Find Deal-Breakers in 2 Minutes,
Not 20 Hours In

Many teams tell us they currently answer every RFP that comes in.

The problem is that spreads your best people thin across bids that were dead before you started.

AutoRFP.ai's Go/No-Go analysis scans uploaded RFP documents against your custom deal-breaker criteria in minutes, identifying compliance gaps, missing certifications, and requirements you cannot meet before you commit 20+ hours of team time.

Teams using Go/No-Go consistently convert more of their bids, because effort goes only to the bids that clear their deal-breaker criteria.

Not every requirement is binary.

AutoRFP.ai's Go/No-Go gives you scored assessments rather than just pass/fail, so you can see where you are fully compliant, partially compliant, or non-compliant.

For requirements in the gray area, the tool provides context on how closely your capabilities align, letting your team make informed judgment calls rather than guessing.

This is particularly valuable in industries like SaaS where some features may be on your roadmap or close enough to satisfy the requirement.

Yes. You define your own deal-breaker questions and qualification criteria.

The AI then scans uploaded RFP documents against those custom parameters, so the assessment reflects your specific business requirements, not generic criteria.

This means you can flag things like geographic restrictions, specific certifications, deployment models, or any other factors that matter to your bid decisions.

The AI scans your uploaded RFP documents against your deal-breaker criteria in about 2 minutes, compared to the hours it takes to manually read 100+ pages hunting for disqualifying requirements.

This means you can make informed bid/no-bid decisions the same day you receive an RFP, rather than discovering deal-breakers 20 hours into the response process.

Yes. AutoRFP.ai maintains ISO 27001:2022 certification and SOC 2 Type II compliance with enterprise-grade encryption.

Your documents remain exclusively within your private instance, and AutoRFP.ai never uses your content to train its models.

After a quick 30-minute demo, your account can be configured within 48 hours.

Our onboarding team helps you define your Go/No-Go criteria and import your documents.

Most teams are running their first analysis within the first week.

You can define screening questions by any category that matters to your business, including Legal, Implementation, Support, Security, and Compliance.

This allows you to check for specific certifications (like SOC 2 or ISO), deployment models (on-premise vs. cloud), and timeline feasibility simultaneously.

If the AI flags a requirement as a potential deal-breaker but the language in the RFP is vague, you can use the 'Chat with Documents' feature to ask clarifying questions like 'Is Section 5 mandatory or preferred?' or 'What's the actual go-live timeline in the implementation section?'.

This provides immediate clarity without needing to manually hunt through hundreds of pages.

Yes, every finding in the Go/No-Go analysis includes a direct source citation and page number from the uploaded RFP document.

This transparency allows your team to verify the AI's assessment quickly and make decisions with high confidence.

Absolutely. The Go/No-Go analysis works across various document types, including RFPs, DDQs, and Security Questionnaires.

It can quickly scan for 'deal-breaker' security requirements, such as specific encryption standards or data residency rules, before your security team spends hours on a detailed response.

AutoRFP.ai provides a confidence score for every qualification result.

High-confidence scores indicate that the AI has found a clear match or conflict with your criteria, while lower scores signal that the requirement may be in a 'gray area' that requires human review.

Discovering deal-breakers late is common because manual screening of 100+ page documents is tedious and error-prone.

Without automated tools, teams often start the response process based on a high-level overview, only to find disqualifying requirements buried deep in the technical or legal sections hours or days later.

The true cost is the massive drain on your most valuable resources, your Subject Matter Experts (SMEs).

Every hour an SME spends on a poor-fit bid is an hour they can't spend on high-probability opportunities, which directly lowers the share of bids you convert and contributes to team burnout.

Rigorous qualification ensures that SMEs only work on bids where the company meets the mandatory requirements and is a strong fit.

This reduces 'bid fatigue,' stops them from 'burning weekends' on dead-end projects, and allows them to focus their expertise on high-value, strategic work that actually moves the needle.

Common hard deal-breakers include missing mandatory certifications, inability to meet strict go-live dates, geographic data residency restrictions, and non-negotiable deployment models (e.g., on-premise requirements for a cloud-only provider).

A 'No-Bid' decision is best justified with objective data.

By using an automated screening tool, you can present a clear compliance gap analysis showing exactly where the RFP requirements conflict with company capabilities, turning a potentially emotional debate into a data-driven business decision.

Traditional spreadsheets rely on manual data entry and 'best guesses' before the document has been fully read.

AI-driven screening, however, scans the *actual* document text against your criteria, identifying specific conflicts that manual scoring often misses.

Teams become proactive by screening every incoming RFP within minutes of receipt.

This 'fail-fast' approach allows you to disqualify poor-fit bids immediately, giving your team more time to strategically prepare for the opportunities you *do* decide to pursue.

Bid fatigue leads to 'copy-paste' responses, missed requirements, and lower-quality submissions across the board.

By qualifying bids effectively and reducing the total volume of work, teams can invest more time in tailoring each response, leading to higher-quality output on the bids that matter.