AI
LangChain
Orchestration for retrieval, tools and multi-step AI workflows.
AIRAG pipelinesTool-using agents
Where we use it
Workloads we build with LangChain
RAG pipelines
Tool-using agents
Evaluation harnesses
Strengths
Why we choose it
- Rapid composition of AI workflows
- Provider abstraction
- Large integration surface
Considerations
What we watch for
- Abstractions must be pinned and reviewed
- Keep custom logic testable outside the framework
Services using this technology
Industries where we deploy it
HealthcareClinical throughput, administrative relief and defensible data handling.ManufacturingConnected plants, predictable output, lower unplanned downtime.FinanceFaster decisions, defensible controls, lower cost to serve.GovernmentCitizen services, digitised process and auditable delivery.EducationAcademic operations, student experience and institutional reporting.LogisticsCost per shipment down, exceptions handled before they escalate.RetailDemand accuracy, margin protection and connected commerce.MiningSafety, asset uptime and site-to-corporate visibility.Real EstatePortfolio visibility, faster sales cycles, tighter project control.HospitalityOccupancy, guest experience and lean back-office operations.NGOMore programme impact per rupee of overhead.EnergyAsset reliability, consumption intelligence and compliance evidence.ConstructionProgramme certainty, cost control and site-level visibility.Professional ServicesHigher utilisation, faster delivery, protected margin.
Pairs well with
Capabilities built on LangChain
Prompt EngineeringSystematic prompt design, versioning and evaluation instead of ad-hoc experimentation.AI AgentsTask-completing agents with tool access, guardrails and human approval gates.CI/CDAutomated build, test and deployment pipelines with quality gates.ETLReliable, monitored ingestion and transformation pipelines.Workflow AutomationRouting, approvals and hand-offs automated across teams and systems.Document AutomationExtract, validate and file documents without manual keying.
Related insights
Why enterprise AI pilots stall — and what changes when they do notThe gap between a working demo and a production system is organisational, not technical.Automation that survives auditDesigning RPA and document automation with controls, logging and ownership from day one.Modernising legacy systems without a big-bang cutoverIncremental strangulation beats a single weekend of concentrated risk.
Assess and plan
AI Readiness AssessmentScore your organisation across six readiness dimensions in about ten minutes.Enterprise AI Readiness ChecklistThe seven-dimension checklist we use before committing to an enterprise AI programme.Downloads centreCapability statement, readiness checklist and other practitioner material.
Downloadable guide
LangChain engineering standards
Our reference architecture, code standards, testing approach and operational checklist for this technology.
Need this stack delivered by
people who have run it in production?
Architecture review, team augmentation or full delivery — we will show you reference implementations before you commit.