Go to market experimentation platform
YC startup here
Problem: There are so many tools for marketing & outreach (LinkedIn, Google Ads, Apollo, Meta). Founders end up duct taping all that data to a spreadsheet. But the feedback loop is slow, manual, and hard to follow.
Demandlabs.ai is the all in one GTM solution.
You:
- Define the personas that you want to sell to
- Connect your Google Ads, Meta, and cold outbound softwares.
- Run experiments for each persona across different channels and get quick feedback on positive or negative signals. Kill what doesn't work and scale what does.
- An AI agent finetunes and reacts to your campaigns, so everything runs autonomously within your defined guardrails.
We'd love for feedback so feel free to join for early access!
demandlabs.ai
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This is a strong problem because GTM experiments usually fail in the feedback loop, not the channel itself.
Founders do not just need another outreach or ads tool. They need to know which persona, channel, message, and signal pattern is actually working before they waste weeks duct-taping data together manually.
The one thing I’d pressure-test hard before early access is the name. Demandlabs.ai explains the category, but it also feels fairly broad and generic for a YC-backed GTM intelligence platform. If the product is moving toward autonomous experiment execution and signal-based campaign decisions, the brand should feel more like a serious decision system, not just another demand-gen lab.
Beryxa.com would fit that direction better as a sharper GTM experimentation and decision-intelligence brand. It gives the product room to become bigger than “marketing tools connected together,” while still feeling enterprise enough for founders and growth teams to trust.
Worth thinking about before users, docs, and early-access assets lock around Demandlabs.
This is actually solving a very real pain point for early-stage founders.
Most teams don’t fail because they lack tools — they fail because the feedback loop between campaigns, personas, attribution, and iteration is fragmented across 10 different dashboards and spreadsheets.
The “run experiments → detect signals → kill/scale automatically” workflow makes a lot of sense, especially if the AI layer can maintain strong guardrails instead of blindly optimizing vanity metrics.
I also think the timing is right. GTM is becoming more like engineering now:
continuous experimentation, rapid iteration, signal tracking, and automation orchestration across channels.
Would be especially interesting to see:
• Cross-channel attribution quality
• Persona memory/context handling
• How the AI agent prevents overfitting on short-term signals
• Integration depth with outbound workflows and CRM systems
Looks promising. Happy to test and give technical/product feedback if needed.
https://teams.live.com/l/invite/FAAk3iOSJkDyS11JQE?v=g1