Field notes · Topic
Voice & knowledge.
veridive began by answering questions from videos with a citation to the exact second, and the lesson carried over: people trust answers they can check. These notes cover knowledge, documents and recorded conversations, where that rule matters most.
9 notes
Notes on this topic
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Answers with receipts: why citations matter at work.
Before veridive helped companies, it answered questions from videos with a citation to the exact second. The lesson carried over: people trust answers they can check.
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Why AI makes things up, and how to design so it gets caught.
You can’t remove hallucinations entirely, but you can make them rarer and visible: ground answers in approved sources, allow “I don’t know”, check names and numbers against systems of record, and put a person where a wrong answer is expensive.
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Do you need an AI assistant, or just better search?
When people need to find a document, better search with good metadata may be enough, and it costs less. When they need an answer assembled from several passages, with a citation, an assistant earns its keep. Many teams need search first.
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Before the AI assistant: a clean-up checklist for your documents.
An assistant can’t be more current or consistent than its library. Before building, name an owner for each document set, retire duplicates and old versions, fix what scans and tables hide, and mark what is draft, final or expired.
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Why document assistants give wrong answers, and where to look first.
When a document assistant is wrong, the model is usually the last suspect. Most errors come from the library and the search: outdated or duplicate documents, broken tables, passages cut mid-thought, missing permissions or a question that needed clarifying.
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Clause extraction for legal teams: what AI can and can’t check.
AI is good at finding, extracting and comparing clauses against your playbook across many contracts; it is not the lawyer. Start with a clause list and a playbook your legal team wrote, cite every extraction, and keep judgment on risk with people.
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How to build a call quality scorecard AI can apply consistently.
AI can review every call only against criteria it can apply the same way twice. Rewrite vague criteria such as “was empathetic” into observable behaviors, calibrate on past calls with the quality team, and cite every finding to the second.
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Meeting minutes with AI: decisions, owners and a link to the moment.
A useful meeting record is not a prose summary. It lists decisions, owners, deadlines and open questions, each linked to the moment it was said, drafted by AI and approved by a person, with consent and retention agreed up front.
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AI for tender and RFP responses: a playbook for bid teams.
Bid teams answer the same questions again and again, under deadline. AI helps most by finding the best past answer with its source, drafting for the new question and flagging what has changed, while subject experts approve every commitment.