17
We made a testing tool for AI agents because we didn’t trust them anymore
r/SideProject
6/25/2025
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Cekura | https://www.producthunt.com/posts/cekura | AI Testing Tool | Simulates real conversations (voice + chat), generates edge cases (accents, background noise, awkward phrasing), stress tests agents, auto-generates test cases, tracks hallucinations, flags drop-offs, monitors instruction compliance |
USER NEEDS
Pain Points:
- Manual testing of AI agents is time-consuming and inefficient
- Bugs frequently slip into production despite manual efforts
- Difficulty simulating real-world edge cases (e.g., accents, background noise)
- Risk of critical errors (e.g., mishearing medication names in healthcare)
- Lack of trust in AI agent reliability
Problems to Solve:
- Automating testing for voice/chat AI agents
- Comprehensive simulation of real user interactions
- Identifying and preventing production bugs
- Ensuring agent reliability in customer-facing applications
- Scaling testing capacity beyond manual limitations
Potential Solutions:
- Automated testing tools that simulate conversations
- Edge case generation capabilities
- Automated test case generation based on agent descriptions
- Hallucination tracking and drop-off monitoring
- Instruction compliance verification
- Large-scale simulation capabilities (e.g., 1000 tests overnight)
GROWTH FACTORS
Effective Strategies:
- Building tools to solve personal pain points (scratching own itch)
- Transitioning internal tools into commercial products
- Focusing on high-stakes industries (e.g., healthcare) for reliability
Marketing & Acquisition:
- Showcasing real-world problem-solving (healthcare use case)
- Offering interactive demos (agent calls user and provides QA report)
- Leveraging Product Hunt for launch exposure
- Engaging communities (e.g., Reddit) for feedback and awareness
Monetization & Product:
- Targeting customer-facing AI applications (higher value use cases)
- Emphasizing time/cost savings (manual vs. automated testing)
- Highlighting risk prevention (avoiding critical errors)
- Providing tangible metrics (e.g., 1000 tests vs 10 manual tests)
User Engagement:
- Creating interactive experiences (fun test calls)
- Encouraging community discussion (asking how others QA agents)
- Soliciting feedback and collaboration (trading notes with builders)