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HeyIris AI, marketed as Iris, is a well-reviewed AI-native platform for RFP responses, security questionnaires, DDQs, and proposals, built specifically for sales engineers, presales teams, and proposal writers. It earns a 4.9/5 rating on G2 across 67 reviews, with users consistently praising response quality, ease of use, and time savings. Limitations emerge around output quality depending on documentation depth, slow processing at times, and gaps in tone control and visual handling.
- Iris targets the same audience as SiftHub — sales engineers, presales teams, and proposal writers at B2B companies — making it a direct comparison for teams evaluating both
- Pricing is not publicly available — Iris operates on a custom quote model with no published tiers or rate card
- Verified limitations: output quality depends heavily on the depth and organization of existing documentation, can feel slow, struggles with nuanced writing and tone control
- SiftHub differentiates on live deal context from CRM and Gong, full presales workflow including deal briefs and solution narratives, and the breadth of connected sources beyond internal documentation
HeyIris AI, marketed as Iris, is a well-reviewed AI-native platform for RFP responses, security questionnaires, DDQs, and proposals, built specifically for sales engineers, presales teams, and proposal writers. It earns a 4.9/5 rating on G2 across 67 reviews, with users consistently praising response quality, ease of use, and time savings. Limitations emerge around output quality depending on documentation depth, slow processing at times, and gaps in tone control and visual handling.
- Iris targets the same audience as SiftHub — sales engineers, presales teams, and proposal writers at B2B companies — making it a direct comparison for teams evaluating both
- Pricing is not publicly available — Iris operates on a custom quote model with no published tiers or rate card
- Verified limitations: output quality depends heavily on the depth and organization of existing documentation, can feel slow, struggles with nuanced writing and tone control
- SiftHub differentiates on live deal context from CRM and Gong, full presales workflow including deal briefs and solution narratives, and the breadth of connected sources beyond internal documentation
HeyIris AI — the platform is marketed simply as Iris — is one of the most highly rated RFP and security questionnaire tools on G2 in 2026. With a 4.9/5 rating across 67 reviews and 95% five-star ratings, it has built a strong reputation among sales engineers, presales teams, and proposal writers at B2B companies.
Unlike buyer-side procurement tools, Iris is built specifically for the vendor side — helping teams respond faster and more accurately to RFPs, DDQs, security questionnaires, and RFIs. This makes it a direct point of comparison for teams evaluating SiftHub, as both platforms serve the same core audience with overlapping capabilities.
This guide covers what Iris actually costs, what it does well based on verified G2 data, where it falls short at scale, and how it compares to SiftHub for teams that need more than fast first drafts.
What is HeyIris AI (Iris)?
Iris describes itself as an AI-native deal desk — a platform that automatically drafts high-quality content for RFPs, security questionnaires, DDQs, proposals, and other buyer-facing documents. The platform is built on the premise that response quality matters as much as response speed, with a particular emphasis on answer provenance — every generated answer is cited back to the source passage it draws from, allowing reviewers to verify accuracy before submission.
Core capabilities based on verified product documentation and G2 reviewer data:
- AI-generated first drafts for RFPs, security questionnaires, DDQs, RFIs, and proposals
- Passage-level citation — every answer is attributed to the specific source document and passage
- Go/no-go analysis on incoming RFPs
- Persona-based drafting tailored to different buyer types
- Automated compliance checks within the response workflow
- SME assignment and question routing
- Multi-format export and portal autofill
- Integrations with Salesforce, Slack, Google Drive, and other connected sources
- Confidence scoring on AI-generated answers
- Version control and audit trails
Iris was founded in 2023 and serves customers in the United States. It has grown rapidly in the RFP automation category, building one of the highest G2 ratings in its space.
HeyIris AI pricing
Iris does not publish pricing publicly. The platform operates on a custom quote model with no published tiers, no self-serve trial, and no rate card. Pricing depends on team size, annual RFP and questionnaire volume, integration requirements, and the scope of deployment.
Before engaging the Iris sales team, buyers should clarify their annual RFP volume, number of users, which integrations are required, and whether they need the full platform or specific modules.
HeyIris AI reviews: What users say
Here’s what the customers of HeyIris have to say:
What reviewers praise
Response quality and accuracy. The most consistently praised aspect of Iris is the quality of AI-generated responses. Reviewers describe outputs that are substantive and relevant rather than generic, which reduces the editing burden compared to platforms that produce drafts requiring heavy rewriting. The passage-level citation model — where every answer traces back to a specific source passage — earns particular praise from compliance and security teams who need to verify answer accuracy before submission.
Time savings on turnaround. Multiple reviewers describe dramatic reductions in RFP turnaround time. One G2 reviewer describes achieving "10x efficiency" on RFP responses. The consensus across reviews is that Iris meaningfully reduces the time from RFP receipt to first draft, particularly for recurring question types that appear across multiple submissions.
Ease of use and onboarding. Reviewers consistently describe Iris as intuitive and easy to adopt, with hands-on onboarding support from the Iris team. This is a meaningful differentiator in a category where platforms often require weeks of setup before delivering value.
Security questionnaire handling. Several reviewers specifically call out Iris's performance on security questionnaires and due diligence documents — a workflow that requires both accuracy and source traceability. The platform's emphasis on provenance makes it particularly suitable for compliance-heavy response workflows.
What reviewers criticize
Output quality depends on documentation depth. The most consistent limitation across G2 reviews is that Iris's output quality is directly tied to the quality and organization of the documentation it draws from. Teams with fragmented, inconsistent, or outdated internal documentation find that Iris surfaces those gaps immediately — responses are only as good as the source material. This is inherent to any retrieval-based AI system, but reviewers note it requires upfront investment in documentation quality before the platform delivers its full value.
Can feel slow at times. Multiple reviewers note that Iris can feel slow during processing, particularly on complex or large documents. For teams under tight RFP deadlines where speed is critical, processing latency is a genuine friction point.
Struggles with nuanced writing and tone control. Reviewers note that Iris's strength is accurate content retrieval and response assembly. Where it is weaker is in tone adaptation — tailoring the voice, style, and persuasive framing of a response to a specific buyer persona or deal context. This requires manual editing that adds time back into the workflow.
Limited handling of visual and graphical elements. Reviewers flag that Iris does not handle visual or graphical elements in RFP responses well — diagrams, infographics, and formatted tables that many enterprise RFPs require are not generated or managed by the platform.
HeyIris AI key features breakdown
AI-generated first drafts
Iris generates initial response drafts by pulling from your connected knowledge sources and applying its language model to produce complete, structured answers. The platform's emphasis on passage-level citation means every draft answer links to the specific source material it draws from — making the review step faster and more confident than platforms that generate responses without traceability.
Go/no-go analysis
Before committing response resources, Iris provides an initial assessment of incoming RFPs — evaluating fit against your product capabilities and surfacing key requirements. This helps teams make faster bid/no-bid decisions without requiring a full manual review of the document.
Persona-based drafting
Iris can tailor response tone and framing based on buyer persona — adjusting language and emphasis for technical evaluators versus business stakeholders versus procurement teams. This reduces the manual persona adaptation that proposal writers typically handle after the first draft is generated.
Compliance checks
The platform includes automated compliance checks within the response workflow — flagging sections that may not fully address mandatory requirements before submission. This reduces the risk of missing requirements that would disqualify a submission.
Passage-level citation
Every AI-generated answer in Iris is attributed to the specific passage in your source documentation it draws from. Reviewers can inspect the source before approving the answer — a feature that earns particular praise from security and compliance teams dealing with questionnaires where answer accuracy carries contractual implications.
Integrations
Iris integrates with Salesforce, Slack, Google Drive, and other connected sources. The platform also offers portal autofill for submitting responses directly into vendor portals without copy-paste workflows.
Where HeyIris AI falls short compared to SiftHub
Iris and SiftHub serve the same core audience — presales, SE, and proposal teams at B2B companies — and overlap significantly on core RFP and security questionnaire functionality. The meaningful differences emerge in three areas.
Live deal context versus internal documentation retrieval.
Iris generates responses by pulling from your internal documentation — your connected knowledge sources, past submissions, and approved content. Responses reflect what is in your documentation.
SiftHub's AI RFP software connects to the same documentation sources — CRM, Gong call recordings, Google Drive, Confluence, SharePoint, Slack, past submissions, and approved Q&A libraries — but also pulls from live deal context: what was actually discussed with this specific buyer on Gong calls, what the CRM opportunity record shows about their priorities, and which proof points are most relevant to this deal stage. For complex enterprise deals where the buyer's specific situation drives the most compelling response, this live context layer produces responses that feel written for this buyer rather than retrieved from a generic library.
Presales workflow beyond response generation.
SiftHub's AI Teammate goes beyond accurate retrieval into deal-specific narrative building — generating executive summaries and solution stories grounded in CRM opportunity data, and Gong call transcripts, producing demo scripts tailored to what the buyer said they care about, and creating follow-up collateral from what was actually discussed on the call rather than assembled from templates. Where Iris retrieves accurately but requires manual tone adaptation, AI Teammate starts from deal context, so the narrative is specific to this buyer before any editing begins.
Buyer-specific tone and personalization.
Where G2 reviewers of Iris flag tone control and nuanced writing as areas requiring manual editing, SiftHub's Personalization feature addresses this directly. Where Iris generates accurate, well-cited responses that can read as retrieved rather than crafted, SiftHub's Personalization tailors tone, language, and content selection to the buyer's specific industry, deal stage, company size, and stated priorities. The same compliance answer reads differently when pitched to a healthcare CISO versus a fintech VP of Engineering — Personalization handles that adaptation automatically rather than leaving it to manual editing after the draft is generated.
Workflow coordination at scale.
SiftHub's project management feature reads the full intake document, surfaces a bid/no-bid recommendation, generates requirements and milestones, and routes unanswered questions to the right SME based on content type, with deadline visibility across every active submission. Expert review is required at each stage. Within the same platform, Q&A pair deduplication, answer expiry rules, and conflict identification across sources are handled before answers reach the submission, so the compliance team reviews clean, consistent responses rather than resolving contradictions after drafting.
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HeyIris AI vs SiftHub: Direct comparison
Choose Iris if: Your team primarily needs AI-assisted RFP and security questionnaire response with strong source citation, you have well-organized internal documentation, and your workflow is focused on draft generation rather than broader presales deal support.
Choose SiftHub if: Your team needs live deal context from CRM and Gong feeding into responses, you need the full presales workflow, including deal briefs and solution narratives, or you need workflow coordination across complex multi-contributor submissions with deadline tracking and Q&A governance.
Conclusion
Iris is one of the strongest RFP and security questionnaire tools in its category — its 4.9/5 G2 rating and consistent reviewer praise for response quality and ease of use reflect genuine product strength. For teams whose primary need is AI-assisted draft generation with strong source citation, it is a credible choice worth evaluating.
Where SiftHub offers a meaningfully different value proposition is the live deal context layer — pulling from Gong and CRM to produce responses grounded in what was discussed with this specific buyer — buyer-specific tone and personalization that removes the manual editing step G2 reviewers flag for Iris, and the broader presales workflow that extends beyond questionnaire response into deal briefs, solution narratives, and sales collateral. For presales and SE teams managing the full deal cycle alongside high RFP volume, that scope difference is significant.
