Agentic Unconference 2026 - September 19, Chennai
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Entrans builds RAG search, chatbots and tool-calling agents on LangChain and LangGraph that run inside your cloud and connect to your real systems. Senior engineers own the evaluation, guardrails and monitoring that keep an enterprise pilot from stalling before production.
LangChain development services cover building and running software that connects large language models to your documents, databases and business tools. The work uses the open-source LangChain framework, often with LangGraph for multi-step agents and LangSmith for tracing and testing. The output is production code your team owns.
✓ Answers have to come from your own documents. Policy manuals, contracts and support tickets change every week, so retraining a model is the wrong tool. LangChain's built-in loaders and retrievers handle the plumbing, so the effort goes into answer quality.
✓ The task takes several steps across real systems. Checking an order, updating a CRM record and drafting a reply is a workflow, not a prompt. LangGraph keeps state between steps and can resume a run after a failure.
✓ You don't want to bet on one model vendor. LangChain puts OpenAI, Anthropic, Google and Amazon Bedrock models behind one standard interface. You can test a cheaper model against the same evaluation set before you switch.
✓ Someone will ask why the AI gave that answer. Compliance, legal and support leads need to trace an output back to its sources. LangSmith records each retrieval, model call and tool call in a run you can inspect later.
> The feature is a single model call. Summarizing a field or classifying a ticket needs the provider's SDK and structured output, not a framework. Extra layers only add dependencies to upgrade, so we'd handle it as plain AI integration work.
> The process has fixed rules with one AI step. If operations staff need to see and change the routing themselves, a visual workflow tool fits better than Python code. n8n workflow automation keeps that logic editable without a release cycle.
> You only need an assistant inside Microsoft 365. For questions over SharePoint, Teams and Outlook files, Microsoft Copilot uses the permissions you already have and needs far less custom code. Our Microsoft Copilot consulting team is the better starting point.
We build LLM features as services, with typed inputs, structured outputs, retries, rate limits and cost tracking per request. Python is our default, and LangChain.js fits when your product team ships in TypeScript. We start with one workflow and a target you can measure.
Good retrieval is what makes people trust the answers. Our RAG development services tune chunking, hybrid search and reranking against your own documents. Access permissions stay attached to every chunk, and each answer carries citations a reviewer can check.
Agents that touch real systems have to survive failures. We build AI agents on create_agent and LangGraph, with checkpoints so a long-running task resumes where it stopped. MCP servers expose your internal tools with scoped access.
AI chatbots stall when they can't see live data. We connect assistants to your order, ticket and account systems through tested APIs and scope memory to each user. Complex cases go to a person with the full transcript attached.
Many teams built on LangChain 0.x and still carry legacy chains and AgentExecutor code. We move them to LangChain 1.x and LangGraph, using langchain-classic as a bridge where needed. Tests come first, so behavior doesn't drift during the upgrade.
Each release runs against a fixed test set in CI, so a prompt or model change that hurts accuracy fails the build. We deploy inside your AWS, Azure or Google Cloud account, trace every run in LangSmith, and track cost per conversation after launch.
80%
Reduction in Manual Reconciliation Effort
Used LLMs on AWS Bedrock to read invoices, matched each one against purchase orders and goods receipt notes, and flagged quantity, price and supplier mismatches automatically.
3X
Reduction in Manual Reconciliation Effort
Used OCR and large language models to pull clean data from messy authorization documents, added a rule engine for medical and payer checks, and split the monolith into microservices.
24%
reduction in manual support responses from automated bot integration
Rebuilt a fragmented web platform for a SaaS company serving fintechs and banks, then added an AI compliance chatbot that answers routine inquiries instead of queuing them for support.
Get a 20-minute technical read from a senior engineer. No pitch deck, no sales team.
Yes, LangChain is ready for production when the app around it is built for production. The framework has been on a stable 1.x release line since October 2025. Its agents run on LangGraph, which saves progress, resumes after failures and can pause for human approval. What tends to break in production is rarely the framework itself. It's missing tests, weak retrieval, tools with too much access and nobody watching cost per request.
LangChain builds LLM applications and agents, LangGraph runs stateful multi-step workflows, and LangSmith traces, evaluates and deploys them. LangChain's agents are built on top of LangGraph, so most production builds use both. LangChain and LangGraph are open source under the MIT license. LangSmith is a commercial platform, and it also works with code that doesn't use LangChain at all.
Yes, LangChain is model-agnostic and works with OpenAI, Anthropic, Google Gemini, Amazon Bedrock and Azure OpenAI models. It also supports open-weight models served through Ollama or vLLM. On the data side, it connects to vector stores such as pgvector, Elasticsearch and Pinecone. It also reads from SQL databases and document sources like SharePoint or Amazon S3. The app can run inside your own cloud account, under the data rules you already follow.
We test it against a fixed set of real questions with known good answers, and that suite runs automatically before every release. For RAG, we score two things separately. Did the right passages come back, and does the answer stick to them and cite them? For agents, we check the tool calls and their arguments, not just the final message. LangSmith stores the datasets and results, so an accuracy drop shows up as a failed check rather than a user complaint.
We secure LangChain agents by treating every tool they can call like a public API, since text the agent reads can smuggle in instructions. That means least-privilege credentials for each tool, allow-lists for actions and validation on inputs and outputs. A person approves anything that moves money or changes records. LangChain 1.x has built-in middleware for part of this, such as masking personal data and pausing for human sign-off. We log every tool call so your security team can audit what the agent did and why.
Tell us the shape of the problem. A senior engineer reads it and replies. You won't get a templated capability deck.
