Jidian起源, founded by Dai Zonghong — former co-founder of Zero One Everything and CTO of Huawei Cloud AI — recently unveiled its self-developed "all-factor LLM." This industrial-AI operating system compresses customization projects from "hundreds of people on site, months to deliver" down to "one person in control, two weeks to deliver." The core is building a digital-twin industrial world model that reflects real production processes.
Technically, the all-factor LLM runs in three steps — learn / optimize / deliver: the learning phase uses raw business data to train a continuously updatable digital-twin model, with attention focused on the data that affects key metrics like yield and capacity more; the optimization phase uses reinforcement learning to keep searching in the digital twin for better process combinations; the delivery phase has frontline workers input on-site parameters through a minimal app to get the current optimal operation plan.
Taking the world-model paradigm into industrial scenarios is worth watching: traditional consulting models expert experience into workflows, while the all-factor LLM embeds the workflow into a self-iterating digital twin and uses the model to mine every potential optimization point on the production line — improving the entire line's yield and capacity, rather than replacing workers. Landings in metallurgy, chemicals, and precision manufacturing show that the core value of an industrial LLM is not "chat" but "computing the business knowledge a company can bank."
Industrial LLMs are rapidly being de-bubbled this year: products that can run the digital-twin + RL + on-site delivery loop end-to-end, more than any parameter count or dataset, decide who really stays in the B-end customization market.