It's like having a mechanic who constantly monitors your car's engine and automatically fixes small problems before they become breakdowns—except for your AI.

Carrot Labs' platform continuously monitors AI agent performance in production against business-aligned metrics such as correctness, latency, and tool call success rate. When statistical drift is detected—whether from changing input distributions, updated APIs, or evolving user behavior—the system automatically triggers a selective fine-tuning pipeline. Rather than retraining the entire model (which is expensive and risks catastrophic forgetting), the platform isolates the degraded capability, curates relevant production data, and applies targeted fine-tuning or prompt adjustments. This closed-loop system ensures AI agents maintain reliability without human intervention, dramatically reducing downtime and the engineering burden of manual model maintenance. The evaluation environment mirrors real customer workflows, so retraining is validated against actual business outcomes before deployment.

It's like Netflix automatically re-learning your taste every time you binge a new genre, instead of waiting for you to angrily rate 50 movies.

It teaches a small, cheap AI to be almost as smart as the big expensive one—but only at the specific things your business actually needs.

Carrot Labs enables enterprises to distill the capabilities of large frontier models (e.g., GPT-5, Claude) into smaller, customer-owned models that are fine-tuned on proprietary data and workflows. The platform orchestrates a teacher-student training loop: the frontier model generates high-quality outputs on domain-specific tasks, which are then used as training signal to fine-tune a smaller, more efficient model. Carrot Labs' evaluation environment continuously benchmarks the distilled model against the teacher across business-relevant dimensions, identifying capability gaps and triggering additional distillation rounds as needed. The result is a proprietary model that captures institutional knowledge, runs at a fraction of the inference cost, can be deployed on-premise or in a VPC for data sovereignty, and improves over