KAIRO

KAIRO is a scalable knowledge engine built on top of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG). The goal is to enable enterprises and developers to query large-scale private or domain-specific data efficiently, with production-level scalability, modularity, and performance.

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Core Problem

LLMs are powerful but limited by their training cutoffs and lack of access to private or domain-specific data. Enterprises require systems that:

1. Incorporate proprietary knowledge.
2. Answer queries with factual grounding.
3. Scale to large datasets and high concurrency.
4. Offer an API-first architecture for integration.

KAIRO addresses this by combining LLMs with high-performance retrieval pipelines and an extensible backend architecture.

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System Overview

Architecture (MVP → Scale):

1. Ingestion Layer

   • Document loaders for PDFs, DOCX, HTML, Markdown, plain text.
   • Preprocessing: text normalization, chunking, sentence splitting.
   • Embeddings generated using transformer-based models (e.g., InstructorXL, OpenAI embeddings).

2. Vector Indexing

   • Phase 1 (MVP): FAISS (local, in-memory / persisted with disk serialization).
   • Phase 2: Cloud vector DB (Pinecone/Qdrant) for persistence and scalability.

3. Retrieval Layer

   • Semantic search with top-k retrieval.
   • Hybrid retrieval (BM25 + dense embeddings) for higher recall.

4. LLM Generation Layer

   • Prompt orchestration with context injection.
   • Configurable LLM backend: OpenAI GPT, LLaMA, Mistral, or fine-tuned domain-specific models.
   • Chain of Thought / ReAct prompting for reasoning tasks.

5. API Layer

   • Exposed via FastAPI (REST initially, later gRPC for high throughput).
   • Endpoints: /ingest, /ask, /health.
   • Rate-limiting and auth (JWT/OAuth2).

6. Frontend (minimal)

   • Next.js + TailwindCSS basic interface.
   • Single chat UI to query backend.
   • Deployed separately for modular scaling.

7. Scaling Components

   • Deployment via Docker + Kubernetes.
   • Load balancing with NGINX/Envoy.
   • Monitoring with Prometheus + Grafana.
   • Caching layer (Redis) for repeated queries.

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Technical Roadmap

Phase 0 — Specification

• Define core product scope.
• Create GitHub repo, README, architecture diagram.

Phase 1 — MVP (4–6 weeks, solo dev)

• Backend: FastAPI, FAISS, OpenAI embeddings, OpenAI GPT-4/3.5.
• Basic ingestion + retrieval + query pipeline.
• One REST endpoint (/ask).
• Minimal frontend with chat interface.
• Deploy backend (Render/Fly.io) and frontend (Vercel).

Phase 2 — Scaling (2–3 months)

• Swap FAISS → Pinecone/Qdrant (managed).
• Add user auth, sessions, and persistence.
• Introduce hybrid retrieval (BM25 + dense).
• Add monitoring and basic analytics.
• Optimize inference with quantized or fine-tuned models.

Phase 3 — Production (3–6 months)

• Kubernetes-based deployment.
• API versioning and multi-tenant support.
• Redis caching layer.
• Horizontal scaling to 10k+ concurrent users.
• Fine-tuned domain-specific models.
• Potential SaaS release with billing + admin dashboard.

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Tech Stack

• Backend: FastAPI, Python 3.11, LangChain for orchestration.
• Vector Indexing: FAISS (MVP), Pinecone/Qdrant (scale).
• Embeddings: OpenAI embeddings / InstructorXL.
• LLM: OpenAI GPT-4, LLaMA 2, or fine-tuned models.
• Frontend: Next.js, TailwindCSS, shadcn.
• Infra: Docker, Kubernetes, AWS/GCP/Azure.
• Monitoring: Prometheus, Grafana.
• Database (optional): Postgres for metadata and user data.

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Deployment Strategy

• MVP: Monolithic deployment (FastAPI + FAISS in one container).

• Scale: Microservices:

  • Ingestion service.
  • Retrieval service.
  • LLM inference service.
  • API gateway.

• Horizontal scaling via Kubernetes.

• Autoscaling rules based on CPU/GPU utilization.

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Cost Estimate

• MVP (FAISS + OpenAI API + cheap hosting): $50–100/month.
• Early scale (vector DB + monitoring + infra): $200–350/month.
• Production scale (multi-region, GPU-backed inference): $2k–5k/month+.

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Differentiation

• Modular design (swap any component: embeddings, vector DB, LLM).
• Enterprise-ready architecture (auth, monitoring, scaling).
• Designed for both recruiter shock value and commercial viability.
• Competitive against existing RAG solutions due to focus on scalability + extensibility.

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Current Status

• Phase 0 (specification) → in progress.
• Repository bootstrapped with README and architecture plan.
• Backend prototyping next.

KAIRO is not a static demo but a long-term, production-heavy system designed to evolve into a full-fledged SaaS product.