Ninjō AI vs TryCase: Features, Pricing & Which Is Better (2026)

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

Ninjō AI

Ninjo

Freemium

Infrastructure for AI sales agents on Instagram, WhatsApp and other DM channels, built and improved by talking to an LLM over MCP.

MCP Server Control Surface: Exposes agent creation, testing, analysis and improvement as MCP tools, so Claude, Claude Code, Codex or ChatGPT becomes the interface instead of a dashboard.
Cortex Playbook Library: Ships prompt templates, KPI rubrics and anti-patterns distilled from agents that ran in production, so a new agent inherits patterns that already converted rather than starting blank.
Multi-Channel DM Deployment: Connects agents to Instagram, WhatsApp and other direct-message channels where the selling actually happens, without a separate build per channel.
Versioned Changes with Rollback: Every edit to an agent is versioned and instantly reversible, so a bad prompt change during a live launch can be undone rather than debugged under pressure.
Synthetic Conversation Testing: Runs an agent against generated conversations before it reaches a real inbox, surfacing broken qualification logic ahead of launch.
Follow-Ups and Keyword Triggers: Fires scheduled follow-up sequences and keyword-based branches so stalled conversations get reopened automatically.
Built-In CRM and Funnel Analytics: Ninjo Studio provides real-time conversation views, contact records and funnel reporting in one panel for when you want direct oversight.
Payment Recovery Flows: Agents can chase declined payments conversation by conversation, a pattern the team credits for recovering 47 declined payments in a single four-day launch.

Creator and Coach Launches: Running a short high-volume launch where an agent qualifies inbound DMs, handles objections and sends payment links at a pace a human team cannot match.
Instagram Lead Qualification: Filtering hundreds of daily inbound Instagram messages down to the prospects worth a human sales call.
WhatsApp Sales Follow-Up: Reopening conversations that went quiet with timed follow-up sequences instead of leaving them to decay.
Agency Multi-Client Operations: Managing many client agents from a chat interface so a three or four person team can operate over a hundred agents.
Declined Payment Recovery: Having an agent work through failed transactions individually to recover revenue that would otherwise be written off.
Rapid Agent Iteration: Rewriting an agent's qualification logic mid-campaign and rolling back immediately if conversion drops.

Paid

An AI QA agent that opens your app on every pull request and posts a verdict, captioned video and screenshot back to GitHub.

PR-Triggered Runs: Connecting a repository is enough - every pull request marked ready for review starts a test run with no pipeline config.
Journey Selection From Diff: TryCase reads the changed code and chooses which user flows are actually affected rather than replaying a whole suite.
Disposable Linux Environments: Each run gets a fresh environment with terminal and browser control, so state from earlier runs never leaks in.
Video and Screenshot Evidence: Results arrive as a captioned recording plus a screenshot commented on the PR, showing exactly what the app did.
Bring Your Own AI: Connect Codex through an existing ChatGPT subscription or supply an OpenRouter key and pay your provider directly for inference.
Agent Skills: Packaged skills teach Claude, Codex, Cursor and other compatible agents to drive TryCase environments without manual setup.
Parallel Workers: Up to twelve workers per bot run journeys concurrently, with testing time tracked separately for setup, the primary bot and each worker.
Usage-Based Hour Pools: Monthly plans grant a shared pool of end-to-end testing hours across setup, PRs and retries, with no automatic overage charges.

Pre-Merge Verification: Confirm a checkout or signup flow still works before approving a pull request, without pulling the branch locally.
Visual Regression Review: Catch layout and rendering breakage that unit tests pass over by watching the recorded walkthrough.
Agent-Written Code Review: Require an AI coding agent to return screenshots and recordings proving its change runs, not just a diff.
Suite-Free E2E Coverage: Give a small team end-to-end coverage without staffing the maintenance of a Playwright or Cypress suite.
Demo Clips From Branches: Reuse the captioned videos as short product demos of a feature still sitting on a branch.
Release Triage: Scan verdicts across several open PRs to decide which changes are safe to batch into a release.