Building with LangChain requires more than wiring an LLM to a prompt template. Perimattic designs and builds production-grade LangChain applications — RAG pipelines, LCEL chains, LangGraph agents, and enterprise integrations — that are observable, evaluable, and reliable in live environments.
LangChain Ecosystem We Build On — LangChain, LangGraph, LangSmith, LCEL, Pinecone, Weaviate, OpenAI, Anthropic Claude
LangChain is the most widely adopted framework for building LLM-powered applications. It provides the abstractions that make it practical — rather than merely possible — to go from an LLM API call to a production application: prompt templates that separate logic from content, retriever interfaces that connect to vector stores and document sources, memory systems that maintain conversation state, tool definitions that give LLMs structured access to external systems, and output parsers that enforce schema on model responses.
LCEL (LangChain Expression Language) adds composability: chains are built by piping components together with the |> operator, giving you readable, testable, streamable pipelines that work with LangSmith tracing by default. LangGraph extends this to stateful workflows: graph-based applications where the next step depends on runtime decisions, enabling conditional branching, loops, and human-in-the-loop interruption — the architecture that most production AI agents actually require.
The practical challenge with LangChain is that the framework's flexibility creates as many ways to build poorly as to build well. A RAG pipeline that retrieves irrelevant documents, a chain that has no fallback when the LLM rate-limits, or an agent with no LangSmith tracing in production are common outcomes of inexperienced LangChain development. Perimattic designs LangChain applications with retrieval quality as a primary metric, production reliability as a requirement from day one, and LangSmith observability as a non-negotiable — not an afterthought.
Prompt management
Hard-coded prompt strings scattered across the codebase
Prompt management
Structured PromptTemplates with variable injection and LangChain Hub versioning
Context and retrieval
Manual document concatenation with no retrieval quality measurement
Context and retrieval
Retriever abstractions with hybrid search, metadata filtering, and LangSmith evaluation
Tool use
Custom JSON wrangling for every tool with no standardised error handling
Tool use
Structured tool definitions with built-in validation, retry logic, and output parsing
Observability
No visibility into chain steps, token usage, latency, or retrieval quality
Observability
LangSmith traces every step with cost, latency, and full prompt/response logging
Production reliability
Crashes on rate limits, timeouts, or unexpected LLM output formats
Production reliability
Fallback chains, retry logic, streaming with timeout budgets, and output validation
The distinction matters most when a LangChain application moves from a developer's notebook into a production environment handling real users, real queries, and real data — where retrieval quality, reliability, and cost visibility are non-negotiable requirements.
Seven specialist service lines covering every layer of the LangChain ecosystem — from RAG pipeline architecture to production optimisation.
We design and build Retrieval Augmented Generation pipelines that ground LLM responses in your specific business data. From document ingestion and chunking strategy through embedding model selection, vector store configuration, and hybrid search, every component is built to retrieval quality standards — not demo quality.
We build LangChain agents that use tools — APIs, databases, code interpreters, and custom functions — to complete multi-step tasks autonomously. Each agent includes structured tool definitions, error handling, output validation, and LangSmith tracing so you can see exactly what the agent does and why.
We build composable LangChain pipelines using LCEL (LangChain Expression Language): prompt templates, document transformers, output parsers, and routing chains assembled into clean, readable, testable pipelines. LCEL chains stream by default, handle async execution, and integrate with LangSmith tracing from day one.
We build stateful, graph-based AI workflows using LangGraph for applications that require conditional branching, looping, human-in-the-loop interruption, or multi-agent coordination. LangGraph is the right architecture for any AI workflow where the next step depends on decisions made at runtime.
We connect LangChain applications to your enterprise data: SharePoint, Confluence, Notion, Google Drive, S3, SQL databases, ERPNext, Salesforce, and custom REST APIs. We handle authentication, incremental document syncing, metadata schema design, and access controls so retrieval respects your existing permission model.
We instrument your LangChain application with LangSmith tracing so every chain step — prompt, retrieval result, LLM call, parsed output — is visible and debuggable. We build evaluation datasets, establish accuracy and retrieval quality baselines, and set up production monitoring dashboards before go-live.
We audit existing LangChain applications for retrieval quality, latency, cost, and reliability issues, then implement targeted improvements: retrieval strategy upgrades, prompt engineering, model tier right-sizing, LCEL refactoring, and LangSmith instrumentation. We also migrate ad-hoc LLM integrations to structured LangChain architectures.
A structured six-stage process from free scoping call through production deployment and ongoing LangSmith-instrumented optimisation.
We map your data sources, use case requirements, and integration landscape, then define the chain architecture, retrieval strategy, and LLM selection that fits. This session is free and produces a clear scope with component rationale.
We design the complete application: LCEL chain structure or LangGraph graph, embedding model, vector store schema, chunking strategy, prompt templates, memory configuration, tool definitions, and output parsers — before writing production code.
We build a working LangChain prototype against a real slice of your data. The PoC demonstrates retrieval quality, chain behaviour, and latency on actual documents — surfacing data preparation issues before the full production build begins.
We build the full production application, connecting to your enterprise data sources and APIs. LangSmith tracing, fallback chains, output validation, rate-limit handling, and streaming support are included from the first production sprint.
We build evaluation datasets in LangSmith, establish retrieval quality and accuracy baselines, and stress-test the application against adversarial queries and edge cases. We define the performance criteria that must be met before deployment.
We deploy via LangServe or directly to your infrastructure and hand over a LangSmith monitoring dashboard tracking accuracy, latency, cost per chain run, and retrieval quality. We resolve production issues in the first weeks and plan the next iteration.
We map your data sources, use case requirements, and integration landscape, then define the chain architecture, retrieval strategy, and LLM selection that fits. This session is free and produces a clear scope with component rationale.
We design the complete application: LCEL chain structure or LangGraph graph, embedding model, vector store schema, chunking strategy, prompt templates, memory configuration, tool definitions, and output parsers — before writing production code.
We build a working LangChain prototype against a real slice of your data. The PoC demonstrates retrieval quality, chain behaviour, and latency on actual documents — surfacing data preparation issues before the full production build begins.
We build the full production application, connecting to your enterprise data sources and APIs. LangSmith tracing, fallback chains, output validation, rate-limit handling, and streaming support are included from the first production sprint.
We build evaluation datasets in LangSmith, establish retrieval quality and accuracy baselines, and stress-test the application against adversarial queries and edge cases. We define the performance criteria that must be met before deployment.
We deploy via LangServe or directly to your infrastructure and hand over a LangSmith monitoring dashboard tracking accuracy, latency, cost per chain run, and retrieval quality. We resolve production issues in the first weeks and plan the next iteration.
Select a function to see how LangChain chains, RAG pipelines, and agents deliver measurable productivity gains in that domain.
LangChain RAG pipelines turn static internal knowledge — policy documents, product catalogues, technical manuals, HR handbooks — into queryable intelligence that employees can access through natural language without needing database expertise.
LangChain RAG pipelines turn static internal knowledge — policy documents, product catalogues, technical manuals, HR handbooks — into queryable intelligence that employees can access through natural language without needing database expertise.
LangChain-powered support systems resolve complex multi-step queries by retrieving product documentation, checking account state, and drafting accurate responses — without hallucinating information that is not in your knowledge base.
Finance and legal teams produce and process more documents than any human team can read. LangChain chains extract, summarise, compare, and structure information from contracts, reports, filings, and correspondence at scale.
Sales teams using LangChain chains can enrich prospect research, personalise outreach, prepare competitive briefings, and draft proposals in a fraction of the time — while maintaining the personalisation that converts.
LangChain chains and agents augment software engineering teams by handling codebase search, documentation generation, code review summarisation, and test generation — tasks that consume significant developer time without adding direct product value.
Clinical teams using LangChain RAG and chain applications reduce documentation burden, improve information retrieval from clinical records, and surface relevant evidence — while maintaining the auditability that healthcare AI requires.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate production-grade LangChain engineering from a RAG pipeline that works in a demo but fails in production.
We work across LangChain, LangGraph, LCEL, LangSmith, and LangServe — not just the core chain abstractions. That means we design retrieval architectures that use advanced retrieval patterns, instrument applications with production-grade observability, and select the right sub-framework for your specific use case rather than applying a single template.
Every LangChain application we build includes LangSmith tracing, fallback chains, output validation, retry logic, and streaming support from day one. We do not build prototypes that need to be rewritten for production. The PoC architecture becomes the production architecture.
We select the LLM and embedding model that best fit your retrieval quality requirements, latency budget, cost target, and data residency constraints — not our defaults. LangChain's provider abstractions mean we can swap models, add fallback providers, or split traffic across models without restructuring your application.
The team that designs your LangChain retrieval architecture and evaluates your chain quality also builds, tests, and deploys the production application. You get continuity of context from the first scoping session through go-live, with no hand-off loss between a strategy team and a delivery team.
“Unlike boutique dev shops that build to spec without strategic context, or large consultancies that advise without shipping, Perimattic brings both capabilities to every engagement.”
LangChain development is the practice of building LLM-powered applications using the LangChain framework and its ecosystem — including LangGraph for stateful workflows, LCEL (LangChain Expression Language) for composable chain construction, LangSmith for evaluation and observability, and LangServe for deployment. LangChain provides the abstractions that make it practical to build reliable, production-grade AI applications: prompt templates, retrievers, memory systems, tool definitions, output parsers, and the orchestration logic that connects them into a coherent pipeline.
Calling an LLM API directly gives you raw text in and text out. LangChain wraps that call in a composable pipeline that handles prompt templating, input/output parsing, document retrieval, conversation memory, tool calling, streaming, retry logic, fallback chains, and observability — all with consistent abstractions that work across multiple LLM providers. The practical difference is the gap between a prototype that works in a notebook and a production system that handles edge cases, logs every decision, can be evaluated against benchmarks, and can be updated without rewriting the core logic.
LCEL, or LangChain Expression Language, is the declarative interface for composing LangChain components using the pipe operator (|>). It lets you construct chains by connecting components — prompts, models, retrievers, parsers — in a readable, composable syntax where each component's output becomes the next component's input. LCEL handles streaming, async execution, batch processing, and parallel branches automatically. For production applications it matters because LCEL chains are inspectable, testable, and re-usable in a way that imperative chain construction is not.
LangGraph is LangChain's framework for building stateful, graph-based workflows where the control flow depends on decisions made at runtime — as opposed to a linear chain where every step always executes. Use LangGraph when your application needs conditional branching (e.g. routing a query to different specialist agents based on intent classification), looping (e.g. a research agent that keeps searching until it finds sufficient evidence), or human-in-the-loop escalation points. LangGraph is the right choice for most agent architectures; standard LCEL chains are the right choice for linear document processing and Q&A pipelines.
RAG, or Retrieval Augmented Generation, is the technique of retrieving relevant documents or data from a knowledge base and providing that context to the LLM before it generates a response. This grounds the model's output in your specific business data rather than general training knowledge. LangChain implements RAG through its retriever abstraction, which connects to vector stores (Pinecone, Weaviate, pgvector, FAISS, Qdrant), document loaders for over 100 source formats, text splitters for chunking, embedding models, and the retrieval chain components that wire everything together into a complete pipeline. LangChain also supports advanced retrieval patterns including multi-query retrieval, parent-document retrieval, and hybrid search.
LangChain's ChatModel and LLM abstractions support all major providers including OpenAI (GPT-4o, GPT-4 Turbo), Anthropic (Claude 3.5 Sonnet, Claude 3 Opus), Google (Gemini 1.5 Pro, Gemini 1.5 Flash), Meta (Llama 3 via Ollama or Together AI), Mistral (Mistral Large, Mixtral), Cohere (Command R+), and self-hosted models through Hugging Face and Ollama. The provider abstraction means your chain logic is not coupled to a single LLM — you can swap providers, add fallbacks, or run A/B tests without restructuring your application.
LangSmith is LangChain's platform for tracing, evaluating, testing, and monitoring LLM applications. Every LangChain call can be traced to LangSmith, giving you a full view of every prompt, every retrieval result, every LLM response, and every parsed output in a chain — with latency, token count, and cost for each step. For evaluation, LangSmith provides an evaluation framework where you define test datasets, run your chain against them, and score outputs automatically or with human review. We use LangSmith on every production LangChain engagement to establish performance baselines before go-live and monitor accuracy and cost in production.
A proof-of-concept LangChain application — a RAG pipeline or a simple chain against your data — typically takes two to four weeks. A production application with full integrations, evaluation pipelines, LangSmith instrumentation, and deployment typically takes six to twelve weeks depending on the number of data sources, the complexity of the retrieval architecture, and the integration requirements. LangGraph-based agent systems with multiple tools and stateful workflows typically run eight to fourteen weeks for initial production deployment.
Yes. Migrations typically start with a mapping exercise: identifying all the places in the existing codebase where LLM calls, prompt strings, and response parsing happen. We then wrap those calls in the appropriate LangChain abstractions (ChatModels, PromptTemplates, OutputParsers), add LangSmith tracing, replace any ad-hoc retrieval logic with proper retriever implementations, and introduce structured memory management. The migration gives you observability you did not have before, a structured testing framework, and the ability to switch providers or models without rewriting application logic.
Production LangChain reliability comes from several layers. At the chain level: LCEL fallback chains so if the primary LLM is unavailable the chain routes to a backup provider; retry logic with exponential back-off for rate limit handling; output parsers with error correction so a malformed LLM response triggers a structured retry rather than a crash. At the retrieval level: retrieval quality evaluation using LangSmith before go-live; query expansion and multi-query strategies to reduce missed retrievals; metadata filtering to scope retrieval to the right data partition. At the system level: streaming with timeout budgets; async chain execution with concurrency controls; and rate-limit-aware token budgeting.
We have delivered LangChain applications for clients in financial services (contract analysis, regulatory document retrieval, reporting automation), healthcare (clinical documentation, medical literature search, patient record Q&A), professional services (knowledge management, proposal generation, due diligence automation), SaaS and technology (developer tooling, codebase search, support automation), and e-commerce and retail (product catalogue Q&A, customer support, inventory intelligence). The LangChain ecosystem's support for data privacy controls and on-premises deployment makes it viable for regulated industries with strict data handling requirements.
Tell us about your data, your use case, and your integration requirements. We will show you exactly how a LangChain RAG pipeline, chain, or agent can reduce manual workload, improve accuracy, and deliver a measurable return in the first quarter.