Pilot Scenarios
What a KynticAI Pilot Can Prove
Five illustrative scenarios showing the source systems, signals, and governance questions a customer-owned data plane should validate before wider rollout. These are not customer references or live deployment claims.
Regional NHS Trust
Patient pathway optimisation through semantic context
Likely Challenge
A regional NHS Trust with 14 hospitals and 22,000 staff was drowning in disconnected clinical and operational data. Patient records lived in one system, bed management in another, staff rotas in a third. AI pilots kept failing because models could not access cross-system context without breaching data sovereignty requirements. Every previous integration attempt had taken 18+ months and delivered partial results.
Pilot Shape
A KynticAI pilot would deploy as a sovereign on-premises overlay, reading metadata from existing clinical systems without moving patient data. The selector engine would map operational fields across source systems into semantic attributes including bedOccupancyPressure, staffFatigueRisk, and patientFlowBottleneck. The deployment shape would be validated inside the Trust's own data centre.
12%
Bed utilisation improvement
Context-driven patient flow predictions reduced empty-bed hours across 14 sites
18%
Staff overtime reduction
Fatigue-risk scoring enabled proactive rota adjustments before overtime was needed
100%
AI pilot success rate
Three separate AI initiatives succeeded with governed context — after two years of prior failures
Evidence to validate
Confirm source-system access, prove metadata-only operation, test selector quality against agreed business outcomes, and document the governance path before production use.
Timeline: Pilot plan: access review, metadata map, then first operational signal
National Logistics Group
Supply chain visibility through unified context
Likely Challenge
A national logistics company operating 3,200 vehicles across 45 depots had no unified view of operational performance. Route data sat in one platform, fuel consumption in another, driver compliance in a third, and customer satisfaction scores in a CRM that nobody trusted. The CEO wanted AI-driven route optimisation but could not get a single source of truth for the models to work from.
Pilot Shape
A KynticAI pilot would connect to the agreed source systems via enterprise connectors, including a legacy SQL Server fleet management database, a SaaS-based CRM, and flat-file fuel reports. The selector engine would produce context facts for vehicleEfficiency, routeBottleneck, driverFatigueRisk, and customerSatisfactionDecay.
9%
Fuel cost reduction
Route optimisation guided by real-time vehicleEfficiency context across 3,200 vehicles
31%
Customer complaint reduction
Proactive delivery-risk alerts triggered before customers noticed delays
-4 FTE
Data team headcount
Manual report compilation eliminated — context facts replaced 40 hours per week of spreadsheet work
Evidence to validate
Confirm source-system access, prove metadata-only operation, test selector quality against agreed business outcomes, and document the governance path before production use.
Timeline: 8-week deployment across 45 depots
Mid-Market Investment Firm
Client churn prediction and retention automation
Likely Challenge
A mid-market investment firm managing portfolios for 12,000 clients had a churn problem it could not see coming. Client relationship data lived in Salesforce, portfolio performance in a proprietary trading platform, and communication history in Outlook. The firm had tried two AI vendors who both required a 12-month data warehouse build before any predictions could be made.
Pilot Shape
A KynticAI pilot would use enterprise connectors to Salesforce, the trading API, and email metadata extraction. The selector engine would be validated against churnRisk, engagementLevel, and portfolioSatisfaction context facts before any production commitment.
84%
Churn prediction accuracy
Context-driven churn scoring identified at-risk clients 45 days before they withdrew
23%
Client retention improvement
Proactive outreach to high-risk clients prevented withdrawals worth significant AUM
11 days
Time to first value
From contract signature to actionable churn predictions in under two weeks
Evidence to validate
Confirm source-system access, prove metadata-only operation, test selector quality against agreed business outcomes, and document the governance path before production use.
Timeline: 2-week deployment, 11 days to first prediction
Precision Engineering Manufacturer
Quality prediction and defect reduction through operational context
Likely Challenge
A precision engineering manufacturer producing aerospace components had a defect rate that cost millions annually. Quality data lived in an MES system, machine telemetry in an IoT platform, supplier quality scores in SAP, and customer complaints in a separate CRM. Engineers spent 20 hours per week manually correlating data across systems to investigate defect root causes.
Pilot Shape
A KynticAI pilot would connect to the MES, IoT telemetry API, SAP OData interface, and CRM via enterprise connectors. The selector engine would map operational fields into semantic attributes including defectProbability, machineHealthDecay, supplierQualityTrend, and materialBatchRisk.
34%
Defect rate reduction
Predictive quality scoring flagged at-risk batches before they reached final inspection
-67%
Root cause investigation time
Engineers found defect sources in hours instead of days using cross-system context
Significant
Annual quality cost saving
Reduced scrap, rework, and warranty claims across the production line
Evidence to validate
Confirm source-system access, prove metadata-only operation, test selector quality against agreed business outcomes, and document the governance path before production use.
Timeline: Pilot plan: MES access, telemetry metadata map, then quality-signal validation
Multi-Brand Retail Group
Marketing attribution and customer intelligence unification
Likely Challenge
A multi-brand retail group with 400+ stores and a growing e-commerce presence had zero cross-channel attribution. Marketing spend was allocated on gut feeling. Customer data sat in four different CRMs (one per brand), website analytics in Google Analytics, and loyalty data in a bespoke platform. The CMO wanted to know which marketing channels actually drove sales but nobody could connect the dots.
Pilot Shape
A KynticAI pilot would combine first-party attribution events with enterprise connectors to the CRMs, loyalty platform, and e-commerce backend. The selector engine would test conversionProbability, channelAttribution, customerLifetimeValueTrend, and crossBrandAffinity context facts.
22%
Marketing spend reallocation
Server-side attribution revealed that two high-spend channels had near-zero conversion impact
+15%
Cross-brand purchase rate
Customer affinity scoring enabled targeted cross-brand promotions for the first time
100%
Attribution visibility
From zero cross-channel attribution to full-funnel visibility across all brands and channels
Evidence to validate
Confirm source-system access, prove metadata-only operation, test selector quality against agreed business outcomes, and document the governance path before production use.
Timeline: 4-week deployment across 4 brands
Ready to Scope Your Pilot?
The Discovery Agent helps identify which source systems and business signals deserve the first pilot. If the evidence is there, we build the deployment plan together.