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.

7 — Profile a new warehouse table before depending on it

The setup

An analyst on the data team has just been asked to build a churn dashboard. The dashboard needs to read from a table no one on the team has used before — a subscription_events table that was loaded last quarter.

The sources

  • A live connection to the analytics warehouse (a database connection the team set up two weeks ago)

The question

The analyst opens the table in VDF AI Data, runs exploratory analysis, and asks:

“Walk me through this table. What’s missing, what’s drifting, and what columns should I trust before I build a churn dashboard on top?”

The output

A clean EDA report: 12% of country values are missing, three columns show medium drift over the last 60 days, and the subscription_status column has only three valid values today vs. five last quarter. The analyst sees the changes a stakeholder would have surfaced as a bug in a week — before writing a single dashboard query.

The takeaway

The cost of investigating a table dropped from “open a notebook for half a day” to “click Run and read for ten minutes.” The dashboard that gets built is built on data the analyst actually trusts.

8 — Prepare fine-tuning data from your team’s voice

The setup

A customer success leader wants the team’s drafting agent to write follow-up emails in their voice — not a generic professional voice. Two years of past customer emails sit in a connected mailbox folder.

The sources

  • A connected folder of past customer-success emails
  • A feature list over the connected helpdesk: the columns that matter for drafting follow-ups (ticket summary, resolution, customer segment)

The question

The leader opens Fine-tuning datasets, picks a prompt/completion mapping template, and previews:

“For each ticket, the prompt is the ticket summary and resolution. The completion is the team’s actual follow-up email.”

The output

A 600-example dataset, exported as JSONL, ready to feed into the team’s fine-tuning workflow. The agent trained on the dataset starts producing follow-ups that sound — for the first time — like the team wrote them.

The takeaway

The work of capturing a team’s writing voice — typically months of style-guide iteration — collapses into a structured dataset built from what the team has already done.

9 — Semantic search across a customer’s data warehouse

The setup

A solutions architect at a B2B SaaS company is preparing for a renewal. They need to answer the customer’s question — “what’s the most common failure mode our users hit in your product?” — from the customer’s own production data.

The sources

  • A live database connection to a sampled mirror of the customer’s production warehouse
  • A vector index built over the error_logs table’s message column

The question

“Across the past 30 days of error messages, what are the three most common failure patterns? Group by theme, not by exact message. Pull a representative example for each.”

The output

A clean three-theme summary — “credential expiry,” “rate-limit on the upload endpoint,” and “stale-cache reads on the dashboard” — each with a representative error message and an approximate count of how often it appeared. The architect walks into the renewal with a real, sourced answer.

The takeaway

A question that would have required a custom SQL engagement is answered in 20 minutes — by searching the warehouse the same way the team searches a wiki.

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.

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