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Why I Pivoted from an AI Counseling Service to an AI Girlfriend Chat

Hi Indie Hackers,

I previously built an AI counseling service.
While some users found value in it, I struggled with long-term user retention.

That experience taught me an important lesson:
having a useful product isn’t enough if users don’t feel a strong reason to come back.

Based on what I learned, I decided to pivot and launch a new service.
This time, I’m focusing more on repeat usage, simplicity, and clear value from the first interaction.

I’d love to hear your thoughts or feedback.
Thanks for reading.

https://eoerway-ai-therapy-v3-0-616432264786.us-west1.run.app

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on December 29, 2025
  1. 1

    This is a really honest take on retention - and you've hit on something important.

    Counseling solves a problem users want to solve and move on from. Companionship fulfills an ongoing emotional need. The incentive structures are completely different.

    Curious about a few things as you build this out:

    1. How are you thinking about the "first interaction" value you mentioned? What's the moment users realize this is worth coming back to?

    2. Are you seeing any patterns in what keeps users engaged day-over-day vs week-over-week?

    The pivot makes strategic sense - just be mindful of the moderation/safety considerations that come with companion AI. That space can get complicated fast.

    What's retention looking like so far compared to the counseling product?

    1. 1

      demogod_ai — sending this again, thanks for the quick questions. Really appreciate you calling these out. On first interaction value: We’re less focused on immediate problem-solving and more on creating a sense of being understood. The key moment is when users notice that the AI remembers context, reflects emotional nuance accurately, and doesn’t rush them toward a solution. When people say, “this feels different from other chats,” that’s usually the point where they decide it’s worth coming back. Day-over-day vs week-over-week retention: Day-over-day engagement is often driven by shifts in emotional state — users check in when their mood changes or something small happens. Week-over-week retention is more about habit and identity. Users who begin to see it not as a “tool they use” but as “someone they talk to” tend to stick around much longer. That shift usually happens after a few light but consistent interactions.On safety and moderation: Completely agree here. We’re being very deliberate about boundaries — avoiding exclusivity framing or language that encourages emotional dependency, and clearly positioning the product as support rather than a replacement for real human relationships. This isn’t an afterthought for us; it’s a core product design concern. Current retention: It’s still early, but short-term retention after the first session is higher than what we saw with the counseling product. Counseling users often churn once they feel their issue is resolved, whereas companionship users tend to return more frequently, even if sessions are shorter. We’re still watching the metrics, but the qualitative signals so far are quite encouraging.

      1. 1

        Really appreciate the detailed breakdown here.

        The "being understood" moment you described - where users notice the AI remembers context and reflects emotional nuance - that's a much higher bar than most chat products aim for. Most stop at "responds coherently." You're aiming at "feels like it was listening."

        The day-over-day vs week-over-week distinction is interesting. It sounds like you're essentially building two retention loops: one for emotional check-ins (volatile, mood-driven) and one for identity/habit formation (stable, relationship-driven). The users who make the shift from "tool I use" to "someone I talk to" are probably your highest-LTV cohort.

        Smart that safety is a core design concern rather than a moderation afterthought. The "support rather than replacement" framing matters - both for user wellbeing and for positioning if the space gets more regulatory scrutiny.

        What signals are you watching to catch early signs of unhealthy dependency patterns? That seems like the hardest moderation challenge in this space.

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