The database stores structured experience, AI fields organize it, and the bot delivers answers. The order matters: build facts and accountability first; otherwise a bot turns fragments into a smoother answer that is still confused.
Minimum rule: every AI answer must lead back to a record with an owner and an original source.
01 / PRACTICEDo not start with a digital twin
Choose one recurring question first: what do new colleagues repeatedly ask, how are customer needs classified, or when should a matter be escalated? A narrow scenario can become usable in a week. “Copy all of the founder’s experience” is too large. “Answer ten common questions before a new colleague accepts an intake” is a testable first target. Speaking like a person is secondary. The system must use the same facts and judgment framework, and know when to stop and hand the question to a responsible person.
02 / SIX TABLESSplit experience into six tables
Create a table for identity and boundaries (what may be done, promised or escalated); products and services (audience, deliverable, process, duration, price rules and exceptions); customer needs (common questions, trigger, necessary information and next step); cases (background, method, result, evidence and limits); judgment frameworks (conditions, priority, red lines, counterexamples and human decision points); and expression and content (approved answers, templates, glossary and prohibited wording). Do not build everything at once. Twenty real records in “common questions” and “judgment frameworks” are enough to test the structure.
03 / PRACTICEMake every record usable
Every record needs a title, the original material or link, a factual summary limited to what the material supports, conditions where it applies, conditions where it does not apply, a status of pending verification, reviewed or retired, and the owner with review date. Do not put customer names, identity numbers, contact details or detailed case files into a general knowledge base. Use anonymous identifiers and keep only the minimum material needed for the task.
04 / PRACTICELimit AI to three jobs
Use AI to extract facts, questions, action items and open points from a meeting record; classify needs, cases and risk levels under predefined tags; and draft a reply or checklist only from reviewed records. Feishu Bitable AI fields can generate text, classify and extract information, and automations can call AI. That does not make output trustworthy. Require the record identifier, label insufficient evidence as “pending verification”, and prohibit unsupported additions. A useful instruction is: list confirmed facts, questions to verify, next actions and tags; do not invent; say what material is missing.
05 / GOVERNANCECreate a review, deduplication and retirement workflow
New material enters a pending-organizing view with its original link. AI makes a structured draft and sets it to pending verification. An owner checks the source and conditions, then marks it reviewed. The bot reads only the reviewed view. Create a new version when a conclusion changes; do not silently overwrite an earlier decision. Retire records that are outdated or disputed and preserve the reason. Use source link plus date as a unique key: repeated imports should alert a person instead of replacing a reviewed record.
06 / PRACTICEConnect the bot last
Feishu Open Platform can connect a bot and APIs to the reviewed Bitable view. Before launch, install three gates. Permission: decide who may ask, see originals or edit. Evidence: every answer gives a record ID or source link; without evidence it declines to answer. Action: the bot may suggest, but sending, deleting, paying, filing or promising always needs human confirmation.
07 / PRACTICEStart with one law-firm scenario
For a law firm, begin with a first consultation from a new client: information to collect, questions that cannot be assessed online, a preliminary conflict check, who receives materials and when a lawyer must take over. The bot returns a materials checklist and next-step guidance only. It does not guarantee an outcome. Each entry should show its policy, template, meeting note or owner confirmation; anonymous minimum data; its professional boundary; and the reviewer plus update date.
08 / WEEKEND MVPBuild the minimum version over a weekend
Pick one question repeated at least three times a week. Create two tables: common questions and judgment frameworks. Enter twenty real records with sources. Add pending verification, reviewed and retired statuses. Use AI to extract five new records and check each one manually. Ask two colleagues to find answers through the reviewed view only, then record what they cannot find or cannot understand. Version one works when colleagues can find an answer, see its evidence and know when to ask a person.
Sources and verification
- 黄小木：用飞书多维表格打造你的数字分身 — the three-layer structure and six asset types were independently checked and extended with governance boundaries.
- Feishu: Bitable AI features — AI fields and automation capabilities.
- Feishu Open Platform overview — bots, apps and open APIs.
- Verified July 26, 2026. Interface names, plans and quotas can change; this guide does not promise a particular free allowance.
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