Model layer · Multi-model scheduling — every task gets the best-fit model
Multi-model scheduling engine
The multi-model scheduling engine turns open-source and commercial LLMs into one governable pool: connect once, route by task, tune on real business data, and accept on measured outcomes.
01 · Context
Connect once, pick the best model per task
Enterprises often face vendor lock-in on model choice and weak feedback loops that stop at demo quality. The engine sits between Antelope DB and industry AI apps—unifying model access below and serving TuringFin, TuringRAG, and the trading agent above with routing, orchestration, and governance.
02 · Capabilities
Connect, route, orchestrate, govern
03 · Value
Industry knowledge as usable AI
Flexible models avoid lock-in; smart routing keeps cost in check; human-in-the-loop makes outputs accountable for finance and government.
04 · Customers
Validated in production
- 01TuringFin institutionalMulti-agent debate with cross-checks for investment views
- 02GF Securities trading instructions96% ticker ID accuracy with deterministic validation
- 03TuringRAGFlexible model switching for private deployments
05 · Delivery
Private inference, data in-domain
Private and domestic-stack delivery with local inference; data stays in-domain. Delivery & support →