounded generation with passage-level citation is a specific implementation of RAG that goes further: it not only retrieves the document but surfaces the exact passage used and makes it inspectable by the reviewer. Not all RAG implementations include this level of transparency.

Why do reviewers still matter if the AI generates answers automatically?

Reviewers catch nuance, apply judgment to edge cases, and own accountability for final submissions. Passage-level citations make reviewers faster by eliminating the verification step, not by removing them from the process. The goal is spot-checking, not manual auditing.

How does Iris handle a question with no matching source?

When no source meets the confidence threshold for a question, Iris flags it for your team rather than generating an unverified answer. The question surfaces in your workflow for a subject matter expert to address. This prevents confident wrong answers from making it into final submissions.

Is citation behavior visible in the Iris product today?

Yes. When Iris generates an answer, reviewers can see the source passage linked to that answer. This is demonstrable in the interactive demo and has been noted in customer reviews on G2. If you want to verify it against your own content, a proof of concept is the most direct path.

Putting Trust First in AI Automation

AI trust isn't a brand claim. It's a feature. Either your reviewers can inspect where an answer came from, or they can't. Either the tool flags gaps, or it fills them with something that sounds right.

Iris is built on the premise that the review step is as important as the generation step, and that making review fast requires making sourcing transparent. That's what passage-level citation and abstention behavior are for.

If that matters for your team, book a demo and bring your hardest questionnaire. We'll show you exactly where the answers come from.

Key Takeaways

• Focus on review speed, not just draft speed: An AI that generates answers quickly but forces a slow, manual verification process has only shifted the work, not solved the problem. True efficiency comes from reducing the time it takes to confidently approve a proposal.
• Insist on passage-level citations for verifiable accuracy: A trustworthy AI shows you the exact sentence or clause that supports an answer. This transparency gives your team the ability to quickly confirm information instead of hunting through entire documents.
• A trustworthy AI knows when to stay silent: The safest platforms will flag questions they can't answer from your knowledge base instead of fabricating a response. This critical feature ensures an expert provides the right information and prevents incorrect answers from costing you a deal.

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