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Playcall is the open-source alternative to Gong, built for AI-native GTM teams. Score every call against your playbook and the buyer context your rep was talking to. Works with MEDDPICC, BANT, SPIN, or upload your playbook and let AI generate the rubric. Get evidence-backed scores, missed moments, and outcome tracking. Every score comes with a coaching drill for the rep to run next. Self-hostable. No lock-in, no $30K/year contracts, or 10+ baked-in features your team never touches.
Playcall
Hey Product Hunt 👋
I've spent the last 5 years building GTM systems at AI companies like Sieve (YC W22), Ragie.ai, and Aviator (YC S21).
Most call intelligence tools are good at summarizing what happened, but weak at judging whether a rep actually followed the team's sales motion based on the buyer context/stage.
And context matters. A discovery call with a 50-person Series A startup buying a tool should not be scored the same way as a Fortune 500 vendor evaluation.
Here's the tell: the founders I know don't even trust Gong. They rawdog their team's calls themselves, rewatching every AE call, because $30K+/year of call intelligence still can't answer their actual question: did my rep say the right thing for this specific buyer?
So I built Playcall.
What Playcall does differently:
Buyer-Aware Scoring: Company stage, contact role, and deal context dynamically shape every scorecard.
Your Methodology, Not Ours: Score against MEDDPICC, BANT, SPIN, or your custom playbook. No framework? Upload your playbook and Playcall generates the rubric for you.
Outcome-Tied Scoring: Every score links to deal stage, outcome, and pipeline impact, so managers can see which behaviors actually move deals.
Coaching Drills, Not Just Feedback: Every score comes with a specific, actionable drill for the rep to run next.
Plug & Play with any LLM: Use your favorite model (Claude, GPT, Gemini, or 15+ others). No vendor lock-in.
Self-Hostable: Open source. Deploy to your own infrastructure. Data stays with you. You can run it for under $50/month, with LLM and enrichment usage as the main variable costs.
The goal is simple: help reps improve against the playbook they're expected to follow, help managers see which behaviors move deals, and spot objection patterns before they compound.
Live demo: playcall.dphenomenal.com
Repo: github.com/Dphenomenal101/playcall
Would love feedback from founders, GTM leaders, RevOps folks, and sales managers.
Two questions I'd love answers to:
What's the biggest gap you've seen in existing call coaching/intelligence tools?
For automatic call ingestion, would you rather connect an existing notetaker like Granola, Fathom, or Fireflies or have Playcall ship its own Zoom/Meet/Teams bot?
Dial
re: your second question, I'd lean towards connecting to an existing notetaker (Fathom/Fireflies) rather than shipping your own bot. Reps already grumble about one bot joining, a second one showing up with a different name would just add confusion in the call and probably slow adoption. On the outcome-tied scoring though, I'm a bit skeptical for early-stage teams specifically - "which behaviors move deals" needs a real sample of closed-won/closed-lost to mean anything, and a 5-person startup team might only close a handful of deals a month. How many scored calls before that correlation stops being noise?