# AI Visibility Software RFP Template Canonical URL: https://trakkr.ai/guides/ai-visibility-software-rfp-template Published: 2026-06-11 Last updated: 2026-06-11 Author: Mack Grenfell Copy an AI visibility software RFP template for evaluating GEO, AEO, LLM monitoring, AI citations, reporting, security, and vendor methodology. ## AI Visibility Software RFP Template Use this RFP template when your team is buying AI visibility, GEO, AEO, LLM brand monitoring, or AI citation tracking software. It is written for procurement teams that need comparable vendor responses and for marketing teams that need evidence, not just a visibility score. Copy the table, add your required prompts and markets, and ask each vendor to respond in the same format. The point is to make every vendor answer the same operational questions: what is monitored, how evidence is stored, which citations or source fields are available, how competitors are compared, how exports work, how security is handled, and what a pilot will prove. A good RFP should make unsupported features visible without punishing a vendor for being honest about them. ## Key Takeaways A strong AI visibility RFP asks for methodology, prompt-level evidence, citations, competitors, exports, reporting, permissions, security, and support. Require vendors to show the raw answer and cited URLs behind any score or executive metric. Ask vendors to state which AI surfaces are supported today, which are beta, and which are not supported. Include a pilot section with acceptance criteria so the RFP does not end at a demo. Keep Trakkr, Profound, Otterly, Semrush-style suites, and internal builds comparable by scoring the same requirements. ## Copyable AI visibility RFP table Paste this into your RFP and add importance ratings, internal owners, and due dates. ## Copy RFP table | RFP section | Vendor question | Required response | | --- | --- | --- | | Platform coverage | Which AI surfaces do you monitor today? | List requested surfaces separately: ChatGPT, Perplexity, Gemini, Claude, AI Overviews, plus buyer-requested surfaces such as Copilot, Google AI Mode, Reddit/community signals, and citations. Mark each as supported, beta, roadmap, or unsupported. | | Prompt methodology | How are prompts created, tagged, refreshed, and quality checked? | Explain prompt sourcing, intent labels, persona or market tagging, duplicate handling, and how customer-supplied prompts are maintained. | | Evidence retention | Can users inspect the exact answer behind every metric? | Show prompt, answer text, timestamp, model or surface, market, cited URLs, competitor mentions, rank logic, and any screenshot or transcript evidence. | | Citation tracking | Do you capture cited domains, cited URLs, citation context, and source ownership? | Describe supported citation providers or surfaces, export fields, lost/new citation logic, and whether competitor citations are tracked. | | Competitor analysis | How are competitors selected, ranked, and compared? | Describe competitor setup, prompt-level side-by-side views, share-of-voice logic, and category or market filters. | | Perception and sentiment | How do you classify sentiment, narrative, claims, and brand descriptors? | Explain methodology, confidence limits, human review options, and how false or outdated claims are handled. | | Reporting | What reports can be shared with executives, clients, and working teams? | Include dashboard examples, PDF/CSV/API options for exportable datasets, scheduled reports, client-safe sharing, and white-label support if relevant. | | Teams and permissions | How do roles, brand access, agency access, and client portals work? | List roles, access levels, invite/revoke flow, brand-level permissions, and any SSO or custom-role support. | | Integrations and exports | How can data leave the platform? | List supported API and CSV datasets, Google Sheets or Looker Studio paths, BI workflows, webhooks, alerts, and analytics integrations. | | Security and privacy | How do you handle customer prompts, competitors, screenshots, reports, and user data? | Attach security documentation, subprocessors, retention/deletion policy, encryption, training-data policy, DPA, and audit or compliance status. | | Pilot plan | What pilot scope do you recommend for our use case? | Propose prompt count, surfaces, competitors, markets, report cadence, success metrics, support model, and timeline. | | Commercials | How is pricing structured? | Explain plan limits, prompt/run limits, seat limits, brand/client limits, add-ons, onboarding fees, overages, renewal terms, and exit/export support. | ## Copyable AI visibility RFP table notes - Ask vendors to mark unsupported, beta, and roadmap items plainly. - Do not accept screenshots of dashboards as a substitute for sample exports. ## What this RFP should prove The RFP should prove whether the vendor can collect reliable AI visibility data, preserve evidence, explain methodology, support your team structure, and pass procurement review. ## Require specific AI surfaces Do not ask for comprehensive AI coverage without naming the surfaces you care about. Include ChatGPT, Perplexity, Gemini, Claude, AI Overviews, and buyer-requested surfaces such as Copilot, Google AI Mode, Reddit, and citation extraction where relevant. ## Require raw examples Ask every vendor to attach at least five sample prompt-level records and one sample export so your team can inspect the actual data. Tip: Make unsupported surfaces acceptable if they are disclosed. Hidden gaps are the real risk. ## How to score vendor responses Use a shared scoring matrix instead of letting each vendor's own score become the evaluation model. Weight evidence, coverage, methodology, reporting, security, and usability separately. ## Score data quality before UI polish The interface matters, but it cannot compensate for shallow evidence, limited model coverage, or weak exports. ## Use pass-fail gates Security, tenant isolation, client confidentiality, and export rights should be pass-fail gates for enterprise and agency purchases. Tip: If a vendor cannot explain a score in plain language, do not use that score as a buying criterion. ## What to include in the pilot ask The RFP should ask for a pilot plan because AI visibility data quality is easiest to evaluate against your own prompts, competitors, markets, and reporting workflow. ## Define the pilot dataset Provide a prompt set, competitor list, market or locale requirements, reporting recipients, and action criteria before the pilot starts. ## Ask for a final readout The vendor should deliver an executive summary, prompt-level appendix, top source gaps, competitor movements, and recommended next actions. Tip: A pilot that cannot produce a clear final readout is not ready for executive reporting. ## Attach a scoring model to the RFP An RFP is easier to evaluate when vendors know how answers will be scored. Add weights for coverage, evidence, methodology, reporting, integrations, security, support, and commercial fit. This prevents a vendor from steering the conversation toward the strongest part of its product while leaving critical gaps vague. ## Use weights, not vibes For example, evidence quality and exportability may matter more than dashboard polish for an analytics-led team, while client-safe reporting may matter more for an agency. ## Keep disqualification gates separate Security, confidentiality, tenant separation, and data deletion should be pass-fail gates where needed, not low-weight line items that can be offset by nice charts. Tip: Send the scoring rubric with the RFP so every vendor understands the buying standard. ## Separate vendor capability from your rollout plan A vendor may support a feature, but your team still needs an owner, cadence, and decision process. Add an RFP section asking how the vendor supports rollout, not just implementation. ## Conclusion A strong AI visibility RFP is not just a feature questionnaire. It is a forcing function for comparable evidence. It should make vendors disclose supported, beta, roadmap, and unsupported surfaces; explain how metrics are produced; show exports and reports; and define a pilot that proves the platform against your real prompts. The best response is the one your buying committee can validate, not the one with the broadest category language. ## Action checklist - Make unsupported surfaces acceptable if they are disclosed. Hidden gaps are the real risk. - If a vendor cannot explain a score in plain language, do not use that score as a buying criterion. - A pilot that cannot produce a clear final readout is not ready for executive reporting. - Send the scoring rubric with the RFP so every vendor understands the buying standard. - A strong AI visibility RFP asks for methodology, prompt-level evidence, citations, competitors, exports, reporting, permissions, security, and support. - Require vendors to show the raw answer and cited URLs behind any score or executive metric. ## Frequently Asked Questions ### What should an AI visibility RFP include? An AI visibility RFP should cover monitored AI surfaces, prompt methodology, evidence retention, citation or source extraction where supported, competitor analysis, perception analysis, reporting, exports, roles, integrations, security, privacy, support, pilot scope, and pricing. The most important requirement is inspectable evidence behind every executive metric or vendor score. ### How many vendors should we invite? Most teams should invite three to five vendors, or two vendors plus an internal-build benchmark if engineering is considering a custom approach. More vendors can slow the process without improving the decision. The key is to make each vendor answer the same questions and provide the same sample evidence. ### Should the RFP ask for proprietary score methodology? Yes, but it should not demand trade secrets. Ask vendors to explain inputs, weighting logic, confidence limits, freshness, and how edge cases are handled in plain language. Buyers do not need the exact formula to be public, but they do need enough methodology to trust, compare, and explain the result. ### Can we use this RFP for GEO and AEO tools? Yes. The same RFP structure works for AI visibility, GEO, AEO, LLM monitoring, and AI search monitoring tools because the buying questions are similar. You may need to adjust surface coverage, citation requirements, reporting needs, and security language depending on whether the vendor is focused on monitoring, optimization, content, or workflow. ### What should vendors attach to their RFP response? Ask for sample prompt records, sample reports, sample exports, methodology notes, support documentation, security and privacy materials, and a proposed pilot plan. These attachments are often more useful than the written answers because they show how the tool behaves when a team tries to inspect, explain, and share the data. ## Useful next steps Related tools, templates, and research surfaces for this workflow. - [Procurement guide](https://trakkr.ai/guides/ai-search-monitoring-procurement-guide) - Use the full buying workflow around this RFP template. - [Enterprise requirements](https://trakkr.ai/guides/enterprise-ai-search-monitoring-requirements) - Translate the RFP into must-have enterprise requirements. - [Security questions](https://trakkr.ai/guides/security-privacy-questions-ai-visibility-tools) - Attach AI visibility-specific security and privacy questions. - [Trakkr demo](https://trakkr.ai/demo) - Invite Trakkr to respond to the same RFP criteria. ## Related procurement guides Adjacent RFP templates, scorecards, and checklists in Trakkr's AI visibility procurement toolkit. - [AI Search Monitoring Procurement Guide](https://trakkr.ai/guides/ai-search-monitoring-procurement-guide) - A procurement workflow for buying AI visibility, GEO, and AEO software: requirements, RFP, vendor demos, security review, pilot, and rollout. - [GEO/AEO Vendor Evaluation Checklist](https://trakkr.ai/guides/geo-aeo-vendor-evaluation-checklist) - A copyable checklist for evaluating GEO, AEO, and AI visibility vendors across coverage, prompts, citations, reporting, exports, teams, and security. - [Security and Privacy Questions for AI Visibility Tools](https://trakkr.ai/guides/security-privacy-questions-ai-visibility-tools) - A procurement security questionnaire for AI visibility, GEO, and AEO vendors covering prompts, competitors, screenshots, reports, retention, access, and model training.