Consolidate your enterprise systems, documents, permissions, and role experience into a unified knowledge system, and equip every business role with a dedicated AI employee. The core landing logic: first let AI get familiar with your business rules, then let AI take over routine role work.
I. The Complete Core Operating Loop of AI Employees
The whole flow forms a complete business loop covering demand initiation, task processing, result delivery, and continuous iteration. Every operation is traceable, with support for human intervention and result review.
Tasks come through two kinds of trigger channels. Employees can initiate business requests directly in DingTalk, Feishu, or WeCom, and the system also supports automatic triggers for automated tasks like scheduled reports, order alerts, and approval flows. Using existing office software as the interaction entry lowers the barrier for employees and turns AI from a Q&A tool into an on-duty assistant available around the clock.
After receiving a request, the system identifies the operator, matches the corresponding business scenario, breaks down colloquial requests, and filters out overreach and invalid requests. When people from different roles raise the same need, the callable data and execution standards differ — precisely locating the task scope and keeping AI output aligned with the role’s demands.
For multi-step complex work, AI automatically breaks down the execution flow, matches the corresponding role skills, anticipates anomalies like missing data and rule conflicts, and prepares fallback plans. No step-by-step human commands are needed — it autonomously chains the whole business flow, solving the limitation of ordinary models that can only handle single simple instructions.
Before executing, AI pulls policies, pricing, metric definitions, and hands-on experience from the enterprise knowledge hub. Every basis for judgment comes from private company material, independent of generic internet information, ensuring output conforms to the company’s internal unified standards.
The system connects all business systems such as ERP, CRM, and MES. Before pulling data it validates role data permissions, then completes data cleaning, definition unification, and deduplication — meeting internal-control and audit data-security requirements while saving the repetitive work of manually organizing data.
Four modular skills — query & verify, analyze & judge, generate & write, and execute & write back — compose freely to fit different roles like sales, finance, and warehousing. Simply recombining the skill set quickly builds an AI employee for a new role, cutting custom-development cost and implementation time.
AI delivers results with complete data sources, policy basis, and analytical logic, plus actionable business suggestions — making it easy for managers to verify conclusions and removing the concern that AI output can’t be checked.
Results are pushed through multiple channels — office messages, data dashboards, and Excel reports — and high-risk operations like document changes and approvals get human-confirmation checkpoints, balancing automation efficiency with business safety.
After a single task ends, employee edits and feedback on AI content automatically flow into the enterprise memory library, continuously optimizing role execution logic and automatically clearing expired business rules. Layered memory retains session context, role hands-on experience, and long-term fixed business standards — unlike fixed traditional automation scripts, AI iterates and optimizes autonomously as the business grows.
Reports, documents, and analytical data generated by AI can write back directly into business systems, initiate approvals, and update operating dashboards — deeply embedding into the company’s existing workflows. The ten-step chain forms a complete, reviewable, auditable business loop.
II. Typical Role Landing Scenarios
A data analyst AI auto-aggregates operating reports and screens data anomalies to quickly locate operating problems.
An inventory manager AI monitors stock levels, issues stock-out alerts and intelligent restock suggestions, and actively manages inventory risk.
A sales operations AI follows customer opportunities, pushes overdue follow-up reminders, and auto-archives visit records, cutting sales administrative entry work.
An approval assistant AI verifies document material and business risk up front, assembling complete judgment basis for approvers.
A smart customer service AI answers customer queries from standardized company material and auto-routes complex requests to humans.
An executive daily brief AI aggregates a full-dimension operating brief and risk list every day, compressing decision time for managers.
These scenarios are all high-frequency, rule-fixed work that depends on private business data — landing them quickly lightens manpower and gains internal recognition from business departments.
III. Three Flexible Deployment Models
The full-stack model builds the knowledge hub and multi-role AI employees together — suited to mid-to-large enterprises planning comprehensive digital transformation, with higher long-term landing efficiency.
The knowledge-only model keeps existing AI tools and adds private knowledge base and permission-control capabilities, activating existing digital assets at lower investment.
The hybrid model keeps original generic office tools unchanged and uses AI employees only for high-consumption core business flows — suited to small and mid-size enterprises with limited budgets that want to pilot and validate in a small scope first, and the recommended starting model.
Core Differentiation & Value
Compatible with all existing business and office systems, protecting prior IT investment and lowering transformation cost.
Multi-layer security controls — fine-grained permissions, full-chain logs, and human-review backstop — meeting internal-control and compliance requirements for all kinds of companies.
Focused on automating real role work rather than simple conversational interaction, directly cutting repetitive human labor.
Capable of autonomous iteration and growth: business experience keeps sedimenting, and the longer it’s used, the better it fits the business.
Uses existing office software as the unified operation entry, lowering employee learning cost and raising daily tool usage.