Live OEE per machine
Availability × performance × quality, computed every shift from real PLC events. No more spreadsheet OEE.
Operator-tagged downtime · Loss Pareto · classification (world-class / good / fair / poor / critical)
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MAIO LABS
Turn machine data into actionable production intelligence.
WareAI connects to your PLCs and delivers real-time OEE visibility, AI-powered root cause analysis, and closed-loop corrective actions — so operators, supervisors, and plant managers make faster, better decisions.
Reduce downtime, increase throughput
Automatic loss categorization
AI that explains, not just alerts
- OEE Improvement
- 15-20%
- Downtime Reduction
- 25-40%
- Time to First Insight
- 5 days
- Production Visibility
- Real-time
The Platform
See your production floor at a glance
Every KPI, every machine, every alert — unified in one real-time dashboard. Operators, supervisors, and plant managers see what matters to them.
Real-time OEE
63.8%
Live calculation per machine
Active Alerts
11
Prioritized by severity
Role-Based Access
5 roles
Operator to executive
Historical Trends
24 records
Time-series analysis
localhost:5173
Connected
Live OEE: 63.8%
11 Active Alerts
Executive View
Live KPI Cards
OEE, Availability, Performance, Quality — updated every 3 seconds
Smart Alerts
Prioritized by severity, with context and suggested actions
Trend Analysis
Historical charts show patterns across shifts and days
Every role sees what they need: Operators get machine controls and downtime logging. Supervisors add production orders and work orders. Plant managers get integrations, asset configuration, and cross-line analytics.
Capabilities
Built for factories that demand reliability.
Every feature was designed for environments that can't tolerate data loss, security breaches, or AI that hallucinates root causes. Industrial-grade reliability meets modern intelligence.
Edge agent that survives WAN drops
Runs on a Raspberry Pi or industrial gateway. Buffers durably to SQLite, drains when the link returns.
MQTT + OPC-UA + Modbus · multi-broker failover · /metrics for Prometheus
AI Copilot that uses tools
"Why is press LC-01 down?" The model calls real DB tools, returns numbers grounded in your live UDM.
OpenAI-compatible gateway · per-tenant rate limit · prompt-injection guards · golden-Q&A eval suite
Multi-tenant by default
Application-layer tenant filter plus Postgres RLS plus a non-superuser app role. Three layers, one promise.
Cross-tenant isolation tests · audit log on every mutation · per-tenant LLM rate limits
21 CFR Part 11-ready audit trail
Every mutating call is logged. Rows are hash-chained and tamper-evident. E-signatures with re-auth.
GAMP 5 IQ/OQ/PQ scripts · controls-map for SOC 2 + ISO 27001
Bidirectional action engine
Close the loop: write back to PLCs, push maintenance to the ERP, trigger playbooks — all role-gated.
OPERATOR / LINE_MANAGER / FACTORY_MANAGER allowlists · denied actions audit-logged
Proven Mechanisms
How we actually achieve 15-20% OEE improvement
Not magic. Not promises. Six specific mechanisms that change operator and supervisor behavior.
OEE Gain
5-8%
Visibility eliminates hidden losses
Before
Operators don't notice 45-minute material delays or slow cycle times. 2-3 hours/day vanish.
After
Real-time dashboard shows: "Material delay - 45 min" → supervisor fixes supply chain issue
Hidden losses become visible → teams fix them immediately
OEE Gain
3-5%
Prioritization via Pareto
Before
Supervisor tackles 20 small problems, ignores big ones. Lots of effort, minimal impact.
After
Loss Pareto shows: "Changeovers = 40% of losses" → focus there, implement quick-change tooling
Effort focused on highest-impact issues → bigger gains per hour invested
OEE Gain
2-4%
Faster response to alarms
Before
Alarm at 9:00 AM, operator notices at 9:20 AM, maintenance arrives 9:40 AM. 40 minutes lost.
After
Alert fires instantly → operator gets push notification → checks dashboard → fixes in 2 minutes
Detection time drops from 20 minutes to 5 seconds → downtime cut 95%
OEE Gain
2-3%
Root cause prevents recurrence
Before
Press stops 3x/week. Each time: reset and resume. Nobody investigates. Problem repeats forever.
After
AI identifies pattern: "Stops when temp > 180°C → cooling system issue" → fix once, done
Recurring issues eliminated → compound effect over time
OEE Gain
1-2%
Perfect shift handover
Before
Night shift: "Line 3 was weird." Day shift: "What does that mean?" 30 min wasted investigating.
After
Day shift sees full context: "Welder W-07: 2 alarms overnight, tool changed, running normal"
Zero information loss between shifts → no redundant troubleshooting
OEE Gain
2-4%
Quality caught early
Before
Defect found 4 hours later. 200 units scrapped. 16 hours of production wasted.
After
Real-time SPC alerts: "Temperature out of control" → operator adjusts → only 10 units scrapped
Catch quality issues in minutes not hours → scrap reduced 15-25%
Cumulative Impact
Typical Starting Point
62% OEE
Manufacturing average (ISA-95 benchmark)
After 3-6 Months
75-80% OEE
+13-18 points = 21-29% relative improvement
It's not the software that improves OEE. It's operators responding 10x faster, supervisors prioritizing correctly, and plant managers fixing root causes instead of symptoms. WareAI just makes that possible.
Impact
Real factories. Real results.
Six scenarios where WareAI transformed operations — focused on outcomes that matter.
Mid-Market Manufacturer
50-100 machines
Challenge
OEE tracked manually on Excel. No visibility into losses. Supervisors can't prioritize improvements.
Solution
Real-time OEE per machine, automatic loss categorization, Pareto analysis showing top 3 issues
Outcome
OEE improved 60% → 78%, reduced unplanned downtime 40%, supervisors focus on highest-impact losses
System Integrator
20+ factory clients
Challenge
Clients demand OEE monitoring. Building custom solutions for each. No reusable platform.
Solution
White-label WareAI, customize per client, deploy as own branded product, multi-tenant architecture
Outcome
Deployed to 12 clients in 6 months, differentiated offering, recurring revenue, own the IP
Private Equity Roll-Up
10 plants in portfolio
Challenge
Each plant reports OEE differently. No standardized KPIs. Can't benchmark performance.
Solution
Single platform across all plants, unified KPIs, cross-site benchmarking, portfolio dashboard
Outcome
Identified bottom 3 performers, implemented best practices, lifted portfolio OEE 8 points
Tier 1 Auto Supplier
ISO/IATF certified
Challenge
Manual OEE tracking creates audit risk. No traceability. Quality data in spreadsheets.
Solution
Automated data collection, tamper-evident audit trail, e-signatures, real-time SPC charts
Outcome
Passed ISO audit, quality issues detected 2x faster, reduced scrap 15%, audit prep time cut 80%
Greenfield Factory
New $100M plant
Challenge
Building smart factory from ground up. Need OEE monitoring on day 1. No legacy to migrate.
Solution
Integrated into factory design, connected during commissioning, operators trained before go-live
Outcome
Hit 75% OEE target in first month (vs typical 6-12 months), avoided ramp-up issues
Quality-Driven Manufacturer
High-mix, low-volume
Challenge
Quality issues detected hours after production. Manual SPC tracking. No real-time correlation.
Solution
Real-time quality readings, automatic SPC calculations, process variable correlation, instant alerts
Outcome
Defect detection time: 4 hours → 5 minutes, scrap reduced 22%, customer complaints down 60%
What Customers Achieve
15-20%
Typical OEE improvement
25-40%
Downtime reduction
2-4 weeks
Time to measurable impact
Operators stop guessing
AI explains root causes, suggests fixes
Supervisors prioritize better
Loss Pareto shows highest-impact issues
Plant managers see trends
Week-over-week, shift patterns, seasonal
Quality caught early
Hours → minutes, scrap reduced 15-25%
Architecture
Edge buffers. Cloud reasons. Operators answer.
Step 1 · Factory floor
PLCs → edge gateway
A small container on a Pi or industrial PC subscribes to MQTT, polls OPC-UA, reads Modbus registers. Normalises to UDM, writes to a WAL-mode SQLite buffer.
- · Survives WAN drops up to days
- · Multi-broker MQTT failover
- · /metrics for Prometheus
Step 2 · Cloud
Ingestion → DB → KPIs
Cloud receives batches over HTTPS, dedupes by external_id, persists to Postgres + TimescaleDB. Rules fire, alerts go out. Audit log is hash-chained.
- · Postgres RLS + non-superuser app role
- · OpenTelemetry tracing
- · Helm + Terraform for AWS / EKS
Step 3 · Humans
Dashboard + AI Copilot
Operators tag downtime, supervisors close work orders, plant managers ask the Copilot "why is yield down on line 3?" and get an answer grounded in real numbers.
- · OpenAI-compatible LLM gateway
- · Tool-calling over your live UDM
- · Per-tenant rate limit + prompt-injection guards
PLCs ──MQTT/OPC-UA/Modbus──▶ edge agent ──HTTPS──▶ cloud WareAI
(SQLite WAL buffer) Postgres + KPIs + Copilot Compliance
Pre-answers half your audit questionnaire.
Regulated factories (pharma / GxP, food safety, automotive Tier 1) have a security + validation pack their procurement team will send within an hour of "we want to pilot this." We ship the artifacts so your reply takes 30 minutes, not 30 days.
Tenant isolation
Application-layer filter + Postgres RLS + non-superuser app role bootstrap. Cross-tenant tests on every PR.
Hash-chained audit log
Every mutating call writes an immutable, tamper-evident row. Verifier endpoint runs on a cron, ships results to your sink.
21 CFR Part 11-aligned e-signatures
Re-auth via password or fresh TOTP on controlled actions. Stored intent statement; FK-linked into the audit chain.
GAMP 5 templates
IQ / OQ / PQ test scripts mapped to specific WareAI features. Traceability matrix template + change-control flow.
SOC 2 / ISO 27001 controls map
A 21-row table mapping each Trust Service Criterion / Annex A control to the file or endpoint that implements it.
SBOM in CI
CycloneDX bill of materials generated on every release. Attached to GitHub Releases for vendor questionnaires.
Not yet shipped (clearly listed in the install guide): SOC 2 / ISO 27001 audit reports, formal pen-test, validated reference installations in pharma. We won't claim certifications we haven't earned. Talk to us about timeline if you need them.
See your factory's data in WareAI.
30-minute demo with your specific scenario. We'll show you live OEE dashboards, loss analysis, and AI-powered insights using data that looks like yours. Walk away understanding exactly how WareAI improves your operations.
Operators
Know why machines stopped, resume faster
Supervisors
Prioritize losses, track orders, manage quality
Plant Managers
Cross-line visibility, trend analysis, proactive alerts
Self-host preferred? Helm chart + Terraform AWS scaffold + edge-agent Docker image are all open. Read the factory install guide.