From Data science, ML model training and LLM fine-tuning to
building RAG pipelines, Evals and Deploying production ready AI systems.
NEO is designed to save you from 1000s of hours of grunt work by automating the entire machine learning workflow.
It is powered by a system of agents that works in parallel to solve your most urgent and important ML engineering problems.
And More...
Ask Neo to fix AI model training pipeline
Add new AI features in your brownfield projects
Analyze data leakage in your training pipeline
NEO makes ML engineers superhuman
Neo uses multi-step reasoning with its extensive knowledge base and GPU sandbox computing to perform iterative ML experimentation for automatic model optimization. Neo understands the task and runs 100s of experiments and automatically evaluates their performance against targets, and select best models.
Take control over Neo with our interactive chat interface. Guide Neo's exploration of models and approaches, providing context and expertise to accelerate tasks and projects. Streamline your ML workflow with Neo's flexible and responsive assistance.
Unlock Neo's full potential with Multi Step Reasoning. Neo proactively explores multiple approaches, assesses potential outcomes, and evaluates risks to find the most effective solution for your challenge. Leveraging its reasoning capabilities, Neo anticipates challenges and refines its recommendations, ensuring a swift and successful path forward.
Use cases
NEO helps with the AI engineering work behind modern AI products: model evals, prompt tests, RAG pipelines, dataset prep, experiments, and reports. Share the goal and context, then review, steer, and use the final outputs.
State the outcome in natural language. Fine-tune a model, ship an agent, build a benchmark — no boilerplate prompt engineering.
Point NEO at your repo, data, connectors, and constraints so the plan fits the hardware and conventions you already run.
NEO writes the code, runs long experiments, evaluates, and hands back versioned artifacts for your review.
Replay on real scenarios, ask for sweeps, harden failure modes, and promote the winning run to staging when you are ready.
Evaluate & Benchmark
An async LLM benchmarking platform that evaluates models from OpenAI, Anthropic, Google, and more across 150+ real-world tasks covering coding, reasoning, structured output, and...
Evaluate & Benchmark
Closed-loop system: an optimizer LLM writes prompts and reads failure summaries, a target LLM runs batches against synthetic data, and a JSON ledger tracks every iteration until scores converge.
Build Agents
10 specialized agents coordinating over async message bus: +4.62% returns across 250 days of S&P 500 data.
