🌻 How Sunflower Scaled Personalized Recovery Support to 80,000+ Users with Mem0
“Mem0 transformed our AI companion in just one day of integration, delivering personalized support that remembers user journeys and significantly reduced our costs. It's been one of our highest-ROI decisions.”
- Koby Conrad, CEO, Sunflower
About Sunflower
Sunflower is a digital health platform designed to support individuals on their recovery journey from addiction. By combining habit tracking, CBT-based interventions, and an always-available AI companion, Sunflower provides deeply personalized and scalable care. Their mission is bold: one billion sober days, achieved one day at a time.
The Challenge: Memory was Missing
Sunflower’s AI companion handles over 20,000 messages each day, powering conversations, check-ins, and push notifications. But early in development, the team ran into a fundamental limitation:
- Stateless sessions meant the AI was forgetting every past conversation and losing critical context.
- Users had to constantly repeat themselves, undermining emotional trust and engagement.
- Engineers had to include entire chat histories in each prompt, inflating cost and latency.
- Building an internal memory layer (summarization, retrieval, vector search) would delay critical product milestones.
“We were throwing the entire memory into context - not scalable. What we needed was summarized, relevant memory pulled just in time.”- Engineering Lead, Sunflower
The Solution: A Memory Layer Built for AI Agents
To solve this, Sunflower integrated Mem0, a memory layer purpose-built for AI applications. In just one day, they integrated Mem0 into their AI companion, with no custom retrieval logic or infra needed.
Immediate Improvements:
- Persistent, user-specific memory across each sobriety journey.
- Automatic summarization and retrieval of relevant context at the right moment.
- Reduced prompt size without losing continuity.
🚀 Results That Moved the Needle
Mem0 didn’t just solve a memory problem, it delivered clear business outcomes across product, engineering, and user experience.
| Use Case | Metric | Change | Notes |
|---|---|---|---|
| AI Messaging System | Token usage | ~70–80% | Summarized, relevant memory only |
| Product Engineering | Dev hours saved | ~3-4 weeks | No memory infra needed |
| User Experience | Satisfaction | ↑ qualitative | “Delighted” by personalized replies |
Memory in Action: How Users Feel Seen
When conversations have memory, they feel more personal, more continuous, and more human.
- The AI remembers context:“Last time, you mentioned after-work stress, how’s that going?”Users describe interactions as “comforting” and “surprisingly human.”
- Smart nudges drive daily engagement:“It’s been 7 days since your last craving journal, want to log one now?”Personalized prompts have boosted daily check-ins, engagement, and routine building.
- Long-term coaching evolves with the user:With memory, the AI can adapt CBT-based coaching, tailored to a user’s evolving goals, patterns, and relapse triggers.
Why This Matters
Sunflower’s success with Mem0 shows how AI agents with memory outperform generic chatbots in high-stakes use cases like addiction recovery:
- Better continuity over long-term conversations → deeper emotional trust
- Memory summarization → scalable compute cost
- Rapid integration → faster product iteration
As the Sunflower team puts it: "All of this user value, with negligible upfront engineering work (1 day of setup), has made adding memory one of the highest ROI decisions we've made."
Interested in giving your AI a real memory?
Try out Mem0: https://mem0.ai/