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Built an AI support sidekick that chewed through 1 000 tickets last week; looking for SaaS eyes before we open the doors
r/SaaS
7/29/2025
Content Summary
The post describes the development of an AI-powered support assistant called CoSupport AI Agent, which was trained on two years of Zendesk data. It handled approximately 1,000 tickets with 99% accuracy last week. The author is seeking feedback from the SaaS community before launching it publicly. They are particularly interested in fail-safes and integrations that would make the AI more trustworthy for customer interactions.
Opinion Analysis
Mainstream opinion: Most commenters seem to agree that the AI agent is promising, especially given its high accuracy and low error rate. There's a strong emphasis on the need for fail-safes like agent handoff and conversation logging. Some users also expressed interest in how the AI was trained, asking whether it used custom embeddings or prompt finetuning. A few comments highlighted the importance of customer sentiment tracking and rate limiting as essential features. Overall, the discussion reflects a cautious but optimistic view toward AI in customer support, with a focus on reliability and user trust.
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| CoSupport AI Agent | https://www.producthunt.com/products/cosupport-ai | Customer Support AI | Trained on Zendesk history, handles support tickets with high accuracy, uses Go service and S3 prompts |
| Zendesk | https://www.zendesk.com | Help Desk Software | Used as historical data source for training the AI agent |
USER NEEDS
Pain Points:
- Overwhelmed with customer support requests
- Need reliable AI tools to handle support tasks efficiently
- Concerns about AI accuracy and potential hallucinations
Problems to Solve:
- Reduce the workload of support teams
- Improve response accuracy and consistency
- Ensure seamless fallback mechanisms when AI fails
Potential Solutions:
- Implement agent handoff fallback for edge cases
- Add conversation logging and replay for debugging
- Include customer sentiment tracking
- Introduce rate limiting to prevent system overload
GROWTH FACTORS
Effective Strategies:
- Leveraging AI to automate customer support processes
- Using shadow mode to test with design-partner customers before public launch
- Continuous fine-tuning and deployment via GitHub Actions and Terraform
Marketing & Acquisition:
- Building a product that solves a real-world problem (support ticket overload)
- Engaging with the SaaS community for feedback and validation
Monetization & Product:
- Focus on high accuracy and reliability as key selling points
- Potential for integration with existing help desk systems like Zendesk
User Engagement:
- Seeking feedback from the SaaS community before public release
- Encouraging discussion around AI safety and reliability in customer support