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.ConstructionProgramme certainty, cost control and site-level visibility.Professional ServicesHigher utilisation, faster delivery, protected margin.
Pairs well with
Capabilities built on Vector Databases
Recommendation SystemsPersonalisation engines for commerce, content and internal decision support.Generative AIApplied generation for drafting, summarising, extraction and content operations.Workflow AutomationRouting, approvals and hand-offs automated across teams and systems.Document AutomationExtract, validate and file documents without manual keying.Enterprise SoftwareLarge-scale internal platforms carrying core operational load.SaaSMulti-tenant product engineering, from MVP to scale.
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
Vector Databases 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.