智能路由 · 分离式系统 · AI Builder for 第一阶段 & 2
多-model routing is not a tech flex it is a 利润杠杆. 本地 7-model orchestration eliminates per-token API costs, reduces unnecessary GPU usage, and enables predictable unit economics at SME scale.
Intelligent request distribution across multiple LLM providers routes queries to the optimal model based on task complexity, latency requirements, and cost efficiency. Ensures high availability and fallback resilience.
独立, isolated AI sub系统s that operate autonomously without cross-contamination. Each 系统 manages its own model lifecycle, data pipeline, and execution context enabling parallel processing and fault isolation.
Generic Agent AI serves as the core AI builder orchestrating both phases. 第一阶段 establishes foundational detached AI 智能体 and routing infrastructure. 第二阶段 scales with 高级模型 orchestration, multi-provider load balancing, and autonomous 系统 optimization.
规划ned layer for decomposing complex requests into discrete subtasks before routing. 延迟 and throughput effects will require validation during implementation.
Proposed adapter layer for supported bank-statement formats before model routing. Additional document families require dedicated parsers and validation fixtures.
对我们主权 LLM 乐团的自动循环评估——跨越 6 个加权维度的蛛网图。
AINNA 神经路由器 selects the right model based on task type. The selected model helps build or improve the 系统. The detached 系统 then runs the business process independently.
商业 系统s that AI helps build, but the actual operation runs through database, queue, cron, worker, dashboard, and human approval.
Generic Agent AI 不仅是自主代理。
For 第一阶段 and 第二阶段, Generic Agent AI 充当 AI Builder and 系统构建器.
它有助于：
最终业务流程必须通过常规软件架构独立运行。
AI builds. 系统 run.
知识 stays local.
AINNA uses 智能路由 and 分离式系统 to reduce unnecessary GPU usage, improve cost efficiency, and support scalable 中小企业AI infrastructure.
