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Understand your business needs, goals, and challenges.
Qiri AI is an AI Knowledge Twin platform built on Next.js — allowing professionals to create AI-powered knowledge bases that answer questions in their voice, built with OpenAI and Claude RAG architecture, Supabase backend, and multi-tenant knowledge isolation.
Qiri AI is a platform enabling experts and teams to create AI Knowledge Twins — AI representations of their expertise that answer questions in their voice. The platform makes expert knowledge accessible at scale without requiring the expert to be personally available for every query.
The project required a full-stack Next.js SaaS application with RAG-based document ingestion, multi-model AI integration (OpenAI and Claude), a conversational interface, subscription management, and a knowledge base management dashboard.
The completed Qiri AI platform is one of our most sophisticated AI product builds — combining document-grounded RAG with conversational AI in a production SaaS architecture.
An overview of the industry, core services, platform integrations, and technology stack powering this solution.
Solving key business challenges to deliver a reliable, user-friendly, and high-performing digital solution.
Grounding responses in the expert's actual content required a complete RAG pipeline: document ingestion, chunking, embedding generation, pgvector storage, and similarity retrieval per query.
Different query types perform better with different models, so we built a routing layer directing queries to GPT-4o or Claude based on query type, context length, and quality requirements.
A knowledge twin must respond in the expert's communication style, so we built a voice calibration system using examples of their writing to condition AI response style.
Experts needed an intuitive interface to upload documents, test responses, and update their twin's knowledge, so we built a full knowledge management dashboard.
Each expert's knowledge base must be completely isolated, so we implemented Supabase Row Level Security with Pinecone namespace isolation for complete data separation.
Multi-turn conversations require maintaining context without excessive token costs, so we implemented rolling window compression for coherence at predictable API costs.
We combine innovation, expertise, and collaboration to deliver tailored solutions that help businesses achieve their goals. Our structured approach ensures quality, transparency, and measurable results at every stage.
Understand your business needs, goals, and challenges.
Create a customized strategy and project roadmap.
Develop user-focused and innovative solution concepts.
Implement and develop the solution using best practices.
Ensure quality, performance, and reliability through rigorous testing.
Launch the solution smoothly and efficiently. Test, validate, and go live with confidence. Deliver a stable user experience.
Provide ongoing maintenance, optimization, and growth support.
An overview of the advanced features and components integrated into this digital solution.
Document ingestion, chunking, OpenAI embedding, and pgvector storage — grounding every Knowledge Twin response in the expert's actual content.
Intelligent routing between GPT-4o and Claude — optimising response quality and cost across different interaction patterns.
Style conditioning using examples of the expert's writing — ensuring the knowledge twin responds as the expert would.
Expert-facing interface for document upload, knowledge review, response testing, and knowledge base versioning.
Supabase RLS and Pinecone namespace isolation ensuring complete separation between expert knowledge bases.
Rolling window conversation context maintaining coherence across multi-turn interactions at predictable API costs.
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