can Indian AI be any different?

Every AI founder is racing to automate away human interaction. But what if the biggest opportunity is doing the exact opposite? Here's why I believe the next wave of Indian AI will be built on enhancing collective decision-making, not replacing it.

Why This Perspective Matters

My approach to investing in AI for Indian markets comes from a specific lens: understanding how people actually behave versus how technology assumes they should. Over the past decade, I've worked at the intersection of technology and human psychology across multiple contexts.

At Inclov, I built India's first matchmaking platform for people with disabilities. While our technology enabled accessibility features others couldn't offer, the real insight came from behavioral economics. We found that showing users just 5 profiles per day created more meaningful connections than offering unlimited choice. The constraint wasn't a limitation—it was the feature. This taught me that understanding user psychology often matters more than technical capabilities.
With Jupiter, I led product strategy during the pre-COVID era when opening a bank account required multiple branch visits and extensive paperwork. We achieved a 4-minute account opening from home, but the breakthrough wasn't just technological—it was redesigning the entire experience around how Indians actually wanted to interact with financial services.
From 2020-2023, I consulted across diverse sectors: women's safety apps, yoga and wellness platforms, fintech for blue-collar workers, and health insurance. Each project reinforced one core insight: products succeed when they align with how people actually think and behave, not how we assume they should.

The Behavioural Economics Blind Spot: Most Indian AI companies ignore this fundamental truth. For instance, I see AI financial advisors trying to make Indians track every rupee spent, completely missing that most Indian families manage money through informal (and emotional) consensus and trust rather than detailed spreadsheets.

This isn't purely theoretical. It's pattern recognition from working across 10+ product builds spanning healthcare, fintech, social platforms, and enterprise tools; experiencing firsthand how cultural context shapes product adoption in ways that pure technology cannot address.

The Problem Silicon Valley Won't Admit

Here's the uncomfortable truth: Western AI solutions fail in India not because of language or infrastructure, but because they're built on fundamentally incorrect assumptions about how decisions are made in India.

The Evidence:

According to McKinsey's 2024 AI Adoption Survey, "cultural resistance" ranks as the #2 barriers for AI implementation in Asian markets (after data quality concerns).
Research from IIM Bangalore's Family Business Center shows Indian family decisions typically involve multiple generations, with financial choices requiring consensus from 3-5 stakeholders.
A 2023 study by AIIMS Delhi found that 80%+ of patients combine allopathic treatment with traditional medicine (Ayurveda, home remedies, religious practices).
The National Sample Survey (NSSO) indicates that career decisions in Indian households involve family consultation in the majority of cases, particularly in smaller cities and rural areas.

Yet every AI product imported from Silicon Valley assumes individual decision-making. It's like trying to run relationship software on single-player architecture.

When Pure Automation Backfires: The Klarna Wake-Up Call

Just this month, we got a stark reminder of why the "automate everything" approach fails. Klarna CEO Sebastian Siemiatkowski, who spent 2023-2024 proudly replacing 700 customer service agents with AI chatbots, just announced they're hiring humans back.

His admission? The pursuit of AI-driven cost-cutting in customer service "has gone too far." Despite achieving significant cost savings, the AI-only approach resulted in "lower quality" customer experiences. Now Klarna is ensuring customers "will always have a human if you want."

This isn't just a Silicon Valley anecdote—it's validation of a fundamental truth about human interaction. Even in a relatively simple domain like purchase inquiries and refunds, complete automation failed to meet human needs. Now imagine applying this automation-first mindset to complex Indian family decisions about marriage, healthcare, or ancestral property.

If Klarna—a tech-forward financial company—had to backtrack on pure AI automation for simple customer service, what does this tell us about building AI for India's complex social and familial contexts?

The Core Opportunity: Insights into Distributed Family Algorithms

Let's be honest: if you're like me, family discussions aren't just complex algorithms—they're often emotionally overwhelming. The stress of navigating unspoken expectations, generational gaps, and conflicting priorities can be exhausting. If this resonates with you, then you understand why simply automating these conversations misses the point entirely.

Every Indian family gathering is essentially a sophisticated algorithm that has been optimised over centuries:

Multiple stakeholders with different priorities
Consensus-building protocols refined through generations
Risk assessment that combines data with intuition
Decision implementation that requires collective buy-in

The opportunity isn't to replace this algorithm. It's to enhance it with modern AI capabilities.

Think about it: When a family decides whether a daughter should pursue MBA or wedding, they're running a complex optimisation across multiple variables:

Financial impact on household
Social status implications
Individual happiness metrics
Long-term family sustainability
Regional cultural expectations

Traditional AI tries to simplify this into individual optimisation. Indian AI opportunity lies in orchestrating these complex multi-party decisions.

Now that we understand how Indian families naturally operate as sophisticated decision-making algorithms, the question becomes: how do we build AI systems that can recognise, respect, and enhance these patterns rather than override them? This requires a fundamental rethinking of AI architecture from the ground up.

The Technical Architecture for Cultural AI

Having established the problem, here's how we build the solution. The technology stack for Indian AI requires three key layers:

1. Multi-Modal Cultural Processing

Voice tone analysis across 8+ Indian languages (recognizing respectful disagreement vs. confrontation)
Computer vision trained on Indian gestures (understanding non-verbal hierarchy indicators)
Context engines that factor in festivals, regional customs, and family traditions:

2. Distributed Decision Orchestration

NLP models that translate complex emotional statements across languages and generations
Knowledge graphs mapping family financial structures (HUFs, joint properties, informal lending)
Conversation flow AI that respects cultural protocols (who speaks when, how dissent is expressed)

3. Transparent Mediation Layer

Clear attribution when AI suggests compromises vs. data-driven recommendations
Cultural sensitivity scoring that prevents AI from violating tradition
Success metrics aligned with Indian values (family harmony, not just individual optimisation)

This isn't theoretical. These capabilities exist today. The challenge is combining them with deep cultural understanding.

The Business Model: Success-Based Pricing

Here's where Indian AI diverges radically from Western models. Instead of subscription or usage-based pricing, we could charge based on successful outcomes/engagements:

Framework applies across all verticals:

₹10,000 when family reaches consensus on major decisions (marriage, property, education)
₹15,000 for 3-month health journey coordination (diagnosis through traditional recovery)
₹5,000 for successfully resolved business negotiations while maintaining healthy relationships

Unit Economics Example:

100 successful family decisions daily × ₹10,000 = ₹10 lakh revenue
Scale to 10 cities × 5 major life categories = ₹50 lakh daily potential
Monthly recurring: ₹15 crores per geographic market

The model works because families pay for value, not time. This aligns AI incentives with human outcomes.

Other Potential Areas?

Here are some paper-napkin-level thought experiments on potential opportunity areas based on similar cultural insights (may explore in more depth in future editions).

1. Family Decision AI - Beyond Matchmaking

Scenario: Marriage Negotiation in Bangalore

Priya, a software engineer, receives a marriage proposal. Instead of awkward family meetings with unspoken expectations, the family uses FamilySync:

AI schedules structured video calls with agenda creation
Cultural translation: "Simple wedding celebration" in Tamil context = ₹15-25 lakhs
Real-time sentiment analysis prevents conversations from escalating
Outcome: Both families align on realistic expectations before emotional investment

Technical Innovation: Multi-generational voice analysis trained on 50,000+ family conversations

2. Holistic Healthcare Stack - The Great Integration

Thought Experiment: When Ravi Gets Diabetes in Lucknow

Reality in Indian healthcare:

Endocrinologist prescribes Metformin
Aunt insists on karela juice
Mother wants astrological timing for treatment
Wife finds yoga teacher specializing in diabetes

Western AI: Order XYZ (allopathy) medicine (wrong answer for Indian context)
Indian AI: Orchestrate all four approaches

The Indian AI platform integrates:

Medical apps with ayurvedic databases
Yoga scheduling with blood sugar monitoring
Family meal (Indian kitchens do not make different meals for every member) planning with regional preferences
Cultural timing preferences with medical protocols

Revenue Model: ₹15,000 for 3-month holistic care journey

3. Career Counselling + Family Dynamics

Delhi Scenario: Arjun's Filmmaker Dreams

22-year-old Arjun wants to pursue filmmaking. Father owns a textile business in Chandni Chowk. Traditional tension: "Beta, camera se ghar nahi chalega."

AI creates structured dialogue:

Presents OTT industry growth (₹5,000 crores market)
Models hybrid scenarios: 3 days in family business, 4 days on film projects
References successful examples from similar families
Facilitates gradual transition with safety nets

Key Technical Feature: Dynamic conversation trees that adapt based on family power dynamics and cultural context

4. Legal Translation for Informal Economies

93% of Indians work in the informal economy. Legal AI needs to translate complex agreements into:

Plain Hindi/regional languages
Cultural context (what "adjustments" really mean)
Power dynamics (landlord-tenant relationships)
Family implications (how decisions affect extended family)

5. Small Business Relationship Networks

Mumbai Diamond Trading Example

Gujarati merchant making sourcing decisions considers:

Market rates and trends
Family relationships with suppliers
Astrological timing preferences
Community reputation impacts

AI enhances (doesn't replace) these considerations:

"Mehta ji's rates are competitive, and your Jupiter transit suggests Thursday would be auspicious for placing this huge risky order" (I know this sounds unusual, but I've literally seen my uncles do this since time immemorial—we're Gujaratis! 🙂)
Optimises relationship-based business without formalising everything

Why This Works: The Behavioural Economics

Most AI products fail behavioural economics 101. They assume rational actors making individual decisions.

Indian family AI succeeds because it:

Works with existing social structures instead of trying to change them
Leverages loss aversion (family harmony > individual optimisation)
Uses social proof from similar family decisions
Respects cognitive bandwidth by limiting choices thoughtfully
Aligns incentives with collective rather than individual success

Bootstrap-Friendly or Building With VC Capital?

Not every founder needs venture capital to build these solutions. In fact, the AI democratisation wave makes many of these ideas perfect for bootstrap ventures.

Today's AI Stack Reduces Building Costs By 90% (ballpark tentative numbers):

Foundation Models: OpenAI API costs ₹1.50 per 1,000 tokens (conversations)
Voice Processing: Whisper API for ₹0.006 per minute of audio
Custom Training: Fine-tuning GPT-3.5 costs ₹8 per 1,000 training examples
Deployment: Vercel/Netlify for ₹0 initial hosting

Let's elaborate this with an example.

Bootstrap Journey - FamilyDecision AI:

Your personal Family Decision Mediator that:

Schedules structured conversations between family members
Translates cultural contexts
Analyses sentiment to prevent conversations from escalating
Surfaces unspoken concerns through guided questions
Helps families reach consensus while respecting hierarchies

Your Cultural AI Stack:

Top Layer: Indian Cultural Logic Layer (You build this)

Indian emotion + language detection
Family hierarchy protocols
Regional gesture recognition [optional]

Middle Layer: Application Orchestration (You control this)

Conversation flow management
Cultural context injection
Success metrics definition

Bottom Layer: OpenAI API (Foundation Model) [Optimise using Silicon Valley models]

Language understanding
Text generation
Basic reasoning

This isn't about building another chatbot with Indian language support - it's about creating AI that reads the emotional undercurrents of Indian family decision-making.

We're exploring building systems sophisticated enough to recognise when a grandfather's silence signals disapproval, when a mother's 'whatever you decide' carries years of suppressed opinions, and when a young man's enthusiasm masks deep financial anxiety.

The real technical challenge isn't translating Hindi to English; it's translating emotional and cultural nuances across generations, regions, and value systems - an incredibly complex problem made harder by the multi-generational gaps that define Indian families.

Real-World Example:

Without Application Orchestration (Chaos):

Six family members talking simultaneously on WhatsApp
Budget discussions mixing with astrological concerns
Decisions made but no one remembers who's doing what
Grandfather's approval assumed but never explicitly stated

With Application Orchestration (Organized):

AI asks: "Before we discuss venues, can Dada ji share his thoughts on the timing?"
AI notes: "I see concern about budget - in similar Tamil families, here's how they typically handled this..."
AI tracks: "We've agreed on ₹8 lakh budget. Next step: Priya's mother will check three venues by Sunday."
AI confirms: "Dada ji, can you confirm your final decision for the December muhurat?"

The core value proposition is in building a culturally intelligent moderator that understands:

How Indian families actually make decisions
What information needs to be added when
How to measure real progress vs. just talk

This could be our competitive advantage over Silicon Valley solutions that have been trained and modelled to treat every conversation the same way.

Tentative roadmap (seriously, steal this idea!)

Month 1-3: MVP with ₹50,000 investment

Build on Next.js + OpenAI API
Create simple WhatsApp bot using Twilio
Test with 10 family decisions manually

Month 4-6: First revenue with ₹200 conversation

20 successful decisions = ₹2 lakh revenue
Covers API costs + basic operations
Start building customer base

Month 7-12: Scale to ₹10 lakh ARR

100 families using service
₹10,000 success rate = 10% take rate
Profit margins of 60%+ due to low infrastructure costs

Many founders assume AI apps require massive capital (AI models need massive investment but AI apps using the silicon valley models as foundation do not need massive capital investment - wrapper theory).

Infact, these family-decision platforms can become ₹100 crore businesses while remaining profitable throughout their journey. The key is starting with a specific cultural context or one problem statement or category (career, marriage, travel) and expanding slowly.

Investment Thesis Summary

The Arbitrage Opportunity: While Silicon Valley builds AI to eliminate human interaction, there's massive unmet demand for AI that enhances complex human coordination. Indian family structures create the perfect testbed for this approach.

Competitive Moat:

(Unique) Cultural data advantage (Indian families using Indian AI platforms)
Technical complexity (multimodal, multi-lingual, multi-generational AI)
Network effects (family platforms grow through family networks)
Success-based pricing creates sticky relationships

I'm actively seeking to collaborate/invest with/in founders who understand this paradox: The most valuable AI products help humans think better together, not think less or alone.

Whether you're building with external capital or bootstrapping with today's democratised AI tools, if you're creating AI that:

Has clear philosophy about human enhancement vs. replacement
Uses thoughtful constraints to create more value
Measures family/community development, not just individual metrics
Specifically addresses Indian cultural contexts
Understands behavioural economics at a fundamental level

I am interested in talking to you. Please reach me at santimstudio@gmail.com

The Meta Question

Every family gathering is a distributed algorithm refined over millennia. The question isn't whether AI can improve these algorithms. The question is whether we're building AI that respects and enhances this collective wisdom, or importing Silicon Valley's obsession with individual optimisation.

I'm betting on the former. The founders who understand this—whether VC-backed or bootstrap-funded—will build the next generation of Indian AI.

This article focuses on family dynamics a lot more but the same cultural adaptation principles apply across healthcare, education, legal services, and small business operations in India. Each deserves deeper exploration - if you're building in any of these spaces or want me to analyse specific verticals, I'd love to hear your feedback.

Some ideas on my radar right now:

Healthcare navigation (integrating modern medicine with traditional wellness)
Career counselling that respects family expectations and market economics
Legal infrastructure for informal economies
Small business relationship management (paying off informal loans, credit request etc from other business owners, joint family discussions on business growtH)
Education guidance for the MIGHTY middle class India

Each represents a unique application of culturally-informed AI that enhances rather than replaces human interaction. I'm considering exploring these in future editions - let me know which resonates most with your experience. Your responses will shape which cultural AI opportunities I explore next.

These insights emerge from my newsletter research in The Third Frontier, which explores the intersection of AI and human potential. If this perspective resonates with you, you’ll want to follow along.

Quick reads:

Signing off,

Kalyani Khona

the third frontier