Business AI Workspace
Start with one high-frequency task, and customize input, AI processing, system actions, human confirmation, evaluation, and operations interface.
The following is a fictional order from the public learning kit, not a real customer case. First understand input, calculation, confirmation, and delivery, then judge what your own process needs.
The order's product amounts are 120 and 180 CNY, with allocated discounts of 12 and 18 CNY, and the second line has a refund of 50 CNY. Currency is CNY for all; there is also a source system summary control total of 220 CNY. This example excludes taxes and shipping.
AI helps sort fields and explain discrepancies; amounts are calculated according to confirmed formulas. The two lines' net sales are 120−12=108 CNY and 180−18−50=112 CNY, totaling 220 CNY. If the total order discount of 30 CNY is deducted twice, it would be incorrectly calculated as 190 CNY.
The owner verifies line items, currency, and independent summary, and approves import only after confirming the difference is 0. If any line lacks currency, stop and request completion; AI must not guess CNY. System writing still requires separate authorization.
Leave 2 lines of import preview, field mapping, net amount formula, reconciliation result, and pending questions. The learning kit provides fictional samples and templates, and does not auto-connect or write to Feishu. Enterprise engineers can use the same materials to practice reproduction and handover.
Every workbench begins with real tasks and system boundaries. You can build each one individually or connect them in stages to form a business chain.
Unified form entry, source parameters, duplicate leads, assignment, and follow-up status.
Compares channel spend, on-site sources, orders, refunds, and operating metrics in parallel.
Organizes operating materials based on confirmed revenue, costs, refunds, taxes, and fulfillment metrics.
Retrieves trusted knowledge, checks orders, generates suggestions, and escalates high-risk issues to humans.
Lets employees search valid versions by role, cite sources, and identify conflicts.
Connect requirements, code, tests, reviews, releases, and runtime records.
Define business responsibilities and verification methods before deciding on models, interfaces, and automation levels.
Who is accountable for outcomes now, what triggers tasks, and what outputs count as done.
Which systems are read, which version is authoritative, and what identities can access.
Define what AI can complete, prepare, and must not decide.
Every query, write, external send, and irreversible action has status, confirmation, and rollback.
Use normal, abnormal, conflict, missing, and high-risk samples to validate quality and permissions.
Define monitoring, costs, knowledge updates, support, versions, and next review.
AI System Architecture
Do not build a large all-encompassing platform first. Start with a verifiable closed loop, then expand systems, users, and automation scope based on evidence.
SCOPE
Fix users, inputs, outputs, success metrics, exclusions, and human responsibilities.
CONNECT
Connect necessary sources, identity permissions, and tools, recording versions, freshness, and failure status.
VERIFY
Test normal, abnormal, conflict, and high-risk cases, recording quality, latency, cost, and human judgment.
OPERATE
Complete monitoring, rollback, human takeover, support, and knowledge updates to form stable operations.
Deliverables adjusted to the actual system, but scope, validation evidence, and operational responsibilities must be itemized and confirmed.
Tasks, roles, data, permissions, tools, human boundaries, non-scope, and success metrics.
Display sources, status, pending items, results, and exceptions for actual users.
Connect agreed APIs, databases, documents, accounts, and business tools, with fallback on failure.
Samples, results, versions, launch criteria, deployment scope, monitoring, and rollback evidence.
Operating manuals, support channels, change processes, training materials, and internal maintenance responsibilities.
Clarifying responsibilities and system boundaries upfront can significantly reduce rework.
Not necessarily. Most projects first connect queries, suggestions, confirmations, and write-backs on top of existing systems; only evaluate replacement when there is clear evidence that the existing process cannot support it.
Yes, and it is often better as the first phase. First stabilize one high-frequency task and clear users, then expand based on operational evidence.
Depending on data sensitivity, performance, cost, and existing infrastructure, you can choose cloud, private, or hybrid deployment, but the deployment form cannot replace permissions, evaluation, and operational governance.
This is exactly the handover goal. It can be combined with FDE training to let internal engineers participate in blueprint, implementation, evaluation, launch, and review.
Explain the owner, current tools, inputs, outputs, exceptions, and desired improvements. No accounts, keys, or customer details are required in the first communication.
FORMWEFT