Solving the hard problems in MMM through AI
From organizational memory decay to siloed insights and unrealistic budget plans-here’s how Aryma Labs is redefining marketing measurement with AI driven innovation.
Everyone is suddenly racing to add AI into MMM.
But I increasingly feel many vendors are solving the easy problems.
Creating MCP servers to expose MMM outputs to Claude is not some massive breakthrough. You can vibe code it nowadays.
The real problems in MMM were never about “connecting to AI”.
The real problems are:
• Why do organizations forget MMM learnings every year?
• Why do MMM outputs live in silos?
• Why do budget optimizers recommend mathematically optimal but business impossible plans?
These are the problems we have been obsessed with solving at Aryma Labs.
📌 MMM Synapse
One of the hidden problems in MMM is organizational memory decay.
- Teams change.
- Agencies change.
- Decks get buried.
- Insights disappear.
A new CMO comes in and the same questions get asked again.
MMM Synapse was built to solve this exact issue.
It acts as a memory layer for marketing measurement - connecting past MMMs, experiments, decks, decisions and business context into a searchable intelligence system.
More than connecting MMM outputs to external AI, connecting and establishing intelligent logics within many MMM reports is the harder task.
And we are proud to have cracked this.
📌 MMM Singularity
Another major problem is MMM outputs are disparate and analysts fail to connect the dots into one coherent narrative.
MMM Singularity was built to solve this.
It acts as an intelligence layer that connects the various outputs of MMM (Contribution chart, predicted and actual charts, effect share and spend share, budget optimization) into a unified reasoning system.
📌 Aryma Nebula
Most budget optimizers fail because they optimize mathematics instead of reality.
They ignore:
• Interaction effects within channels
• Spend floors
• Risk tolerance of the brand
Aryma Nebula was designed very differently.
The goal was not just optimization. The goal was decision making grounded in business reality.
A mathematically perfect answer that cannot be implemented is useless. That is why our multi agentic approach helps. One checks the mathematics and the other checks the business relevance.
📌 The reason for our success
Honestly, I think a big reason we could build all this is because both Venkat and I are still extremely hands on.
- We still talk to analysts, create and debug MMM models.
- Still sit in client discussions.
Venkat Raman is perhaps the only founder in the marketing measurement circle who has engineered and built NLP products (yes the word that was cool before Gen AI :) )
Further we realized the importance of having a team of AI experts very early on. AI for MMM is serious business. It can't be vibe coded. Since 2022, we have invested heavily in our AI team and R&D. And Recently launched our AI vertical Aryma AI
And the results are now paying off.
Schedule a demo with us (https://aryma-ai.arymalabs.com/) and you will be amazed by what we’ve built and how it works on real data.
Thanks for reading.
For help with MMM, Causal Marketing Experiments and Experimentation, get in touch with us.
We also build some pretty cool AI products to aid Marketing Measurements. Check out our products page to know more -
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