Why We Invested in TrainLoop

We're investing in TrainLoop because they're making advanced model fine-tuning accessible to all developers.

The Real Challenge

Prompt engineering has become a significant bottleneck for AI development. Developers spend countless hours tweaking instructions, hoping to coax reliable performance from their models. Meanwhile, large tech companies leverage sophisticated internal tools to fine-tune models precisely - capabilities that remain inaccessible to most development teams.

Jackson (former Gemini optimizer at Google) and Mason (AI engineering lead at Second) lived this pain firsthand. They've been on both sides of the problem and teamed up to build TrainLoop to solve it.

What Makes TrainLoop Different

TrainLoop isn't just another fine-tuning platform - they're bringing reinforcement learning techniques previously reserved for AI labs to everyday developers:

1. Capture real usage data with three lines of code
2. Fine-tune for reasoning - not just pattern matching
3. Deploy instantly and automatically through familiar APIs

This matters because most businesses aren't trying to beat generic benchmarks. They need models that excel at specific tasks for their use cases - generating clean code, ensuring compliance, or providing accurate industry information.

We've repeatedly seen custom-tuned models deliver dramatic improvements in both efficiency and performance. TrainLoop makes this accessible without requiring a team of ML PhDs in house.

Why Now?

The market has shifted from "can AI do this?" to "can AI do this reliably?" For companies building serious applications, the limitations of prompt engineering are becoming painfully obvious. TrainLoop enters at exactly the right moment.

Their end-to-end solution handles everything from data collection to deployment, eliminating the complexity that has made fine-tuning inaccessible to most teams. By focusing on reasoning fine-tuning, they're attacking the core problem: getting models to consistently produce the right outputs for your specific use case.

We're betting on TrainLoop because they're solving the right problem with the right approach at the right time. We're confident that Jackson and Mason represent the highest-caliber talent working on this challenge today. The team that democratizes AI fine-tuning will unlock tremendous value, and we believe TrainLoop is positioned to be that team.

Want to stop fighting with prompts? Join TrainLoop's alpha and get a model that actually understands your business.

Marble Creek Ventures is an early investor in TrainLoop.