ESG & SustainabilityScope 3 emissions Β· all tiers
π‘ Live Intelligence Feed
Real-time AI activity Β· all agents
Why SupplyChainOS
π Visibility: Blind Beyond Tier 1
90% of supply chain risk sits in Tier 2 and 3 β invisible to traditional monitoring. SupplyChainOS maps the extended supply chain and monitors sub-tier suppliers using satellite, news, and financial signals.
π Disruption: Zero Warning Time
Supply disruptions are discovered when shelves are empty. AI detects geopolitical events, weather, and financial distress 4β8 weeks before operational impact β time to act, not react.
π¦ Inventory: Too Much and Too Little
Simultaneously carrying 30% excess in slow movers and suffering stockouts on fast movers. AI demand sensing resolves both β β28% working capital, 97.5% service level maintained.
Supply Chain Mapping builds a complete graph of the extended supply chain β not just Tier 1 suppliers, but Tier 2, Tier 3, and beyond. Using trade data, corporate filings, and network analysis, the system identifies which sub-suppliers your Tier 1 suppliers depend on β and maps the geographic concentration, financial health, and single-source dependencies across the whole network. During COVID, 70% of companies discovered critical Tier 2 dependencies only when production stopped. SupplyChainOS identifies these dependencies proactively β giving procurement teams the visibility to diversify or de-risk before disruption strikes. All supplier relationship decisions remain with the procurement team.
Disruption Warnings (30d)
12
6-week advance notice
Disruptions Avoided
8
Alternative sourcing
Cost Avoidance
Β£2.4M
This quarter
Warning Accuracy
84%
Confirmed disruptions
π Disruption Intelligence
Disruption Intelligence monitors geopolitical events, weather patterns, port congestion data, financial distress signals, and industrial action news across the supply chain network. For each signal, the system assesses which suppliers are affected, which of your materials or components flow through those suppliers, and what the lead time impact would be. Average warning time: 6 weeks before operational impact. This gives procurement teams time to: activate alternative suppliers, pull forward orders, adjust safety stock, or communicate with customers about potential delays. All sourcing decisions triggered by disruption intelligence require procurement manager approval.
SKUs Optimised
847
Demand sensing live
Safety Stock Reduction
β28%
WC released
Service Level
97.5%
Maintained or improved
Working Capital Released
Β£284K
This quarter
π¦ Demand Sensing & Inventory Optimisation
Demand Sensing AI integrates real-time signals β POS data, weather, social trends, economic indicators, and customer order patterns β to update demand forecasts continuously rather than in weekly batches. The result is a 34% reduction in forecast error, enabling safety stock levels to be reduced by 28% without degrading service levels. Fast-moving items maintain 97.5% service level at lower stock investment. Slow-moving items are identified earlier, enabling proactive markdown or return-to-vendor decisions before they become deadstock. All inventory parameter changes are recommendations β supply chain planners review and approve before system updates.
Commodities Tracked
500+
Market price live
Savings vs Market
Β£840K
YTD procurement
Contracts Monitored
284
Compliance tracking
Forward Buy Opportunities
7
CFO review
π° Procurement Intelligence
Procurement Intelligence provides real-time market price tracking for 500+ commodities and raw materials β benchmarking every active supplier contract against current market rates. When a supplier's price is above market, the system surfaces the gap with supporting market data for the buyer's next negotiation conversation. Forward buying recommendations: when commodity price forecasting models identify a buying window (price below 3-month trend with upside signal), the system flags this to procurement leadership. All forward buying decisions require CFO and procurement director approval β AI provides the market intelligence, treasury makes financial commitments.
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