25
every small update to our voice agent used to feel like a gamble. now we just simulate everything
r/SaaS
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 adherence |
| LangSmith | Not provided | AI Monitoring | Logs traces for AI models |
| Honeycomb | Not provided | Observability | Monitors latency spikes |
| Pulse for Reddit | Not provided | Community Monitoring | Tracks user complaints about odd responses |
USER NEEDS
Pain Points:
- Manual testing is time-consuming and inefficient
- Bugs often sneak into production despite manual QA
- Difficulty handling edge cases (accents, background noise, awkward phrasing)
- Risk of critical errors in high-stakes domains like healthcare
- Underlying LLM changes can cause subtle but impactful failures
Problems to Solve:
- Ensuring reliability of voice/chat agents before deployment
- Comprehensive testing for edge cases
- Automating QA processes to replace manual testing
- Detecting hallucinations and instruction deviations in AI agents
- Monitoring model drift and silent provider tweaks
Potential Solutions:
- Simulated testing with auto-generated edge cases
- Automated stress testing at scale (e.g., 1,000 simulations overnight)
- Version pinning and A/B testing for model updates
- Monitoring tools for hallucinations, drop-offs, and instruction adherence
- Combining multiple monitoring tools (LangSmith, Honeycomb, Pulse for Reddit) for comprehensive oversight
GROWTH FACTORS
Effective Strategies:
- Building tools to solve personal pain points and validating them with others
- Focusing on high-stakes industries (healthcare, fintech) where reliability is critical
- Automating manual processes to demonstrate clear value
Marketing & Acquisition:
- Showcasing real-world impact (e.g., saving clients from painful bugs)
- Offering interactive demos (e.g., fun test where agent calls user)
- Leveraging platforms like ProductHunt for visibility
- Engaging in community discussions to share insights
Monetization & Product:
- Targeting customer-facing AI agents as key market
- Providing features that address specific pain points (edge case generation, hallucination tracking)
- Emphasizing time/cost savings compared to manual QA
User Engagement:
- Creating interactive experiences (e.g., test call with QA report)
- Encouraging peer discussions and knowledge sharing
- Building trust through transparency about product capabilities