VDF AI Data

Use cases for VDF AI Data

Six worked examples — turning real documents and connected apps into structured outputs your team can act on.

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How to read these scenarios

Each one has the same shape:

  • The setup — who’s doing the work and what’s at stake.
  • The sources — what’s been uploaded or connected.
  • The question or task — what gets asked of VDF AI.
  • The output — what comes back.
  • The takeaway — why this scenario pays back the time spent setting Data up.

Copy any of these patterns. Each one assumes your sources are already uploaded or connected — see Connecting sources if you haven’t started yet.

1 — Turn a 60-page policy into a one-page checklist

The setup

A new internal data-handling policy was just published. The compliance lead needs every team to follow it — and a 60-page PDF isn’t going to get read.

The sources

  • The full policy PDF (uploaded)
  • The company’s existing compliance handbook (in connected Confluence)

The question

“Using the attached new policy and our existing compliance handbook, turn the new policy into a one-page checklist of actions teams need to take. Group by team type (engineering, sales, ops, HR). Cite the policy section for each item.”

The output

A one-page checklist with three columns: action, owning team, policy section. Every line cites a specific page of the source policy. The compliance lead reviews, prints, distributes.

The takeaway

A document that would have taken a full day to translate by hand becomes a 15-minute task. And every team gets the same trustworthy interpretation.

2 — Compare two versions of a customer contract

The setup

A sales engineer is renewing a contract with an enterprise customer. The customer sent a redline; the SE needs to know what actually changed before flagging it to legal.

The sources

  • Last year’s signed contract (uploaded)
  • This year’s redlined draft (uploaded)

The question

“Compare the attached signed contract from last year with the redlined draft for this year. Produce a section-by-section list of substantive changes. Skip cosmetic edits. Highlight anything that affects pricing, term length, indemnification, data handling, or SLAs.”

The output

A clean comparison report grouped by section. Each substantive change is described in plain English with a “where in the document” reference. Cosmetic changes are omitted. The customer’s deeper asks (a softer indemnification clause; a longer term) are clearly flagged.

The takeaway

What used to be a multi-hour read-and-compare exercise becomes a 20-minute review. The SE walks into the legal conversation already knowing what matters.

3 — Build a glossary from scattered team notes

The setup

A new hire is onboarding to a team that’s been operating for two years. They keep encountering acronyms and project names that aren’t documented anywhere.

The sources

  • The team’s connected Confluence space
  • The team’s connected Slack channel (read access)
  • A folder of past planning docs (uploaded)

The question

“Build a glossary of internal terms used by this team. For each term, give a short plain-language definition, an example sentence using the term, and a citation to the first place it appears in our sources. Sort alphabetically.”

The output

A 40-term glossary covering acronyms, project codenames, internal jargon, and team-specific concepts. Every term cites where it first appears.

The takeaway

A reference doc that nobody had time to write — but everyone needed — is built in an afternoon. The new hire gets up to speed twice as fast.

4 — Find every reference to a topic across your sources

The setup

A product manager is preparing a customer escalation. They need to find every place the team has discussed this customer’s stated pain points across calls, tickets, and internal notes.

The sources

  • The connected helpdesk (Zendesk, Jira Service Management, or similar)
  • The connected meeting transcript app (Zoom, Granola, or similar)
  • The team’s internal notes (connected Confluence space)

The question

“Find every reference across our sources to the topic ‘data ingestion delays for AcmeCorp.’ Include support tickets, meeting transcripts, and internal notes. For each, summarize in one sentence what was said and link to the source.”

The output

A chronological list of 14 references, each with a one-sentence summary and a clickable link. Some are tickets, some are call quotes, some are notes. The PM can see the full pattern at a glance.

The takeaway

The PM walks into the escalation with the full history. They aren’t guessing, and they aren’t surprised by what the customer references.

5 — Summarize a quarter of standups for a status report

The setup

A VP of Engineering wants a clean quarterly summary of what shipped, what slipped, and what was learned — pulled from 13 weeks of standup notes plus the team’s release log.

The sources

  • The team’s standup notes (connected Google Drive folder)
  • The release log (connected GitHub repo or release tracker)
  • The team’s quarterly OKRs (uploaded)

The question

“Produce a quarterly engineering summary covering: what we shipped vs. the OKRs we set, what slipped and why, top three learnings, and recommended focus for next quarter. Plain prose. Eight to ten paragraphs.”

The output

A polished summary that reads like a strong human draft — what shipped (with examples), what slipped (with named reasons), three concrete learnings, and a recommended focus area for the next quarter. Each section references specific standup notes and release entries.

The takeaway

The VP gets a draft summary in 10 minutes. They edit for tone, send to leadership. The whole exercise — which previously consumed a full Friday — is done before lunch.

6 — Onboard a new customer with a tailored playbook

The setup

A customer success lead is starting a new enterprise customer. They want a tailored onboarding playbook that combines the team’s standard process with what’s specific to this customer’s environment and goals.

The sources

  • The team’s standard onboarding playbook (in connected Confluence)
  • The signed contract (uploaded)
  • Pre-sales meeting transcripts (uploaded)
  • The customer’s environment doc (uploaded)

The question

“Build a tailored 90-day onboarding playbook for this customer. Start from our standard playbook structure, then adapt it based on what they told us in pre-sales, what they bought, and their environment. For each step, name an owner, a deliverable, and a target completion date. Add a ‘special considerations’ section at the bottom that captures anything unusual.”

The output

A 6–8 page customized playbook in the team’s standard structure, with customer-specific adjustments at every step. A clear “special considerations” appendix names three pieces of unusual context the team should be aware of.

The takeaway

Every customer gets a thoughtful, individualized onboarding plan — without the lead spending three days writing it.

Patterns across the examples

These six scenarios share a few signals worth recognizing:

  • Sources are reused. The same connected Confluence space, the same uploaded contract — these aren’t one-off file drops. They’re durable knowledge that pays back many times.
  • Outputs are structured. Checklists, comparisons, glossaries, summaries, playbooks — not just paragraphs. Structure is what makes the output actionable.
  • Citations are part of the deliverable. Every output points back to a source. Trust is built by traceability.
  • The work was previously high-friction. All six replaced multi-hour or multi-day manual tasks. The ROI compounds with each repetition.

When your task has those traits, Data is the right starting point.

Where to go next