llm-fine-tuning
(5)
Osmosis
Osmosis is a San Francisco reinforcement learning platform (YC W25) that helps companies fine-tune open-source models to beat foundation models on cost, latency, and reliability. Founded by Kasey Zhang and Andy Lyu, it lets teams train task-specific AI agents with multi-turn tool use over the Model Context Protocol, then export or self-host the weights they own.
InfoObjects Inc.
InfoObjects Inc. is a San Jose-based AI consulting and digital transformation firm, founded in 2005 by Rishi Yadav. Built originally around the open-source big data stack - Apache Spark, Kafka and Hadoop - the company has repositioned around generative and agentic AI, offering services from data engineering and cloud migration to LLM fine-tuning, retrieval-augmented generation and AI application development. It partners with AWS, Google Cloud, Microsoft Azure, Databricks, Anthropic and OpenAI, and works with enterprise clients across finance, healthcare, manufacturing and retail.
Arcee AI
Arcee AI is a San Francisco-based open intelligence lab that builds small, efficient, US-made language models enterprises can own, customize and run inside their own infrastructure. Founded in 2023 by former Hugging Face and Roboflow operators, the company pioneered practical techniques like model merging (via its acquisition of mergekit) and Spectrum training, and has expanded from small language models into its own open-weight Arcee Foundation Model family, the Trinity mixture-of-experts models, and agentic products such as Arcee Conductor and Arcee Orchestra. Its pitch: smaller, domain-adapted models that rival frontier systems at a fraction of the cost while keeping data private.
Bespoke Labs
Bespoke Labs is a Mountain View AI research lab that builds tools and datasets for training more reliable AI models and agents, starting with synthetic data curation and hallucination detection and expanding into realistic reinforcement-learning environments for long-horizon agentic AI. Its open-source projects, including Bespoke Curator, Bespoke-MiniCheck, OpenThoughts, Terminal-Bench, and GEPA, are widely used across the AI research community, and the company raised $40M in seed and Series A funding by mid-2026 from investors including Wing Venture Capital, 8VC, and Mayfield.
Snorkel AI
Snorkel AI is a Redwood City-based enterprise AI company spun out of the Stanford AI Lab in 2019. Its Data Development Platform lets enterprises programmatically label, curate, and evaluate the training and evaluation data that powers custom LLMs, RAG systems, and AI agents - turning expensive manual annotation into a software-engineering discipline.