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.Professional ServicesHigher utilisation, faster delivery, protected margin.
Pairs well with
Downloadable guide
LangChain engineering standards
Our reference architecture, code standards, testing approach and operational checklist for this technology.
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