Prelint vs Visiby: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Prelint and Visiby — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Prelint

Paid

AI reviewer that checks every pull request against your ADRs, docs and past decisions to catch product drift before it ships.

Context-Aware Review: Reviews each PR against your ADRs, product docs and past decisions instead of only linting style or syntax.
Product Drift Detection: Flags conflicts, gaps and drift between the PR's intent and your product context inline in the diff.
Agent Self-Correction: The Prelint agent can self-correct against detected violations before a human reviewer opens the PR.
GitHub & GitLab Integration: Runs on both major hosted git platforms with native PR/MR integration.
Isolated Infrastructure: Every organization runs on isolated per-organization infrastructure and Prelint does not train on customer code.
Usage-Based Pricing: $1 per completed review with $10 in free credits, no seats or subscription.

AI-Written PR Triage: A team using coding agents like Copilot or Claude Code lets Prelint gate agent PRs against product intent before humans look.
Architecture Enforcement: A platform team encodes architecture decisions in ADRs and uses Prelint to catch PRs that violate them.
Onboarding Safety Net: A new engineer's early PRs get reviewed against product context they haven't fully internalized yet.
Open Source Maintenance: A maintainer of a public repo enables Prelint (free for OSS) to review incoming contributions for product fit.
Cost-Controlled Review: A startup wants automated review without paying per developer seat and pays $1 per PR instead.

FNA Technology

Paid

AI visibility platform that tracks how ChatGPT, Perplexity, Claude, Gemini and AI Overviews cite your brand, and ships fixes.

AI Citation Tracking: Continuously samples roughly 50,000 prompts per week across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews to record where and how a brand is cited.
Per-Engine Visibility Scoring: Reports a composite AI Visibility score plus share of voice and prompts won or lost, broken out engine by engine so declines can be traced to a specific model.
Prompts & Citations Explorer: Lets teams open any tracked prompt and read the actual model answer to see which competitor was named and why.
Brand Entity Analysis: Maps the adjectives each engine associates with your brand versus competitors and suggests reframing plays to change that portrait.
Competitor Intelligence: Tracks rival citation share on comparison and 'alternatives to' prompts, highlighting categories where a competitor dominates.
Prioritized Action Plan: Converts findings into P0/P1 recommendations such as schema additions or comparison pages, each with a time estimate and projected score gain.
Site Audit for AI Parseability: Audits pages for missing entity definitions, structured Q&A data and other signals that prevent models from citing the site correctly.
White-Label Reporting and API: Higher tiers add white-label client reports, SSO/SAML and API access for agencies managing multiple brands.

AI Search Monitoring: Marketing teams track whether ChatGPT and Perplexity recommend their product or a competitor on high-intent category prompts.
Competitive Benchmarking: Brands quantify how much citation share a named rival is capturing on 'alternatives to' and 'best of' queries.
Content Prioritization: Content teams decide which pages to write or refresh based on which prompts are currently missed rather than on keyword volume alone.
Technical AEO Audits: SEO specialists find pages lacking FAQ schema or entity markers that keep answer engines from parsing them.
Agency Client Reporting: Agencies run pooled prompt tracking across multiple client workspaces and deliver white-label AI visibility reports.
Executive Reporting: Operators present a weekly digest showing search clicks alongside AI citation share to explain traffic shifts leadership sees.