We build with LangChain
We use LangChain to build LLM-powered applications like chatbots, agents, and retrieval-augmented systems that support your business goals. From chain and prompt design to tool integration and evaluation, we create products designed to evolve with your needs.
DISCUSS YOUR PROJECTComposable Chains
We connect prompts, tools, and data into structured workflows.
Agent Orchestration
We build agents that reason, call tools, and take action.
RAG-Ready Integrations
We connect LLMs to vector stores and external data sources.
BENEFITS OF LANGCHAIN technology
We use LangChain to orchestrate multi-step LLM workflows, connect models to external data, and standardize tool use.
BUILD
[01]- Design prompt chains
- Configure retrieval pipelines
- Connect LLM providers
- Set up agent tools
ENGAGE
[02]- Orchestrate multi-step reasoning
- Execute agent tool calls
- Retrieve relevant context
- Coordinate multiple LLM calls
GROW
[03]- Add new integrations
- Extend agent capabilities
- Trace and evaluate runs
- Scale RAG pipelines
Our LangChain Technology Stack
We combine LangChain with vector databases like Pinecone and Chroma, LLM providers such as OpenAI and Anthropic, and LangGraph and LangSmith for orchestration, tracing, and evaluation.
Custom LangChain development company
With our LangChain development services, we build retrieval-augmented generation systems, conversational agents, and multi-step LLM-orchestrated workflows, tailored to your boldest business goals. Having years of experience with LangChain, our engineers harness its full potential to connect large language models with your data, tools, and business logic across industries and company sizes. Whether it's a new AI application built from scratch or adding LLM capabilities to an existing product, we design chains and agent workflows that scale with real usage. Our range of LangChain development services spans consulting, architecture design, development, and ongoing maintenance. We work as your technical partner and ensure complete transparency and comfortable communication throughout the process, bringing deep LLM engineering expertise and a pragmatic approach to prompt design, retrieval, and agent orchestration. All this to make sure your application delivers reliable, well-grounded outputs as usage grows, and moves your business forward.
OUR LANGCHAIN SERVICES
We build, modernize, and support LangChain applications around your product goals.
Your roadmap is growing faster than your team. Add senior engineering capacity and deliver more without sacrificing quality.
Custom LLM applications, from prototype to production, built around your data and workflows.
Retrieval-augmented generation pipelines that ground LLM outputs in your own data sources.
Multi-step agents that call tools, execute tasks, and reason over your business logic.
Migrate prototype LLM scripts to production-grade chains, or optimize an existing LangChain setup.
Meet our LangChain experts
A curated selection of senior specialists currently available for new engagements.
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We Turn Technology Into Results
Partner with a team that blends technical precision, creative design, and business insight. We’ll help you launch, scale, and dominate your digital niche.
Frequently Asked Questions
Common questions about how we use LangChain and what it can bring to your project. Have a specific requirement?
How does SoftDoes use LangChain?
We use LangChain to build retrieval-augmented generation systems, conversational agents, and multi-step LLM-orchestrated workflows. We select the models, retrievers, and tools around your product's data and business logic.
What types of applications do you build with LangChain?
We build RAG-powered knowledge assistants, customer-facing chatbots, internal copilots, and multi-step agents that automate workflows across your existing systems.
Can LangChain integrate with our existing data and tools?
Yes. LangChain connects to your databases, APIs, and internal tools through document loaders, retrievers, and custom tool definitions, so agents can act on your real data.
Do you use LangGraph for agent workflows?
Yes, we use LangGraph when a project needs stateful, multi-step agent behavior with branching logic, retries, or human-in-the-loop checkpoints, rather than a single linear chain.
Which LLM providers and vector databases do you work with?
We build with OpenAI, Anthropic, and Hugging Face models, and pair them with vector databases such as Pinecone, Chroma, or FAISS depending on your scale and hosting requirements.
How do you monitor and evaluate a LangChain application in production?
We use LangSmith to trace requests, debug agent behavior, and evaluate output quality, so we can catch regressions and improve prompts and retrieval as usage grows.
How do you decide whether LangChain fits a project?
We look at your data sources, required tool integrations, latency needs, and team experience, then confirm LangChain is the right fit before starting development.
