Carrot Labs

Continuous learning and fine-tuning infrastructure for enterprise AI agents is a genuine and growing B2B need — the TAM expands as every enterprise deploys LLM-based workflows. ICP is clear: companies running production AI agents who need reliability, domain alignment, and performance improvement over time. Monetization path is obvious (SaaS/consumption-based on model optimization and inference).

Christopher Acker spent 6+ years as Principal Software Engineer at Skylo Technologies (a $116M-funded satellite IoT company) doing AI agent development and neural voice compression — solid technical depth. Yuta Baba was a Data Scientist at Snowflake for nearly 6 years, architecting ML models for financial forecasting through IPO, but his YC bio does not mention engineering depth. The combo is reasonably technical but not exceptional — no prior exits, no deep ML research background relevant to continuous fine-tuning infrastructure.

This space is crowded: Weights & Biases, Arize AI, Langsmith (LangChain), Braintrust, Scale AI, and Humanloop all compete in LLM evaluation/fine-tuning/monitoring. OpenAI itself offers fine-tuning APIs. The differentiation around 'continuous learning loops' is real but not a strong moat — big platform players (Databricks, AWS SageMaker) are building similar capabilities. No proprietary data advantage cited.