LangChain Development
Built for Production
We build RAG pipelines, AI agents, and LLM-powered applications using LangChain connecting your data to the world's most powerful AI models. Real answers. Real context. Production-ready.
End-to-End LangChain Services
From RAG pipelines to multi-agent systems we build every LangChain component your AI application needs.
RAG Pipelines
Build Retrieval-Augmented Generation systems that let your LLM answer questions using your own documents, databases, and knowledge bases with accurate, cited answers.
AI Agent Chains
Create multi-step AI agents that reason, plan, use tools, and execute tasks autonomously from research agents to code-writing bots to complex decision engines.
Vector Database Integration
Connect Pinecone, Supabase Vector, Weaviate, or pgvector to store and retrieve embeddings at scale the foundation of every accurate RAG and semantic search system.
Document Intelligence
Parse, chunk, embed, and query PDFs, Word docs, spreadsheets, and web pages so your LLM can reason over large document collections accurately.
LLM Orchestration
Route queries across multiple LLMs GPT-4o, Claude, Gemini, or open-source models based on cost, latency, or capability, with automatic fallbacks.
Custom Chain Development
Build custom LangChain chains and tools tailored to your exact use case memory management, structured output parsing, tool use, and multi-agent coordination.
Why LangChain
Smarter Chains. Better Output.
Why Choose Mind Stack Labs
LangChain specialists since 2022
GPT-4o, Claude & open-source LLM experts
250+ AI systems delivered globally
Dedicated project manager per engagement
Clean, documented, production-ready code
NDA & full IP ownership guaranteed
Long-term support & maintenance plans
Free 30-day post-launch support included
Industries We Serve
We've built LangChain applications for businesses across every industry from solo founders to enterprise ops teams.
LangChain Apps We Build
From document Q&A to multi-agent intelligence we've built every type of LangChain application.
Document Q&A System
Upload your internal docs, manuals, or legal files → LangChain chunks and embeds them → users ask questions in plain English → system returns accurate, cited answers from your documents.
AI Customer Support Bot
LangChain retrieves relevant knowledge-base articles → GPT-4o generates a contextual reply → escalates to human if confidence is low → logs conversation to CRM.
Research & Summarisation Agent
Agent receives a topic → searches the web or internal docs → reads and summarises findings → produces a structured report in minutes, not hours.
Code Generation Assistant
Internal dev tool where engineers describe a feature → LangChain agent generates code → runs tests → iterates on failures → produces a PR-ready diff.
Contract & Legal Document Analysis
Upload contracts → LangChain extracts key clauses, dates, obligations, and risks → produces a plain-English summary → flags anomalies for legal review.
Multi-source Data Intelligence
Connect your CRM, database, and docs to a LangChain agent → business users ask questions in plain English → agent queries the right source and synthesises a complete answer.
LangChain Stack We Use
Every workflow we build uses this stack battle-tested and production-ready across hundreds of deployments.
LangChain
OpenAI GPT-4o
Supabase Vector
Pinecone
Python
FastAPI
PostgreSQL
Next.js
DockerHow We Work
Our Process
From discovery to deployment we follow a proven 4-phase process that ensures every LangChain app is reliable, scalable, and production-ready.
Use-case Design
We define the retrieval strategy, agent architecture, memory requirements, and data sources choosing the right LangChain components for your specific problem.
Data Pipeline Build
We build ingestion pipelines to load, chunk, clean, and embed your documents or data and store embeddings in the right vector database for your scale.
Chain & Agent Build
We build and wire all LangChain components retrievers, chains, agents, tools, and memory and test against real queries to tune accuracy and latency.
Deploy & Monitor
We deploy via FastAPI or serverless, set up latency and cost monitoring, and hand over full documentation and source code.
Advanced AI Capabilities
Multi-agent Orchestration
Coordinate multiple specialised AI agents a researcher, a writer, a reviewer that collaborate autonomously to complete complex tasks end-to-end.
Conversation Memory
LangChain memory modules store conversation history so your AI remembers context across sessions enabling natural, ongoing conversations not just one-shot queries.
Hybrid Search (BM25 + Vector)
Combine keyword and semantic search for retrieval so users get accurate results whether they search with exact terms or describe what they're looking for.
LLM Cost Optimisation
Route simple queries to cheaper models and complex ones to GPT-4o with caching, batching, and streaming reducing LLM costs by up to 70%.
FAQ
Common Questions
Have more questions? Book a free 30-minute discovery call no commitment required.
Book a free callWhat's the difference between LangChain and just calling the OpenAI API directly?
Direct API calls work for simple prompts. LangChain adds the infrastructure for complex AI apps retrieval from your own data, agent reasoning, multi-step chains, memory across conversations, and tool use. It's the difference between a one-shot query and a full AI application.
How accurate are RAG systems with our documents?
Accuracy depends heavily on document quality, chunking strategy, and retrieval tuning. We achieve 85–95% accuracy on well-structured documents by optimising every step of the pipeline and we test against your real documents before delivery.
Can LangChain work with our existing database or CRM?
Yes. LangChain supports PostgreSQL, MySQL, Supabase, and any SQL database as a retrieval source plus REST APIs for CRMs. Your AI can query your live data directly alongside your embedded documents.
Which LLM should we use GPT-4o, Claude, or open-source?
It depends on your accuracy needs, budget, and data privacy requirements. We evaluate the right model for your use case and often build multi-LLM setups that route queries intelligently between models to balance cost and quality.
Let's Build Your LangChain AI App
Tell us about your data and use case we'll propose the right LangChain architecture within 24 hours.