AI
MCP
Model Context Protocol for giving assistants governed access to enterprise tools and data.
AIEnterprise copilots with tool accessGoverned data access for agents
Where we use it
Workloads we build with MCP
Enterprise copilots with tool access
Governed data access for agents
Internal developer assistants
Strengths
Why we choose it
- Standardised tool interface
- Cleaner permission boundaries
- Reduces bespoke glue code
Considerations
What we watch for
- Access scoping must be explicit
- Ecosystem still maturing
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 MCP
AI AgentsTask-completing agents with tool access, guardrails and human approval gates.Knowledge ManagementTurning scattered institutional knowledge into a governed, searchable, answerable corpus.Enterprise SoftwareLarge-scale internal platforms carrying core operational load.Hospital ManagementClinical, administrative and billing workflows in one governed platform.Process DigitizationConvert paper and email-driven processes into governed digital workflows.Executive MISBoard-ready packs generated from governed data, not spreadsheets.
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
MCP 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.