Adaptive ML is a GenAI infrastructure company that helps enterprises evaluate, fine-tune, and serve open-source LLMs using reinforcement learning.
Their product, Adaptive Engine, lets teams bootstrap small models (like Llama or Gemma) with synthetic data, train them with RL, benchmark them against APIs like GPT-4o, and continuously improve performance with honest production feedback—often beating larger proprietary models on cost, latency, and task accuracy.
Adaptive Engine: the flywheel behind enterprise AI
This is the system that keeps AI models getting better over time—not in theory, but in production.
01 — Adapt
Start by bootstrapping your model with reinforcement fine-tuning. Generate synthetic data, train with reinforcement learning, and build small, specialized models that can outperform large, generic frontier APIs for your specific use case.
02 — Evaluate
Measure what actually matters to your business. Custom AI judges evaluate performance using metrics that closely reflect real-world, production behavior—not just lab benchmarks.
03 — Validate before production
Don’t guess. Test.
Use A/B testing to compare models and validate user preference before anything goes live. That way, performance is proven—not assumed—before it reaches users.
04 — Serve & adapt
Once in production, the loop continues. Track business outcomes and model interactions in real time, then feed that data back into the system to keep improving performance over time.
In short: build, test, learn, and adapt—continuously.
