Run LLMs, agents, RAG and Python fully in-browser for private inference

Run LLMs, agents, RAG and Python fully in-browser for private inference targets a $6.0B = 2M businesses × $3K ACV (AI developer & internal automation tooling addressing privacy-sensitive applications) total addressable market with medium saturation and a year-over-year growth rate of 35% YoY — based on growth rates reported in enterprise AI tooling and developer platform adoption (Gartner / McKinsey summaries on AI adoption).

Key trends driving demand: Privacy and data residency demands are rising — companies prefer solutions that prevent data exfiltration, creating demand for local-first AI tools.; Edge and client-side compute improvements (WebGPU, WASM) are enabling complex workloads in the browser, unlocking new product form factors.; Open-source LLM progress and quantization tools have reduced model size and cost, making local inference viable for many applications.; Organizations are moving from experimentation to production for internal AI assistants, increasing appetite for secure, low-cost deployment options..