AI Chatbot Platform

Production AI chatbot with LLM integration, RAG architecture, and real-time streaming. Handles thousands of customer queries daily with 95%+ accuracy.

LLM • RAG • Production AI
AI Chatbot Platform
AI LLMRAGProduction AI

Case study

AI Chatbot Platform

Production AI chatbot with LLM integration, RAG architecture, and real-time streaming. Handles thousands of customer queries daily with 95%+ accuracy.

Production AI chatbot platform with LLM integration, RAG architecture, and real-time streaming serving thousands of queries daily.

The Challenge

The client needed an intelligent customer support system that could handle complex queries with high accuracy, integrate with their existing knowledge base, and scale to thousands of concurrent users without degradation.

The Solution

We built a production-grade AI chatbot with retrieval-augmented generation (RAG), real-time streaming responses, and a custom fine-tuning pipeline. The system integrates with multiple LLM providers and includes admin tooling for managing knowledge bases and monitoring conversation quality.

Key results

95%+
Query accuracy

Measured against human baseline

<2s
Response time

P95 including retrieval + generation

5K+
Daily queries

With auto-scaling infrastructure

Stack

PythonFastAPILangChainPostgreSQLRedisTypeScriptDocker

Timeline

  • Week 1

    Architecture design, LLM evaluation, and RAG pipeline prototyping

  • Week 2–4

    Core chatbot engine, embedding pipeline, and streaming API

  • Week 5–6

    Admin dashboard, monitoring, and production deployment

“The chatbot handles 80% of our support tickets autonomously—it paid for itself in the first month.”

Project Details

Technical implementation and architecture overview

RAG-powered knowledge retrieval

Custom embedding pipeline with semantic search across structured and unstructured data. Automatic chunk optimization and relevance scoring ensure accurate, grounded responses.

Real-time streaming architecture

FastAPI backend with WebSocket streaming delivers token-by-token responses. Built-in fallback chains, rate limiting, and conversation memory for contextual multi-turn interactions.

Admin tooling + analytics

TypeScript dashboard for managing knowledge bases, reviewing conversations, and tracking accuracy metrics. Automated quality scoring flags conversations that need human review.

FAQs

How much does it cost? +

Complete projects start at $2,000. We give you a fixed price after a free 30-minute call — no hourly billing, no surprise invoices.

How fast can you deliver? +

Most MVPs go live in 2–4 weeks. Larger projects run in 2-week sprints with a live demo every week so you always see progress.

Who will actually build my product? +

Senior engineers with 10+ years each. No juniors, no interns, no outsourcing. The people on the call are the people writing the code.

What makes you different from agencies? +

No overhead. You talk directly to engineers — not account managers or project managers. That means faster decisions, better output, and lower cost.

What if I need changes after launch? +

You get 30 days of free post-launch support included. After that, we offer affordable monthly retainers or project-based work.

What technologies do you use? +

TypeScript, React, FastAPI, Node.js, Python, Rust, Solidity, React Native, Flutter — we pick what fits your project, not what's trendy.

Can a small team really handle everything? +

Yes. Frontend, backend, infrastructure, mobile, and Web3. Senior specialists with the right tools consistently outperform teams 10x their size.

What if my project doesn't work out? +

You own everything we build — every line of code, every design file. If you want to walk away or bring in another team, you can. No lock-in.