AI Visibility Platform RFP Checklist
Procure an AI visibility platform with business, functional, methodology, data, security, service and pilot requirements.
An AI visibility RFP should require method transparency, raw evidence, data controls, and a pilot with acceptance criteria. Feature lists alone cannot show whether a platform measures the engines, markets, and business questions you actually need.
Business Requirements
- Objectives, audiences, markets, and use cases.
- Required engines and product surfaces.
- Prompt volume, repetition, and cadence.
- Competitors, products, and languages.
- Reporting audiences and action workflows.
- Budget range, term, and implementation timeline.
Functional Requirements
- Prompt library, tagging, versioning, and approvals.
- Scheduled and on-demand tests.
- Mentions, citations, recommendations, sentiment, and accuracy.
- Raw answer, source, date, mode, and market retention.
- Alerts, annotations, tasks, reports, APIs, and exports.
- Multi-brand workspaces and role-based permissions.
Methodology Questions
Ask vendors to disclose sampling, retries, personalization controls, location methods, classification rules, model-assisted labeling, invalid-run treatment, score formulas, and platform changes. Require sample sizes beside rates.
Use the statistical confidence guide to evaluate claims.
Data and Security
Request architecture, subprocessors, data locations, encryption, SSO, audit logs, retention, deletion, incident response, business continuity, and AI-training policies. Include legal review for submitted prompts or sensitive client data.
Service and Ownership
Define onboarding, prompt research, implementation, training, support SLAs, analyst services, quarterly reviews, and offboarding. State that the buyer owns or can export prompts, history, classifications, and reports.
Pilot Acceptance Criteria
Test real prompts across required markets. Before starting, agree thresholds for:
- valid-run completion;
- source and answer capture;
- classification accuracy on a human-reviewed sample;
- permission isolation;
- export completeness;
- dashboard latency;
- support response;
- normalized total cost.
Scoring Model
Weight business fit, methodology, data quality, security, workflow, service, and cost. Keep minimum security and evidence requirements as pass/fail gates so a low price cannot compensate for unacceptable risk.
Frequently Asked Questions
How long should the pilot run?
Long enough to test repeated collection and at least one reporting cycle. Define dates and workload in advance.
Should vendors guarantee visibility gains?
No. They should guarantee contracted platform functions and service levels, not outcomes controlled by third-party AI systems.
What evidence should finalists provide?
Raw sample data, methods, security documents, reference workflows, export examples, and a dated commercial schedule.
