Enterprise
Three production systems for teams that train,
serve, and operate AI on their own infrastructure
Distributed post-training across, preference optimization, reinforcement learning, and SFT.
Kubernetes-native operators with drop-in compatibility for Unsloth and TRL — zero migration time. Custom evaluation framework that plugs into internal model registries.
- Methods
- SFT, Curriculum-SFT, RSFT, DPO, ORPO, PPO, GRPO, GRPO++
- Environments
- Web-RL and Computer-RL with mock-app harnesses
- Eval
- Distributed framework with checkpointing
Distributed LLM inference profiled across vLLM, SGLang, LMDeploy, and TensorRT on production hardware.
Speculative decoding, chunked-prefill, disaggregated inference, KV cache strategies. Custom blackbox benchmarking with Poisson workload generation surfaces real GPU bottlenecks.
- Techniques
- Flash Attention, chunked-prefill, speculative decoding, disaggregated inference
- Peft
- LoRA, Q-LoRA, ZipLoRA — incl. SDXL on H100
- Hardware
- A100, H100, A10 fully profiled
Agentic training environments and a modular RAG library, with open benchmarks for the field.
Browser and computer-use agents trained for short-horizon workflows at 90% confidence. API and MCP agents in custom RL environments. Financial modeling agents that produce DCF and LBO models from SEC scraping.
- Methods
- Browser, computer, API, MCP, financial (DCF, LBO)
- Environments
- Modular — parsing, chunking, retrieval evals at every stage
- Eval
- Distributed framework with checkpointing
