Why an AI 核心 是必需的
AI should not be one model answering everything. It should be an orchestrated 系统 where every task is routed, checked and executed by the correct component.
- 模式l dependency everything depends on one model's behaviour
- 高 token consumption simple tasks billed like complex ones
- 未验证的输出——无独立验证
- 审计性弱——决策轨迹有限
- 执行不一致——无策略或审批控制
- 任务-matched routing the right component for each job
- 独立验证——确定性引擎检查输出
- 人类 关卡s where risk requires them
- 完整 execution trace on every task
- 避免浪费的 Token 感知路由
传统 AI 应用
用户 → One 模式l → Direct 输出. Fast to deploy, but the model becomes the single point of 失败ure, cost and risk.
- 解释与执行之间没有分离
- 输出 is trusted on the model's word alone
- 没有内置审批或审计层
AINNA AI 核心
用户 → 任务 分析 → 智能路由 → Specialised 代理 → 独立验证 → 已批准 输出. Every task runs through the right component under governance.
- 确定性 系统s handle what can be computed
- 模式ls handle what needs reasoning
- 审批s and audit recorded on every execution
AI should not be one model answering everything. It should be an orchestrated 系统 where every task is routed, checked and executed by the correct component.
任务 路由 Simulator
Pick a task (or type your own) and watch the core classify it, select an agent and model class, and decide whether a detached 系统 or human approval is required.
路线 a task
预设 tasks or describe your own.
路由 rules are deterministic: creative work → generative model · calculations → deterministic engine · sensitive decisions → analysis + human approval · document extraction → parser first · repetitive classification → 轻量模型 · complex reasoning → 高级模型.
代理 群体
代理s are not generic chatbots. Each has a defined role, restricted 工具, input and output schemas, permission boundaries, confidence reporting and escalation rules. Select a node to inspect it.
Select an agent node to inspect its role, 工具, schemas, permissions and 审计追踪.
AI 推荐ation vs 确定性 执行
模型进行解释。 分离的系统进行验证。 严重 rules are never left to probability. Pick a reconciliation scenario and watch the three panels.
AI 解读
任务理解 + 提议流程独立式 引擎
确定性验证，无概率受管控 结果
已验证、已警告、已拒绝或送审引擎的判定独立于模型。更改输入会确定性地改变判定。
置信度 & 验证 Lab
模式l confidence is not 已验证 correctness. Toggle the 验证层 and watch the final decision confidence change.
人工控制 & 审批 工作流
风险 decides how much automation a task gets. Choose a risk mode and watch the workflow path change.
Token 与算力 效率 计算器
Adjust the workload assumptions and compare a single-model approach against AINNA routed execution.
Simple tasks within the deterministic and lightweight lanes can be served 来自缓存d results (≈0 令牌).
部署 探索r
The AI 核心 deploys the same architecture in private cloud, on-premise or hybrid configurations. Switch modes to see how the boundary changes.
定制er-controlled network
All components 实时 inside a private cloud network controlled by the customer. 推理 endpoints are never exposed publicly when the private deployment configuration is correctly implemented.
私密 API 关卡way
流量 enters through an allowlisted 关卡way behind a customer-controlled VPN.
加密存储
审计 logs and data encrypted with deployment-specific keys.
托管 scaling
计算在客户的云账户内弹性伸缩。
本地 infrastructure
The full core runs inside the organisation's own infrastructure. 数据 never leaves the 内部网络.
本地 model option
模式ls can run locally where required for control or sovereignty.
内部网络访问
推理 traffic stays on the organisation's own network.
组织控制的数据
数据, logs and audit records remain under organisation control.
敏感执行在本地
任务 that must not leave the boundary run on local detached 系统s and agent runtime.
选定模型工作负载在云端
重型模型工作负载仅在策略允许时在云端运行。
中央治理层
路由, approvals and audit stay 受治理的 from one place.
政策-based routing
路由器根据策略为每个任务决定本地或云端。
AI 核心 运营 控制台
核心在负载下的模拟实时视图。筛选任务流并实时查看指标更新。
| ID | 代理 | 任务 | 风险 | 验证 | 模式 | Token |
|---|
产品 架构
五层，每层均可点击。它们共同构成受治理的智能技术栈。
AI 核心 Across AINNA
滚动选择器并检查每个场景的受治理流水线。
财务 document processing
银行 statements and ledgers to structured, reconciled records.
工程 设计 validation
发布前检查规格与计算。
半导体 workflows
设计 参数 validated against PVT and process rules.
网络安全 operations
信号s classified and escalated under policy.
科学研究
多-source literature grounded before claims are made.
零售 intelligence
需求 signals and inventory data turned into 预测s.
ESG 计算
环境 metrics computed and audited per reporting standard.
机构智能
敏感报告在受管控的边界内综合生成。
将 AI 构建为 基础设施, Not Just an 界面
Move beyond isolated chatbots and deploy 受治理的智能 that can route, validate, execute and improve across real operational 系统s.
Run AI as 基础设施, Not Just an 界面
Move beyond isolated chatbots and deploy 受治理的智能 that can route, validate, execute and improve across real operational 系统s.
