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

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

Ito

Freemium

AI code review tool that builds and runs your app on every PR, catches runtime bugs, and attaches a failing test as evidence.

Targeted Test Plans: Reads the PR diff and description on open, then focuses testing on the user flows the change affects — no test cases to write and no suite to maintain.
Containerized Test Execution: For every PR, Ito builds a real single-use copy of your app from source and navigates it like a real user against your real backend.
Self-Healing Codebase: Proposes a fix as its own PR for the bugs it finds so the fix loop starts before an engineer opens the ticket.
Product Demo Videos: Posts a polished walkthrough of every change to the PR so reviewers see the feature in action, not just the diff.
Automatic Smoke Testing: Maps each change to at-risk user journeys and builds a smoke-test plan without human authoring.
Full PR Findings with Evidence: Every failure includes a video replay, exact lines responsible, logs, and reproduction steps, with severity ratings to prioritize.
Auto Re-run on Fix: Push a fix and Ito re-runs the failed flows automatically to confirm the bug is resolved before merge.
Agent Sandboxes: Gives autonomous coding agents a live, managed copy of your app to build and test against without hitting production.

AI-Generated PR Verification: Teams whose engineers and coding agents open many PRs a day get every one built, exercised, and evidence-backed before merge.
Catching Runtime-Only Bugs: Surface authentication, concurrency, service-authorization, and data-migration bugs a static reviewer or diff-only AI can't see.
Removing Manual QA Bottlenecks: Reclaim the hours each developer spends manually verifying AI-written code by delegating the pre-merge runtime pass to Ito.
Regulated & Security-Sensitive Codebases: Financial, healthcare, and defense teams get SOC 2 Type II, isolated test runs, and zero data retention on every PR.
Enabling Agent Automerge: Give autonomous coding agents a runtime gate so they can merge safely without an added human review bottleneck.
PR Demo Artifacts: Reviewers and PMs get a runnable video walkthrough of the feature attached to the PR instead of guessing from the diff.

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.