AI Agent & RAG Development Company — LangChain, OpenAI & Claude Experts in India
TechEinHub builds AI agents that know your business — RAG chatbots trained on your documents, LLM-powered automation workflows, and intelligent assistants using OpenAI GPT-4, Claude, and Gemini. We build faster using Cursor AI, GitHub Copilot, and Claude Code — so you get production-ready AI systems in weeks, not months. Based in Ahmedabad, Gujarat, India, serving clients across USA, UK, UAE, and Australia.
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AI Products We Build
From RAG chatbots to multi-step AI agents — we build production AI systems, not demos.
RAG Chatbots
Teach an AI your own documents, PDFs, knowledge base, and policies. Ask it anything — it answers from your data, not the internet. Built with LangChain, LlamaIndex, and vector databases like Pinecone or ChromaDB.
AI Agents
Multi-step agents that can browse the web, query your database, send emails, create tickets, and take actions based on conditions — all orchestrated with LangChain Agents or LangGraph for complex workflows.
Document Analysis
Automatically extract, classify, and summarise information from contracts, invoices, medical records, and legal documents. Structured output delivered to your backend via API at scale.
Customer Support Bots
AI-powered support that handles 80% of queries automatically — trained on your product docs, FAQs, and past tickets. Escalates to human agents when needed. Integrates with WhatsApp, website chat, and Slack.
Internal Knowledge Base AI
Give your employees an AI that knows your entire company wiki, HR policies, SOPs, and product specs. Ask questions in plain English — get instant, accurate answers from your own knowledge base.
LLM Automation Workflows
Connect LLMs to your existing tools — CRM, ERP, email, Slack, WhatsApp, databases. Build intelligent automation pipelines that understand natural language inputs and trigger the right actions.
Technologies We Work With
The full AI/LLM stack — from model API to vector database to production deployment.
Industry Use Cases
AI agents solving real business problems across manufacturing, healthcare, retail, finance, and more.
Manufacturing & Factory
Query machinery maintenance manuals. AI checks fault codes against SOPs and suggests fixes. Factory floor teams get instant answers without calling the engineer. Links to our factory management system.
Healthcare
Medical record summarisation, drug interaction checking, and patient FAQ bots trained on hospital policies. DPDP-compliant data handling for sensitive health information.
E-Commerce & Retail
AI shopping assistants that recommend products based on conversational input. Automated returns and order query resolution. Trained on your product catalogue for accurate recommendations.
Finance & Accounting
Document AI for invoice processing, contract analysis, and financial report summarisation. Extracts structured data from unstructured PDFs at scale. Integrates with your accounting ERP.
EdTech
Personalised AI tutors that answer curriculum questions, grade assignments, and generate practice problems — trained on your course content. Scalable to thousands of students.
Enterprise Internal Tools
Company-wide AI assistant connected to Confluence, Notion, Google Drive, Slack — answers questions from your entire knowledge base instantly. No more digging through folders.
We Build AI Faster — Because We Use AI
Our entire development workflow is built around AI tools. That saving gets passed directly to you.
How We Build 40% Faster
Unlike most agencies that treat AI tools as a novelty, our entire development workflow is built around them. Cursor AI for intelligent code completion, GitHub Copilot for boilerplate generation, and Claude Code for architecture review and debugging — these aren't extras, they're our standard operating procedure. The result: you get production-grade AI systems delivered in 6–12 weeks instead of 4–6 months. Every hour saved is passed to you as lower cost and faster delivery.
faster delivery vs traditional agencies
typical timeline for production RAG system
GPT-4, Claude & Gemini expertise
apps shipped to production
Also explore our AI Development and Python/Django Development services, or Cloud & DevOps to host your AI system.
Development Investment
Fixed-contract pricing. Architecture design, vector database setup, and deployment included at every tier.
Starter
- RAG chatbot on 1 document source
- OpenAI / Claude integration
- REST API backend
- Basic web chat UI
- Admin panel to update docs
- 3 months post-launch support
Standard
- Everything in Starter
- Multi-source RAG (PDF, DB, web)
- AI agents with tool use
- LangGraph workflow orchestration
- Custom frontend (React / Flutter)
- Analytics dashboard
- 6 months support
Enterprise
- Everything in Standard
- Multi-agent systems
- CRM / ERP integration
- On-premise LLM deployment
- Fine-tuning on domain data
- SOC2-ready data handling
- 12 months managed support
All prices are fixed-contract. Hosting and LLM API costs are quoted separately based on usage.
Get a Custom QuoteFrequently Asked Questions
Questions clients ask us most often before starting an AI agent project.
Can the AI be trained on my own documents?
Yes. We build RAG (Retrieval-Augmented Generation) systems that index your documents, PDFs, websites, databases, and APIs into a vector store. When a user asks a question, the system retrieves the most relevant content from your data and feeds it to the LLM as context — so the AI answers from your knowledge, not general training data. Documents can be updated and the AI stays current without retraining.
Is my business data safe?
Yes. Your documents never leave your infrastructure unless you choose a hosted vector database. We can deploy the entire RAG pipeline on your AWS or Google Cloud account — no data passes through any third party except the LLM API you choose (OpenAI, Anthropic, or Google). For sensitive industries like healthcare or government, we support on-premise LLM deployment using open-source models like LLaMA so no data leaves your servers.
What is the difference between a RAG chatbot and a fine-tuned model?
RAG (retrieval-augmented generation) retrieves fresh information from your documents at query time — it's updatable, auditable, and accurate for factual Q&A. Fine-tuning trains the model weights on your data — better for style/tone adaptation but expensive, harder to update, and prone to hallucination on factual queries. For most business use cases, RAG is the right choice. We advise on which approach fits your specific need during the free consultation call.
Can the AI agent connect to our existing systems?
Yes. We connect AI agents to REST APIs, SQL databases, Google Sheets, CRM systems (HubSpot, Salesforce), project management tools (Jira, Trello), communication platforms (Slack, WhatsApp), and email. The agent can read data, write data, trigger actions, and route tasks — all orchestrated by the LLM. We design the tool schema and integration layer as part of the project scope.
How long does it take to build an AI agent system?
A focused RAG chatbot on a single document source typically takes 6–10 weeks. A multi-agent system with LangGraph orchestration, multiple tool integrations, and a production frontend takes 14–24 weeks. Timeline depends on the number of data sources, tool integrations, and the complexity of the agent logic. We give a fixed-price quote and timeline commitment before starting.
Ready to Build Your AI Agent?
Tell us your use case — RAG chatbot, document AI, or LLM automation. We'll design the architecture and give you a fixed-price quote within 48 hours. NDA signed before any technical discussion.
