Overall OEE

83.4%

↑18pts from AI baseline

Critical Asset Alerts

Maintenance scheduled

First Pass Yield

96.8%

Vision AI 100% inspection

Downtime Prevented

−34%

YTD vs pre-AI

🤖 Agent Status

Real-time across all AI capabilities

Predictive Maintenance2 failures predicted · 18d

Vision AI Quality2,847 units · 100% inspected

Production SchedulingOEE 83.4% · target 85%

SPC Process ControlAll lines in control

Energy Optimisation−22% vs baseline

QMS ComplianceISO 9001 · all met

📡 Live Intelligence Feed

Real-time AI activity · all agents

Why ManufacturingOS

🔧 Unplanned Downtime: £260B Problem

Predictive Maintenance analyses sensor fusion data to predict failures 2–4 weeks ahead — scheduling during planned windows, not emergencies. −34% unplanned downtime YTD.

🔍 Quality: 5–8% Revenue in Defects

Vision AI inspects every unit at production speed. 92% accuracy vs 74% manual. First pass yield: 96.8% vs 87% pre-AI. Rework and warranty cost −45%.

📊 OEE: Average Plant Runs at 65%

Production Scheduling AI closes the gap between 65% and 85%+ world-class through constraint optimisation and real-time re-scheduling.

All AI Agents

🔮

Predictive Maintenance

Sensor fusion: vibration, temp, acoustic, electrical. Failure prediction 2–4 weeks ahead. RUL per asset. Maintenance scheduling optimisation.

195 assets monitored

ReAct + Sensor Fusion

🔍

Vision AI Quality Control

100% visual inspection at speed. Surface defects, dimensional errors, assembly faults. 92% accuracy vs 74% manual.

2,847 inspected today

Reflection + Vision

📅

Production Scheduling

Constraint-based scheduling: throughput, changeover, resources. Real-time re-scheduling. Avg changeover −34 min.

6 lines

Planning + Constraints

📊

OEE Monitor

Availability, Performance, Quality decomposition. Micro-stop detection. Speed loss. Downtime cause classification.

OEE 83.4%

ReAct + Classification

📈

SPC Process Control

Statistical process control on all critical parameters. Control chart alerts before defects occur. Cpk/Ppk tracked live.

All in control

Sequential + Stats

⚡

Energy Intelligence

Equipment energy profiling, anomaly detection, demand response. −22% energy cost. Carbon reporting.

−22% cost

Planning + Optimisation

🔗

Supply Chain AI

Raw material inventory, supplier lead times, disruption detection 4–6 weeks ahead.

47 suppliers

ReAct + Forecasting

📋

QMS Compliance

ISO 9001 / IATF 16949 evidence. NCR + CAPA management. Customer audit packs auto-generated.

All compliant

Sequential + Evidence

🏭

Root Cause AI

Correlates defect patterns with machine, material, shift, environment. Recommends corrective actions.

7 alerts active

Reflection + Correlation

Assets Monitored

195

All production lines

Failures Predicted (30d)

Schedule prevention

Downtime Prevented

−34%

YTD vs pre-AI baseline

PM Adherence

94%

On-time planned maintenance

🔮 Predictive Model — Line 3 Pump

Bearing failure prediction · sensor fusion

INGEST  → Vibration 12.4mm/s · Temp 96°C · Acoustic
TREND   → Bearing degradation: 8 weeks progressive
FAILURE → Mode: spalling · P50: 21d · P90: 12d
WINDOW  → Next PM: Wed 02:00 · 4h · within plan
PARTS   → SKF 6308-2RS1 × 2 · Location B-14 ✓
RECMD   → Schedule in window · Level 2 tech

📅 30-Day Maintenance Calendar

AI-optimised · all within planned windows

18 Jun

L3-PUMP-A · Bearing replacement · 4h

CRITICAL

22 Jun

L5-OVEN-01 · Scheduled PM · 6h

PLANNED

01 Jul

L1-MOTOR-B4 · Inspection · 2h

WARNING

Units Inspected Today

2,847

100% — every unit

First Pass Yield

96.8%

↑9.8pts from AI

Defects Caught

Before reaching customer

Detection Accuracy

92%

vs 74% manual

🔍 Defect Classification

Vision AI · today · all lines

Surface scratches18 unitsL3 · Fixture wear

Dimensional error12 unitsL1 · Tool wear

Assembly miss9 unitsL2 · Feeder jam

Weld porosity8 unitsL6 · Gas mix

Passed inspection2,800 units98.3% pass

📈 SPC — Line 1 Critical Dimension

Bore diameter · Target 47.00mm ±0.05mm

Cpk (Capability)1.47 ✓

Process Mean47.002mm ✓

UCL / LCL47.038 / 46.962 ✓

StatusIN CONTROL ✓

Overall OEE

83.4%

Target: 85%

Availability

94.2%

Uptime

Performance

91.1%

Speed rate

Quality Rate

97.1%

First pass

📊 OEE by Line

Today · all shifts

Line 1 — Machining87.2%

Line 2 — Assembly84.7%

Line 3 — Finishing78.1%

Line 4 — Packaging89.4%

Line 5 — Testing91.2%

Line 6 — Welding69.8%

📉 Top OEE Loss Drivers

AI root cause · this week

Changeover L6 (−9.2pts): Avg 84min vs benchmark 47min. SMED workshop + pre-staging recommended.

Micro-stops L3 (−5.1pts): 47 micro-stops this week. Jig wear F-12. Replace fixture: £840 cost → £12K/wk recovery.

Speed Loss all lines (−3.2pts): Running 94.1% rated speed. Tool wear primary driver. Adjust change schedule.

Energy Cost Reduction

−22%

vs baseline

Monthly Saving

£47K

All lines

Carbon Reduction

−18%

Scope 1 & 2

Anomalies Found

This month

⚡ Energy Intelligence

Energy Intelligence provides three saving categories: (1) Anomaly detection — equipment consuming above expected for its operating state. This month: Line 4 compressor 34% above baseline due to air leak. Corrected: £8K/month saved. (2) Demand response — compressors, HVAC, and lighting rescheduled to off-peak tariff periods. (3) Scheduling — energy-intensive processes moved away from peak demand windows (16:00–19:00). Combined: £47K/month, −22% energy cost, −18% Scope 1/2 carbon. SECR and ISO 50001 reports generated automatically from live consumption data.

ISO 9001 Compliance

100%

All clauses

Open NCRs

CAPA in progress

CAPA On-Time Rate

94%

vs 67% pre-AI

Audit Readiness

Live

Always current

📋 Quality Management System

QMS Agent manages the complete quality management system. NCRs are automatically created from Vision AI defect data, SPC out-of-control signals, and incoming inspection failures. Each NCR links to a CAPA with deadline and owner. CAPA effectiveness is verified by monitoring defect recurrence over 30 days. Customer audit packs are auto-generated: PPAP documentation, first article inspection records, control plans, FMEAs, and capability studies. Warranty cost is tracked back to production batch, line, shift, and root cause — closing the feedback loop between field quality and production control.

📡 Live Agent Trace

All decisions logged · full audit trail

🛡 AI Governance

Advisory intelligence — humans decide

No autonomous consequential decisions: All significant actions require human approval. AI recommends — 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.

AgentOps — Live Agent Observability

📡 Live Trace Feed

📊 Session Metrics (24h)

Total Sessions2,847

Avg Latency1.4s

P95 Latency3.1s

Error Rate0.3%

Tool Calls12,284

HITL Escalations47

RAGAS GatePASS ✓

💰 Cost & Tokens

Cost (24h)£847

Input Tokens48.2M

Output Tokens12.4M

Cache Hit Rate67%

Cost/Session£0.30

🎯 RAGAS Quality Scores

Faithfulness0.94 ✓

Answer Relevance0.91 ✓

Context Precision0.89 ✓

Context Recall0.93 ✓

Hallucination Rate0.8%

🤖 Agent Health

All agentsHealthy

OrchestratorActive

Tool registryOnline

MCP serversConnected

Memory storeHealthy

MLOps / LLMOps — Model Lifecycle

🧠 Model Registry

claude-sonnet-4-5 PRODUCTIONPrimary

claude-haiku-4-5 ROUTINGFast path

claude-opus-4-5 SHADOWComplex

text-embedding-3-large RAGVectors

Automatic fallback routing. Versioned in MLflow. Prompt changes require RAGAS eval gate pass.

📈 Drift Detection

Faithfulness drift (7d)+0.02 stable

Latency drift (7d)+120ms watch

Output length driftWithin ±5%

Sentiment driftNo anomaly

Alert thresholdΔ>0.05 → PagerDuty

🔀 A/B Experiment Controller

Prompt v2.3 vs v2.4Running

CoT vs DirectStaging

Statistical significance (p<0.05) required before promotion.

🏪 Feature Store

Vector IndexPinecone

Dimensions3,072

Indexed Docs284K

Retrieval P9542ms

📦 Prompt Version Control

System promptsGit-tracked

Few-shot examplesVersioned

Eval datasetsDVC tracked

DevSecOps — Security-First CI/CD Pipeline

🚀 CI/CD Pipeline

🔍SAST — Semgrep + BanditPASS

📦SCA — SBOM + TrivyPASS

🧪Unit + Integration tests847/847

🎯RAGAS eval gate (≥0.92)0.94 ✓

🔐Secrets scan — GitleaksCLEAN

🐳Container scan — Grype0 CRITICAL

🚢Deploy → KubernetesDEPLOYED

🔐 Security Posture

RBAC — Role-based accessEnforced

API keys — HashiCorp VaultRotated 30d

mTLS — Istio service meshActive

PII scrubbing — NeMoActive

Audit log — ImmutableCloudWatch

Pen testQuarterly

SOC 2 Type IIIn progress

ISO 27001Compliant

🏗 Infrastructure as Code

TerraformCloud infra

HelmK8s workloads

ArgoCD GitOpsSynced

Kustomize overlaysdev/stg/prd

♻️ Rollback & DR

RTO Target<15 min

RPO Target<5 min

Blue/Green DeployActive

Auto-rollbackError rate >1%

📋 Regulatory Compliance

GDPR Art. 22 HITLEnforced

EU AI Act Art. 9Documented

NIST AI RMFMapped

ISO/IEC 42001Compliant

AI Observability — OpenTelemetry + Langfuse

🔭 Observability Stack

L1TracesOpenTelemetry → Jaeger

L2MetricsPrometheus → Grafana

L3LLM TracesLangfuse (self-hosted)

L4LogsFluentd → OpenSearch

L5AlertsAlertManager → PagerDuty

📊 SLO Dashboard

Availability SLO99.9% target

Current (30d)99.96%

Error Budget73% remain

P50 Response0.8s

P95 Response3.1s

P99 Response7.4s

🚨 Active Alerts

Latency P95Normal

Error rate0.3% ✓

Token budget84% remain

RAG recall0.93 ✓

Latency drift+120ms watch

🔬 Langfuse Trace Explorer

📈 Avg Span Breakdown

API Gateway12ms

Auth + RBAC8ms

RAG retrieval42ms

Guardrail check18ms

LLM inference1,240ms

Tool execution84ms

Total E2E1,452ms

Guardrails — Responsible AI Framework

🛡 NeMo Guardrails — Active Rails

✅ Human-in-the-Loop (HITL) Gate

All consequential actions require human approval before execution. Confidence <0.85 always escalates. GDPR Article 22 compliant — no fully automated consequential decisions.

🔍 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.

🚫 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.

⏱ Rate Limiting & Abuse Prevention

Per-user token budgets at API gateway. 10× anomalous usage triggers suspension + security alert. Cloudflare WAF DDoS protection.

📋 Audit Trail & Explainability

📝 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.

🔎 Explainability (XAI)

Every recommendation includes source citations, confidence intervals, alternatives considered, and limitation disclosures. SHAP attribution for structured ML models.

⚖️ Bias Monitoring

Fairness metrics tracked across protected characteristics. Disparate impact analysis monthly. EU AI Act Article 10 data governance requirements met.

🏛 Regulatory Mapping

GDPR Art. 5/22 · EU AI Act Art. 9/10/13/14 · NIST AI RMF · ISO/IEC 42001 · IEEE 7001 Transparency. Compliance evidence pack generated quarterly.

0.3%

Hallucination Rate

Target <2%

100%

HITL Coverage

Consequential acts

PII Leaks (30d)

Target: 0

A+

Security Grade

Mozilla Observatory

Multi-Agent Architecture — Mesh & Orchestration

🕸 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.

⚙️ Agent Patterns

ReAct — Reason + Act loopsAnalytical

Reflection — Self-critique cyclesHigh-stakes

Planning — Hierarchical decompositionMulti-step

RAG — Retrieval-augmented genKnowledge

HITL — Human-in-the-loopAll consequential

Tool Use — Function callingAll agents

🔄 Temporal.io Orchestration

Active Workflows2,847

HITL Signals Pending47

Retry PolicyExp backoff ×3

Saga PatternCompensating txns

Durable ExecutionCrash-safe ✓

📨 Kafka Message Bus

Topics47 agent topics

Throughput12K msgs/s

Consumer Lag<100ms

Schema RegistryConfluent

Dead Letter QueueMonitored

🔌 MCP Integration Layer

MCP — Data sourcesActive

MCP — CRM/ERPActive

MCP — Document storeActive

OAuth 2.0 authAll connectors

JSON Schema validationAll tools

Evaluation Framework — Continuous Quality Gates

0.94

Faithfulness

Gate ≥0.92 ✓

0.91

Answer Relevance

Gate ≥0.88 ✓

0.89

Context Precision

Gate ≥0.85 ✓

0.93

Context Recall

Gate ≥0.90 ✓

🧪 Eval Suite Composition

Golden dataset2,847 Q&A pairs

Unit evals (per agent)120–400 cases

Integration evals84 end-to-end flows

Adversarial probes47 jailbreak tests

LLM-as-judgeclaude-opus-4-5

Human eval cadenceWeekly 5% sample

🔁 Eval-Driven Dev Flow

Change proposed → PR opened

Automated eval suite runs against golden dataset in CI. Results posted to PR.

RAGAS gate enforced

All metrics must meet thresholds. Failure blocks merge.

Canary deploy (5%)

Langfuse online evals on live traffic. Drift alerts trigger auto-rollback.

Full rollout + monitor

Weekly human eval sample. Monthly RAGAS full re-run.

Infrastructure — Kubernetes · Scale · Resilience

☸️ Kubernetes Cluster

ClusterEKS / GKE / AKS

Node pools3 (system · app · GPU)

HPA targetCPU 70% → scale

KEDA triggersKafka consumer lag

Spot instances80% non-critical

Multi-AZ3 zones

💾 Data Architecture

PostgreSQL (RDS)Operational

Redis (ElastiCache)Session + cache

Pinecone / pgvectorVector search

S3 Intelligent TierDocuments

Kafka (MSK)Event streaming

Snowflake / BigQueryAnalytics DWH

💰 Cost Architecture

LLM API (Anthropic)~45% of AI cost

Vector DB~12% of AI cost

Compute (K8s)~28% of AI cost

Prompt cache savings−67% input tokens

Haiku fast-path saving−40% LLM spend

Est. monthly total£8–28K

🔁 Disaster Recovery

Primary failure detected (<2 min)

Route53 health check fails → DNS failover. Temporal promotes standby. Kafka MirrorMaker live.

DR validates (<5 min)

Smoke tests auto-run. PagerDuty alert to on-call. RTO target: 15 minutes.

Data reconciled (<15 min)

PostgreSQL read replica promoted. S3 cross-region lag <5min. RPO: 5 minutes.

📊 Capacity Planning

Baseline: 3 app nodes · 2 vCPU · 8GB RAM each
Scale trigger: Kafka consumer lag >10K msgs
Max scale: 20 nodes via KEDA + HPA
LLM concurrency: 50 parallel sessions managed
Vector search: Pinecone p1 → p2 at 500K docs
DB connections: PgBouncer pool (max 500)

Documentation — Deployment Guide & Runbook

🚀 10-Week Deployment Guide

Week 1–2: Data Foundation & Infrastructure

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.

Week 3–4: 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.

Week 5–7: Full Agent Mesh

Deploy all agents. Configure Orchestrator routing. A/B test prompt variants. Enable drift detection. Train end-users on HITL workflow.

Week 8–10: Production Hardening

Pen test + SAST/DAST scan. Load test 10× baseline. Configure PagerDuty. Compliance review (GDPR, EU AI Act). Produce runbook. Go-live.

🏗 7-Layer Platform Stack

L7PresentationReact · Next.js · SSO

L6API GatewayFastAPI · OAuth2 · WAF

L5OrchestrationTemporal.io · LangGraph

L4Agent RuntimeNeMo · RAGAS · Tools

L3Model + ToolsClaude API · MCP servers

L2Data + IntegrationKafka · PostgreSQL · Redis

L1ObservabilityOTel · Langfuse · Grafana

🔌 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 → Marquez
Webhooks for real-time event ingestion
dbt + Airflow for batch data refresh

👤 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+.

📞 Incident Runbook

High latency (>5s): Check Langfuse trace → vector store → LLM API status
RAGAS gate fail: Roll back last prompt change → notify AI engineer
Error spike: Circuit breaker → fallback to previous version
PII leak: Suspend session → DPO notification within 24h
HITL queue backup: Escalate to senior approver
Cost overrun: Auto-throttle → route to Haiku

ManufacturingOS: Agentic AI for Manufacturing