The setting
offers , a cloud platform that gives sales teams insights, forecasts and coaching, and runs AI agents that carry out tasks and automate workflows.
Tonight's deletion makes historical omissions permanent, while extraction makes disclosure irreversible.
The customer CIO wants the complete history retained, while security requires uncertain records to remain outside the destination.
Who you’d be doing this for
“My director asked why three deals slipped last quarter; those old calls are where the answers are.”
Hiwot Tsegaye · Regional Sales Manager · a character in this brief
His next pipeline review depends on historical buyer commitments that will disappear from the source.
What is at stake
A customer will delete 60,000 interactions tonight, with consent unresolved for 3,200. You must weigh permanent evidence loss against irreversible disclosure.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- historical continuity vs. disclosure limits
- verification coverage vs. deletion deadline
- machine classification vs. authoritative consent
- complete retention vs. bounded approval
Why Gong
Gong APIs and MCP carry historical revenue context into a customer environment whose permissions must remain meaningful after source deletion.
Written with these in mind
Not your kind of problem? 21 more at Gong, or browse every organization.
Where the organization context comes fromThe postings this brief was written from, and the reporting read before it.
The organization context comes from public information. The situation and characters are written for the brief.
Job postings it was written from
Public descriptions of the work at Gong. They are listed as sources, not as openings. The lines under a posting are its own words, where it meets this brief.
- AI Enterprise Architect (opens in a new tab)
- Architect how Gong can be used within and alongside a customer's AI ecosystem, including through Gong APIs, MCP, Gong Assistant, Deep Research, integrations, and workflow automation.
- Understand customers' AI ambitions, business objectives, technology stack, data environment, security requirements, governance models, integration landscape, and revenue workflows.
- Talent Engineer (opens in a new tab)
- Real experience building solutions that integrate with external systems — APIs, OAuth, webhooks, rate limits, error handling in production, not just in a tutorial.
- A governed data and context layer that lets AI tools answer questions using our real definitions and data, with proper permissions — not generic or hallucinated logic.
- Lead Solutions Engineer (opens in a new tab)
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.
Where similar work appears
Other live briefs connected to this one, and what connects them.
Similar problems at other organizations
Like this one, each of these is about an irreversible change forced by a fixed date.
Thirty-two sampled Voice Cloning assets conflict with their approval records, and automated summaries disagree with logs.
You must weigh a hard cutover for future exports against the risk of treating incomplete historical evidence as verified.
The old encrypted store retires on a fixed date, and 6.9% of historical artifacts still lack a source record and retention decision.
You have to weigh speed against proof that reports stay reproducible.
Agents have quoted 11 memories belonging to unrelated customers, and source conversations expire tonight.
You must weigh a broader permanent purge against retaining context whose ownership cannot all be checked in time.
The same setting, from another role
Each is set around Gong’s Gong Revenue AI OS, written for a different role. They’re separate briefs, not the same scenario.
RevOps leaders are appearing in two active corporate campaigns.
You must decide whether AI proposes segments only, or also recommends which active campaign yields.
Mid-market buyers question another revenue platform while cheaper AI tools promise deal-risk signals.
You must decide which consolidation concern earns a meeting and what proof belongs in the first message.
Enterprise CIOs are pausing technical validation over incompatible claims about revenue context.
You must choose a proof-backed story that can survive security review and still create qualified pipeline.