We recently wrapped up VectoLens, a review-labeling engine for a client drowning in customer feedback. They were manually sorting through 500+ reviews per week, trying to identify issues, complaints, and feature requests.
The Challenge
The client's support team was spending 8-10 hours weekly just categorizing feedback. Critical issues were getting lost in the noise, and positive feedback wasn't being leveraged for marketing.
Our Approach
Instead of building a complex ML model from scratch, we used GPT-4 with carefully crafted prompts and a validation layer:
- •**Prompt Engineering**: We created specific prompts for different review types (bug reports, feature requests, praise, complaints)
- •**Confidence Scoring**: Each classification includes a confidence score. Low-confidence items get human review
- •**Feedback Loop**: The system learns from human corrections to improve accuracy over time
What Worked
Speed: Processing time dropped from hours to minutes. The system now handles 500 reviews in under 10 minutes.
Accuracy: After two weeks of training, we hit 87% accuracy on classifications — better than the previous manual process.
Cost: Total monthly cost is $47 in API fees. Previously, they were spending $320/week in labor costs.
What We'd Improve
Context Window: Long reviews sometimes get truncated. We're exploring chunking strategies for the next version.
Edge Cases: Sarcastic reviews still trip up the classifier. We need better examples in our training prompts.
Integration: The current setup requires manual export/import. Direct integration with their help desk would save more time.
The Lesson
Most AI projects don't need custom models. Well-designed prompts + validation + human feedback loops often deliver better results faster and cheaper than complex ML pipelines.
VectoLens saved our client 32 hours per month and improved their response time to critical issues by 65%. Sometimes the simplest solution is the right solution.