03 / SCENARIOS
In these scenarios,
AI can take over part.
Pick a starting point by use case. No need to build a full system at once. Every scenario starts with a small-scope verification.
Enterprise Knowledge Q&A
Connect internal documents, product materials and policies to a retrieval system. Staff ask in natural language; answers come with sources. Verify with one department's documents before scaling.
- AI does: document parsing and ingestion, semantic retrieval, answer composition, source listing
- People do: confirm answers, flag wrong answers, maintain document versions
- Verification: hit rate and answer accuracy tested with a real question list
- Deliverables: knowledge base system, Q&A interface, tuning records
Typical start: one department + one document type + 20 real questions.
Content Production Pipeline
From trend tracking and topic analysis to drafts, images and layout publishing. Repetitive steps go to the pipeline; key nodes keep human review. Running in production for official-account content.
- AI does: trend tracking, topic analysis, first drafts, image generation, layout
- People do: topic decisions, fact-checking, final approval, publish confirmation
- Verification: run one full pipeline; check output quality at each node
- Deliverables: workflow configuration, node templates, operating manual
Typical start: one platform + one content direction + a fixed publishing rhythm.
Intelligent Document Processing
Read, extract and compare contracts, reports and forms into structured records. Test complex formats with real files before agreeing on scope.
- AI does: format parsing, key-information extraction, version diffing
- People do: verify extraction results, handle exceptions, confirm archiving
- Verification: extraction accuracy measured on 10–20 real files
- Deliverables: processing flow, extraction templates, accuracy test report
Typical start: one high-frequency document type + a defined field list.
Business System AI Assistant
Add Q&A, entry assistance and status alerts to existing systems or mini programs. AI output is presented as suggestion, confirmed by staff before execution — never a direct replacement for operations.
- AI does: common Q&A, entry suggestions, anomaly alerts
- People do: confirm suggestions, execute operations, report wrong suggestions
- Verification: small-scale trial measuring adoption rate and correction cost
- Deliverables: assistant UI, suggestion rules, usage dashboard
Typical start: one system + one high-frequency operation + a clear suggestion boundary.
HOW TO START
How a scenario starts,
from minimal verification.
Every scenario has a start small enough to show results within a week. Scale after verification; stop losses if it fails.
Pick one scenario
Choose the most painful one from the four above, or describe your own. Criteria: repetitive, rule-based, errors detectable.
Pick a starting point →Prepare one sample set
Prepare real documents, real questions or real process records. Sample quality determines the conclusion; demo data is not a substitute.
Prepare samples →Run one verification
Run the full flow in a small-scope environment; record quality, time cost and human touchpoints as the basis for scaling.
Start verification →QUESTIONS BEFORE WE BUILD
Let's be clear first.
Our scenario is not on the list — can you do it?
It can be assessed. Describe the problem, current manual process and expected outcome; we judge which steps suit AI and which do not, without forcing a fit to existing scenarios.
How long does verification take, and is it charged?
Depends on scenario and sample volume; initial verification of one scenario typically takes one to two weeks. Verification is quoted in the proposal and confirmed before start; if verification fails, no downstream development fee is charged.
Will AI replace existing staff?
The goal is to save time on repetitive steps, not to replace positions. Every scenario defines human review points; final output is checked by staff, with the system only advising and organizing.
How do we scale after verification passes?
Agree the extension scope per verification results: more documents, more workflow nodes or more users. Each extension re-confirms quality first; nothing is assumed to be one-shot.