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Quarterdeck AI
Trust. Then Tell.
Most forecast misses start as CRM hygiene problems — not presentation problems. Quarterdeck scores every HubSpot deal before generating a report: CRM Hygiene, Revenue Health, and a Board Readiness gate that blocks generation when data isn't clean enough. Fix the gaps in HubSpot, re-sync, and Quarterdeck writes the full revenue narrative automatically. Clean data first. Board report second.
Hey Product Hunt! 👋 I'm Kanthipriya, solo founder of Quarterdeck AI.
I built this after watching RevOps teams spend hours before every board meeting doing the same thing: export HubSpot deals, manually clean the data, hope nothing is missing or stale, then write the pipeline narrative from scratch.
The real problem nobody talks about: by the time you're writing that narrative, you don't actually know if the data behind it is trustworthy. Deals with no owners, close dates that passed months ago still open, one deal representing 40% of forecast. The report looks polished. The data isn't.
So I built the data quality gate that should exist before any board report is written. Quarterdeck scores your CRM hygiene, pipeline health, and board readiness — and blocks report generation until the data earns it. Then it writes the narrative automatically.
Rebuilt the entire core product in one week after feedback from a RevOps community told me I was solving the wrong problem. That conversation is what led to this.
Early access is free — would love feedback from anyone doing monthly board or investor reporting. What does your current process look like when you discover a data problem the day before the meeting? 👇
About Quarterdeck AI on Product Hunt
“Trust. Then Tell.”
Quarterdeck AI was submitted on Product Hunt and earned 5 upvotes and 1 comments, placing #66 on the daily leaderboard. Most forecast misses start as CRM hygiene problems — not presentation problems. Quarterdeck scores every HubSpot deal before generating a report: CRM Hygiene, Revenue Health, and a Board Readiness gate that blocks generation when data isn't clean enough. Fix the gaps in HubSpot, re-sync, and Quarterdeck writes the full revenue narrative automatically. Clean data first. Board report second.
On the analytics side, Quarterdeck AI competes within Productivity, Sales, SaaS and Artificial Intelligence — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Quarterdeck AI performed against the three products that launched closest to it on the same day.
Who hunted Quarterdeck AI?
Quarterdeck AI was hunted by Kanthipriya Mahale. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Quarterdeck AI including community comment highlights and product details, visit the product overview.
Kanthipriya Mahale
Hey Product Hunt! 👋 I'm Kanthipriya, solo founder of Quarterdeck AI.
I built this after watching RevOps teams spend hours before every board meeting doing the same thing: export HubSpot deals, manually clean the data, hope nothing is missing or stale, then write the pipeline narrative from scratch.
The real problem nobody talks about: by the time you're writing that narrative, you don't actually know if the data behind it is trustworthy. Deals with no owners, close dates that passed months ago still open, one deal representing 40% of forecast. The report looks polished. The data isn't.
So I built the data quality gate that should exist before any board report is written. Quarterdeck scores your CRM hygiene, pipeline health, and board readiness — and blocks report generation until the data earns it. Then it writes the narrative automatically.
Rebuilt the entire core product in one week after feedback from a RevOps community told me I was solving the wrong problem. That conversation is what led to this.
Early access is free — would love feedback from anyone doing monthly board or investor reporting. What does your current process look like when you discover a data problem the day before the meeting? 👇