Jivana
Built a scalable AI-native platform that captures memories, voice, personality, and life experiences to create conversational digital avatars families can interact with forever — across 11 languages and multiple AI models.
80%
Reduction in time to create a usable AI avatar
11
Languages supported for voice and chat
4×
Increase in story depth via AI-guided capture
Client Overview
Industry
Consumer AI · Digital Legacy & Storytelling
Core product
AI-powered platform for preserving memories, stories, personality, and voice through conversational digital avatars
Company type
Early-stage startup → Scaling SaaS
Audience
Families, seniors, veterans, professionals, and individuals preserving life stories for future generations
Digital Legacy Platform Overview
Challenges
Jivana set out to solve a deeply emotional problem: preserving a person's stories, personality, and voice in a way future generations could actually interact with. But turning that vision into a scalable AI product introduced significant technical and operational complexity.
Traditional memory preservation was fragmented
User memories existed across scattered documents, PDFs, voice recordings, journals, videos, and chats. There was no unified system capable of converting fragmented life experiences into structured AI memory.
Writing memoirs felt overwhelming
Most people never finish documenting their life stories because the process feels intimidating and time-consuming. The platform needed to make storytelling conversational instead of manual.
Generic AI chatbots lacked personality
Standard AI models could answer questions, but they couldn't speak with a user's emotional tone, values, communication style, or lived experiences. Jivana required deeply personalized avatar generation.
Long-running AI workloads risked poor UX
Avatar training, biography generation, and voice synthesis could take over a minute to process. Running these tasks synchronously would create lag, timeouts, and broken user experiences.
Multi-provider AI orchestration was required
Relying on a single LLM provider would create scalability, reliability, and cost challenges. The platform needed intelligent routing across OpenAI, Claude, Gemini, and Mistral models.
Family access and privacy added complexity
Users needed secure family sharing, role-based permissions, invite systems, and privacy controls to safely share their digital avatar with selected family members.
Subscription and credits management needed production-grade reliability
Trials, subscriptions, pending upgrades, credits, Stripe webhooks, and recurring billing flows all had to work seamlessly without operational overhead.
Solution
Advant AI Labs engineered a full-scale AI-native digital legacy platform designed around conversational storytelling, asynchronous AI processing, and personalized avatar intelligence.
Conversational AI memory capture
Instead of asking users to manually write memoirs, the platform guides them through structured conversations across 21 life categories including childhood, family, relationships, spirituality, career, health, and major life experiences. An AI legacy coach dynamically asks follow-up questions to help users naturally expand their stories through conversation.
Multi-LLM AI architecture
Built a unified multi-provider AI layer using the Vercel AI SDK, enabling seamless routing between OpenAI GPT-4o, Claude Sonnet 4.5, Google Gemini, and Mistral models. This architecture allowed intelligent cost optimization while maintaining high-quality persona generation and conversational performance.
Event-driven AI infrastructure
Heavy AI operations including avatar training, biography generation, TTS rendering, and email campaigns were moved into an asynchronous Google Cloud Pub/Sub worker pipeline. This ensured the frontend experience remained fast and responsive even during long-running AI operations.
Full-stack Next.js and Firebase ecosystem
The platform was built using Next.js 15 App Router and React 19 with Firebase Auth, Firestore, and Storage powering realtime data synchronization, authentication, multimedia storage, and RBAC workflows.
Voice AI and multilingual support
Integrated ElevenLabs voice synthesis and browser-native speech recognition APIs to enable natural voice interactions and multilingual conversations across 11 supported languages.
Infrastructure designed for scale
Terraform-based infrastructure, Dockerized workers, GitHub Actions deployment pipelines, Sentry observability, and production-grade monitoring ensured scalability, reliability, and operational visibility from day one.
Platform Architecture
Full-stack AI-native platform built on Next.js 15, Firebase, multi-LLM orchestration, event-driven workers, and production-grade infrastructure.
Frontend
- Next.js 15
- React 19
- TypeScript
- TailwindCSS
Backend
- Next.js API Routes
- Node.js Workers
AI/ML
- OpenAI
- Claude
- Gemini
- Mistral
- LangChain
- LangGraph
Voice AI
- ElevenLabs TTS
- Web Speech API
Database
- Firebase Firestore
- Firebase Storage
Messaging
- Google Cloud Pub/Sub
Payments
- Stripe Subscriptions + Credits
Infrastructure
- Terraform
- Docker
- GitHub Actions
Observability
- Sentry
- Admin analytics dashboards
Business Impact
The platform transformed a founder-led idea into a scalable, monetizable AI SaaS platform designed for long-term growth.
Faster avatar creation
Users can now generate a conversational AI avatar within a single guided session instead of spending months documenting their life story manually.
Richer AI personalities and conversations
The combination of surveys, recordings, journals, uploaded documents, and conversational coaching created significantly deeper and more emotionally accurate AI personas.
Zero UI blocking during AI processing
Asynchronous worker infrastructure ensured that heavy AI tasks never interrupted the user experience, even during long-running operations.
Lower AI operating costs
Multi-provider routing reduced overall AI costs by 30–50% through intelligent model allocation across workloads.
Global-ready multilingual experience
Support for 11 languages enabled expansion into international markets without requiring separate platform implementations.
Production-grade recurring revenue system
Stripe-powered subscriptions, upgrades, credits, and billing automation enabled reliable SaaS monetization from launch.
Investor-ready technical foundation
The architecture combined multi-LLM orchestration, event-driven infrastructure, observability, and infrastructure-as-code into a scalable and defensible platform foundation.
“"Jivana became more than a memory platform — it became a way for families to preserve identity, emotion, and wisdom across generations. Advant AI Labs gave us the technical foundation to make that vision scalable." — Founding Team, Jivana
FAQ
Frequently Asked Questions