Feedback Intelligence provides a platform that captures real‑time user evaluations of AI outputs and delivers observability into model performance in production. By embedding in‑app feedback widgets, aggregating and tagging responses, and offering dashboards and API access, it helps AI product teams and marketers identify errors, track accuracy trends, and feed annotated data back into model improvement workflows.
Funding
$900K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
AI-driven products often lack reliable mechanisms to capture real-world user feedback and monitor model performance, leading to undetected errors, reduced trust, and missed opportunities for improvement.
Solution
Feedback Intelligence offers a platform that closes the feedback loop by collecting user‑generated evaluations of AI outputs and providing observability into model behavior in production. The system integrates with existing workflows, such as ActiveCampaign, to surface real usage data, aggregate quantitative and qualitative feedback, and surface actionable insights for model refinement. By centralizing this information, organizations can quickly identify performance regressions, prioritize enhancements, and maintain higher quality AI experiences for end users.
Target Audience
Primary customers are AI product teams, data scientists, and marketers who need continuous, user‑driven evaluation of AI services, particularly those already using ActiveCampaign for customer engagement.
Features
- In‑app feedback widgets that let users rate and comment on AI-generated results in real time
- Automated aggregation and tagging of feedback to surface trends and error patterns
- Dashboards with metrics on model accuracy, confidence, and user satisfaction over time
- Integration hooks for marketing and CRM platforms (e.g., ActiveCampaign) to sync feedback data with customer profiles
- API access for exporting annotated feedback to data pipelines and model retraining workflows