Product Bench & GTM Engines - AI workflows for product, content, community, and outbound
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Give Claude Code and Codex a product brain. Turn your team’s knowledge into sharper research, stronger strategy, better specs, and faster execution.
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Best
Hunter
📌
Hey Product Hunt, Mohit here from Tetriz.
Agents made engineering faster. Then the bottleneck moved.
Product decisions still needed to be explained in every prompt. GTM still stitched market signals together by hand. Getting what we learned back into product planning took deliberate work.
Today, we’re open-sourcing 4 tools we use across Product and GTM.
Product: start with a shared brain
Product Bench brings product decisions, research, GTM plans and analytics into context people and agents share. It helps carry that understanding into specs, plans and analysis, with citations and evidence gaps called out.
GTM: take it to market, bring the learning back
Content Engine turns community, news and competitor signals into evidence-graded themes, then writing your team approves.
Buzz Engine sends a weekday Slack digest of relevant Product Hunt, Hacker News and Reddit threads, with draft responses your team can use as inspiration and post.
Leadscore scores leads against your rubric and routes them to Apollo or HubSpot, skipping anyone who replied or opted out.
We use what we learn from those signals and conversations to shape the next product decision.
Start with whichever repeated task is costing you time: rebuilding context, finding content ideas, following community conversations or qualifying leads.
All 4 tools are open source and MIT-licensed. Read the code, try them in your workflow, and tell us what breaks.
What’s one workflow your Product or GTM team has changed with agents that’s actually worked?
Replies
Hey Product Hunt, Mohit here from Tetriz.
Agents made engineering faster. Then the bottleneck moved.
Product decisions still needed to be explained in every prompt. GTM still stitched market signals together by hand. Getting what we learned back into product planning took deliberate work.
Today, we’re open-sourcing 4 tools we use across Product and GTM.
Product: start with a shared brain
Product Bench brings product decisions, research, GTM plans and analytics into context people and agents share. It helps carry that understanding into specs, plans and analysis, with citations and evidence gaps called out.
GTM: take it to market, bring the learning back
Content Engine turns community, news and competitor signals into evidence-graded themes, then writing your team approves.
Buzz Engine sends a weekday Slack digest of relevant Product Hunt, Hacker News and Reddit threads, with draft responses your team can use as inspiration and post.
Leadscore scores leads against your rubric and routes them to Apollo or HubSpot, skipping anyone who replied or opted out.
We use what we learn from those signals and conversations to shape the next product decision.
Start with whichever repeated task is costing you time: rebuilding context, finding content ideas, following community conversations or qualifying leads.
All 4 tools are open source and MIT-licensed. Read the code, try them in your workflow, and tell us what breaks.
What’s one workflow your Product or GTM team has changed with agents that’s actually worked?
Repo links:
https://github.com/tetriz-ai/product-bench
https://github.com/tetriz-ai/content-engine
https://github.com/tetriz-ai/buzz-engine
https://github.com/tetriz-ai/leadscore
Built by the Tetriz team.