Manual routing uses yesterday's data. AI recalculates every 15 minutes from live traffic, weather, capacity, and driver hours. β31% fuel, +28% deliveries per vehicle.
π¦ Last-Mile: 53% of Total Cost
Last-mile is the most expensive and visible part of logistics. AI failure prediction, dynamic slotting, and proactive rescheduling cut last-mile cost by 28% per parcel.
π¨ Disruption: Blind Until It's Too Late
AI disruption prediction detects traffic incidents, port delays, and vehicle breakdowns 2β4h ahead β triggering rerouting and customer notifications before the problem compounds.
All AI Agents
π£
Route Optimisation AI
Real-time route calculation: traffic, weather, windows, capacity, driver hours. Recalculates every 15 min. β31% fuel. β24% COβ.
Route Optimisation AI recalculates every active route every 15 minutes incorporating: real-time traffic, weather events, delivery window constraints, vehicle capacity, driver hours remaining, and road restrictions. When a traffic incident is detected on Route R-0847, the AI calculates 12 alternative paths in 200ms and presents the optimal reroute to the driver β including revised ETA for all remaining stops. Average fuel saving: 31%. Average additional deliveries per vehicle per day: 28%. COβ reduction: 24% fleet-wide. All route changes are presented to drivers as recommendations β drivers retain authority to deviate for local knowledge.
Last-Mile Cost
β28%
vs industry avg
First Attempt Success
94%
vs 81% industry
Failed Delivery Prediction
87%
Accuracy 24h ahead
Cost per Parcel
β28%
AI optimisation
π¦ Last-Mile Intelligence
Last-mile delivery accounts for 53% of total logistics cost and is the most visible part of the customer experience. AI last-mile intelligence reduces cost and improves experience through three mechanisms: (1) Failure prediction β 87% accuracy in predicting failed deliveries 24h ahead, triggering proactive rescheduling and customer communication before the driver wastes a journey. (2) Dynamic slotting β consolidates nearby deliveries into optimal time windows, reducing per-parcel cost by 28%. (3) Crowdsourced triggers β identifies when to activate gig economy capacity for overflow, optimising the fixed-vs-variable fleet split in real time.
Vehicles Active
284
Fleet monitored live
Fleet Utilisation
89%
β22pts from AI
Driver Hours Compliance
100%
EU WTD adherence
Maintenance Flags
3
Predictive alerts
π Fleet Intelligence
Fleet Intelligence tracks every vehicle and driver in real time. Load optimisation ensures vehicles depart at target utilisation β reducing the number of vehicles needed to serve the same delivery volume. Driver performance monitoring identifies coaching opportunities without punitive surveillance: harsh braking, excessive idling, and out-of-hours running are flagged to fleet managers, not automatically reported to drivers. EU Working Time Directive compliance is monitored continuously β the system prevents route assignment that would breach driver hours before the journey begins, not after. Predictive maintenance flags for fleet vehicles are integrated with the maintenance scheduling system.
Labour Productivity
+34%
Demand-driven staffing
Inbound Processing
β28%
Scheduling AI
Slotting Accuracy
97%
Pick path optimised
Demand Forecast Accuracy
91%
4-week horizon
π Warehouse Intelligence
Warehouse AI optimises the three largest cost drivers in distribution operations: (1) Labour planning β demand forecasting drives a rolling 4-week staffing plan. Right headcount, right skills, right shifts. Labour productivity up 34%. (2) Slotting β fast-moving SKUs positioned to minimise pick travel distance. Slotting recommendations updated weekly as velocity profiles change. Pick time per unit reduced 22%. (3) Inbound scheduling β carrier arrival times smoothed to prevent receiving dock congestion. Dock staff utilisation improved from 61% to 87%. All recommendations require operations manager approval before shift planning is finalised.
π‘ Live Agent Trace
All decisions logged Β· full audit trail
π‘ AI Governance
Advisory intelligence β humans decide
No autonomous consequential decisions: All significant actions require human approval. AI recommends β authorised personnel decide and execute.
Full explainability: Every AI output includes source data, reasoning chain, and confidence level. No black-box recommendations.
Human override always available: Any AI recommendation can be overridden at any time. Override is logged and reviewed.
Regulatory compliance: All processes designed to applicable sector frameworks. Data processed under relevant legal basis. Audit trails maintained.
Statistical significance (p<0.05) required before promotion.
πͺ Feature Store
Vector IndexPinecone
Dimensions3,072
Indexed Docs284K
Retrieval P9542ms
π¦ Prompt Version Control
System promptsGit-tracked
Few-shot examplesVersioned
Eval datasetsDVC tracked
DevSecOps β Security-First CI/CD Pipeline
π CI/CD Pipeline
πSAST β Semgrep + BanditPASS
π¦SCA β SBOM + TrivyPASS
π§ͺUnit + Integration tests847/847
π―RAGAS eval gate (β₯0.92)0.94 β
πSecrets scan β GitleaksCLEAN
π³Container scan β Grype0 CRITICAL
π’Deploy β KubernetesDEPLOYED
π Security Posture
RBAC β Role-based accessEnforced
API keys β HashiCorp VaultRotated 30d
mTLS β Istio service meshActive
PII scrubbing β NeMoActive
Audit log β ImmutableCloudWatch
Pen testQuarterly
SOC 2 Type IIIn progress
ISO 27001Compliant
π Infrastructure as Code
TerraformCloud infra
HelmK8s workloads
ArgoCD GitOpsSynced
Kustomize overlaysdev/stg/prd
β»οΈ Rollback & DR
RTO Target<15 min
RPO Target<5 min
Blue/Green DeployActive
Auto-rollbackError rate >1%
π Regulatory Compliance
GDPR Art. 22 HITLEnforced
EU AI Act Art. 9Documented
NIST AI RMFMapped
ISO/IEC 42001Compliant
AI Observability β OpenTelemetry + Langfuse
π Observability Stack
L1TracesOpenTelemetry β Jaeger
L2MetricsPrometheus β Grafana
L3LLM TracesLangfuse (self-hosted)
L4LogsFluentd β OpenSearch
L5AlertsAlertManager β PagerDuty
π SLO Dashboard
Availability SLO99.9% target
Current (30d)99.96%
Error Budget73% remain
P50 Response0.8s
P95 Response3.1s
P99 Response7.4s
π¨ Active Alerts
Latency P95Normal
Error rate0.3% β
Token budget84% remain
RAG recall0.93 β
Latency drift+120ms watch
π¬ Langfuse Trace Explorer
π Avg Span Breakdown
API Gateway12ms
Auth + RBAC8ms
RAG retrieval42ms
Guardrail check18ms
LLM inference1,240ms
Tool execution84ms
Total E2E1,452ms
Guardrails β Responsible AI Framework
π‘ NeMo Guardrails β Active Rails
β Human-in-the-Loop (HITL) Gate
All consequential actions require human approval before execution. Confidence <0.85 always escalates. GDPR Article 22 compliant β no fully automated consequential decisions.
π PII Detection & Scrubbing
Microsoft Presidio + custom patterns. Names, emails, NI/SSN, card numbers scrubbed from all LLM I/O before logging. 47 entity types across 12 jurisdictions.
π« Toxicity & Hallucination Filter
NeMo topic rails block off-topic responses. Factual grounding check cross-references every claim against retrieved context. Hallucination >5% triggers human review queue.
β± Rate Limiting & Abuse Prevention
Per-user token budgets at API gateway. 10Γ anomalous usage triggers suspension + security alert. Cloudflare WAF DDoS protection.
π Audit Trail & Explainability
π Immutable Decision Log
Every AI recommendation logged: input context, retrieved docs, reasoning chain, confidence, model version, user ID, timestamp. 7-year retention for regulated decisions.
π Explainability (XAI)
Every recommendation includes source citations, confidence intervals, alternatives considered, and limitation disclosures. SHAP attribution for structured ML models.
βοΈ Bias Monitoring
Fairness metrics tracked across protected characteristics. Disparate impact analysis monthly. EU AI Act Article 10 data governance requirements met.
π Regulatory Mapping
GDPR Art. 5/22 Β· EU AI Act Art. 9/10/13/14 Β· NIST AI RMF Β· ISO/IEC 42001 Β· IEEE 7001 Transparency. Compliance evidence pack generated quarterly.
0.3%
Hallucination Rate
Target <2%
100%
HITL Coverage
Consequential acts
0
PII Leaks (30d)
Target: 0
A+
Security Grade
Mozilla Observatory
Multi-Agent Architecture β Mesh & Orchestration
πΈ Agent Mesh Topology
Orchestrator
Agent 1
Agent 2
Agent 3
Agent 4
Agent 5
Agent 6
Orchestrator decomposes tasks, routes to specialists, aggregates results, handles conflicts. All inter-agent communication via typed schemas. No agent takes external action without Orchestrator validation.
βοΈ Agent Patterns
ReAct β Reason + Act loopsAnalytical
Reflection β Self-critique cyclesHigh-stakes
Planning β Hierarchical decompositionMulti-step
RAG β Retrieval-augmented genKnowledge
HITL β Human-in-the-loopAll consequential
Tool Use β Function callingAll agents
π Temporal.io Orchestration
Active Workflows2,847
HITL Signals Pending47
Retry PolicyExp backoff Γ3
Saga PatternCompensating txns
Durable ExecutionCrash-safe β
π¨ Kafka Message Bus
Topics47 agent topics
Throughput12K msgs/s
Consumer Lag<100ms
Schema RegistryConfluent
Dead Letter QueueMonitored
π MCP Integration Layer
MCP β Data sourcesActive
MCP β CRM/ERPActive
MCP β Document storeActive
OAuth 2.0 authAll connectors
JSON Schema validationAll tools
Evaluation Framework β Continuous Quality Gates
0.94
Faithfulness
Gate β₯0.92 β
0.91
Answer Relevance
Gate β₯0.88 β
0.89
Context Precision
Gate β₯0.85 β
0.93
Context Recall
Gate β₯0.90 β
π§ͺ Eval Suite Composition
Golden dataset2,847 Q&A pairs
Unit evals (per agent)120β400 cases
Integration evals84 end-to-end flows
Adversarial probes47 jailbreak tests
LLM-as-judgeclaude-opus-4-5
Human eval cadenceWeekly 5% sample
π Eval-Driven Dev Flow
1
Change proposed β PR opened
Automated eval suite runs against golden dataset in CI. Results posted to PR.
2
RAGAS gate enforced
All metrics must meet thresholds. Failure blocks merge.
3
Canary deploy (5%)
Langfuse online evals on live traffic. Drift alerts trigger auto-rollback.
4
Full rollout + monitor
Weekly human eval sample. Monthly RAGAS full re-run.
Deploy K8s cluster. Provision Temporal.io, Kafka, PostgreSQL, Pinecone. Connect source systems via MCP. Establish data governance and RBAC. Run baseline eval on golden dataset.
2
Week 3β4: Core Agents Live
Deploy first 3 highest-value agents. Wire HITL approval workflows in Temporal. Configure NeMo guardrails and PII scrubbing. Set up Langfuse tracing and RAGAS eval gate.
3
Week 5β7: Full Agent Mesh
Deploy all agents. Configure Orchestrator routing. A/B test prompt variants. Enable drift detection. Train end-users on HITL workflow.
4
Week 8β10: Production Hardening
Pen test + SAST/DAST scan. Load test 10Γ baseline. Configure PagerDuty. Compliance review (GDPR, EU AI Act). Produce runbook. Go-live.
π 7-Layer Platform Stack
L7PresentationReact Β· Next.js Β· SSO
L6API GatewayFastAPI Β· OAuth2 Β· WAF
L5OrchestrationTemporal.io Β· LangGraph
L4Agent RuntimeNeMo Β· RAGAS Β· Tools
L3Model + ToolsClaude API Β· MCP servers
L2Data + IntegrationKafka Β· PostgreSQL Β· Redis
L1ObservabilityOTel Β· Langfuse Β· Grafana
π Integration How-To
MCP server per data source (REST/GraphQL/gRPC)
OAuth 2.0 service account per enterprise system
Kafka topics per agent capability namespace
Schema registry for typed message contracts
Data lineage via OpenLineage β Marquez
Webhooks for real-time event ingestion
dbt + Airflow for batch data refresh
π€ RBAC User Roles
ViewerRead dashboards
AnalystRun queries + export
ApproverHITL decisions
ManagerConfig + agents
AdminFull platform
AI EngineerModels + prompts
IdP via Okta/Azure AD. MFA enforced for Approver+.
π Incident Runbook
High latency (>5s): Check Langfuse trace β vector store β LLM API status
RAGAS gate fail: Roll back last prompt change β notify AI engineer
Error spike: Circuit breaker β fallback to previous version
PII leak: Suspend session β DPO notification within 24h