Data / AI
Vector Databases
Semantic retrieval layers with tenant and role-aware access control.
Data / AIRAG over enterprise contentSemantic search
Where we use it
Workloads we build with Vector Databases
RAG over enterprise content
Semantic search
Recommendation and similarity features
Strengths
Why we choose it
- Fast semantic retrieval at scale
- Hybrid keyword and vector search
- Metadata filtering for access control
Considerations
What we watch for
- Index freshness needs a pipeline
- Access control must be enforced at retrieval
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
Vector Databases engineering standards
Our reference architecture, code standards, testing approach and operational checklist for this technology.
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