But outside a handful of breakout apps, retention is weak, monetisation is shallow, and most products still feel like curiosities.

The reason is not model quality. It is that most consumer AI products do not compound. They generate, but do not remember. They answer, but do not act. They personalise, but only at the surface. And they rarely ship the trust rails that make users confident putting AI at the centre of their lives.

To move from hype to habit, the next wave has to shift from “what can the model do” to “what behaviours can the product sustain.” For that, you need better systems: memory that carries forward, retrieval that is precise, actions that close loops, and trust that is visible.

Behaviour before category

Instead of slicing GenAI by sector (edtech, healthtech, productivity), start with what people actually do. Across markets, five behavioural archetypes are emerging.

Across markets, five behavioural archetypes are emerging.

Companionship: continuous presence

The stickiest products feel like they’re there for you, not just responding to prompts. Emotional responsiveness, cultural fluency, and persistence create attachment loops. Wysa’s mental health companion keeps users coming back because it remembers past sessions and adapts tone. Replika builds continuity by referencing shared history. In India, Astrotalk has turned astrology into a chat-based companion, with Hindi-first users spending noticeably longer in sessions than English-first users.

Creative and intellectual partnership: extending output

Tools that accelerate and enrich the creative process turn into collaborators. Jasper and Runway compress the path from idea to output. In education, Supernova’s AI tutor increases lesson completion by tailoring content in Hindi-English rather than English-only. In one travel-planning startup, simply remembering a user’s last constraints improved re-engagement more than upgrading to a newer LLM.

Interaction and immersion: worlds that react When environments evolve with the user, engagement deepens. Dream11’s adaptive cricket trivia increases streak completions when difficulty adjusts to player history. In stealth AI-social apps, users who create an AI “friend” early stay longer and return more often. The magic isn’t infinite content - it’s pacing, progression, and the sense that the world changes because of you.

Personalisation: retrieval-first identity Moving beyond recommendations, retrieval-first personalisation taps into a user’s own data. Flipkart’s AI fashion engine uses a shopper’s gallery to suggest colours that fit their wardrobe. Legal bots that mix Hindi with English terms see fewer repeated queries because the language matches how users actually think. The key is transparency: showing what’s being used and letting people revoke it.

Discovery: answer plus action Search is shifting from finding information to resolving intent in one surface. Perplexity delivers conversational travel answers that flow directly into booking. In tier-2 commerce, voice-led product search in Hindi doubles checkout rates compared to typed English. The most effective designs collapse “decide” and “do” into the same moment, turning answers into transactions.

The India testbed

India is the best place to stress-test consumer GenAI:

• Vernacular and multimodal UX are not optional, they are table stakes.
• Price sensitivity forces efficient inference and GPU-light design that travels globally.
• Commerce rails (UPI, ONDC) make “answer to action to payment” native.
• Cultural niches (matchmaking, faith, cricket) are defensible wedges.

Memory as the multiplier

Without memory, you are stuck in novelty. With it, every interaction compounds.

Right now:

• Session memory is solved; context windows are long enough.
• Persistent memory is shallow; tone and preferences are not enough.
  Action memory is emerging; LangGraph and AutoGen can track what agents do.
  Shared memory (MCP, A2A) is fragile; permissioning breaks easily.
• Memory UX is missing entirely; no consumer app lets you view, edit, or expire what AI remembers.

Builders who get memory right-vault APIs with consent and TTL, drift-tested personalisation, policy wrappers that make trust a feature- will win on retention, recall, and recommendation all at once.

From hype to habit

The first generation of consumer GenAI proved people will talk to machines, create with them, and even form attachments. The second generation will decide if those interactions become daily habits.

The winners will:

• Close loops: answer, then act.
• Compound context: remember, retrieve, adapt.
• Earn trust: make memory visible and editable.
• Fit the fabric: speak the language, use the rails, respect the norms.

India will produce many of them. The constraints here create better systems. And the systems that work here will travel.

If you are building AI-native, memory-compounding, trust-first consumer products, especially in India, we want the first call.
→ build@boundlessvc.com