Manufacturing
IoT + Data Agent

Your factory floor,
monitored by AI.

Connect sensors over MQTT. Ask questions in plain language. Get dashboards, alerts, and shift reports — without a SCADA license, a BI subscription, or a six-month MES project.

Try free with your data See what it does
The problem

Factories are drowning in data they can't use.

Sensors push thousands of readings per minute. Production logs pile up in spreadsheets. Quality records sit in binders. The data is there — but the tools to make sense of it cost six figures and take months to deploy.

01

SCADA shows, but doesn't think

Real-time screens are useful — until you need to ask "why did this happen?" SCADA shows the number. It doesn't analyze it.

02

MES is powerful, but rigid

Enterprise MES takes 3–6 months to implement, costs six figures, and every new report is an IT project. Most factories under 500 people can't justify it.

03

Excel is flexible, but manual

The plant manager exports data, pivots it, charts it, emails it. Every Monday. Every month. The same work, repeated forever.

How it works

From sensor to insight in three steps.

1

Connect

Devices push data over MQTT — the standard protocol your sensors already speak. Tablize stores every reading in TimescaleDB, a time-series database built for exactly this workload. Or upload CSV production logs, Excel quality records, or connect an existing database.

MQTT CSV / Excel PostgreSQL REST API Webhook
2

Ask

Ask any question about your factory data in plain language. The agent writes the SQL, runs the Python, generates the chart — and explains what it found. No query language to learn. No dashboard to configure.

"What was the OEE for Line A yesterday?" "Why did CNC-002 stop at 14:30?" "Which shift uses the most energy per unit?"
3

Keep

Every analysis becomes an asset you can reuse. Save it as a dashboard that refreshes automatically, a script that runs every Monday, a watch that fires when something goes wrong, or a report your team can reference.

Dashboard Watch / Alert Scheduled report Reusable script
In action

Two conversations. Two real outcomes.

These are the kinds of questions factory operators actually ask — and the kind of answers that used to require a BI team.

06:45 · Morning shift
James
Maintenance supervisor
at a precision parts factory

James manages 12 CNC machines across two lines. Last night's overnight watch caught a vibration anomaly on CNC-002. He opens Tablize before walking the floor to understand what happened.

What he keptA predictive maintenance watch that catches degradation 2 weeks before failure.
workshop-a · cnc monitoring
Wednesday · 06:45
James
CNC-002 had a vibration alert last night. What happened?
mqtt · 12 cnc devices · workshop-a/#
Tablize
✓ sql.query · vibration readings last 24h for cnc-002 186 ms
✓ python.analyze · trend analysis + 90-day baseline comparison 824 ms
CNC-002 vibration crossed 4.8 mm/s at 02:17 AM (threshold: 4.5 mm/s). It peaked at 5.1 mm/s and settled back to 4.6 mm/s by 04:00.
Current
4.6 mm/s
30d avg
3.2 mm/s
90d avg
2.8 mm/s
The 30-day trend shows a steady climb of ~0.05 mm/s per day. At this rate, it will cross the 5.0 mm/s maintenance threshold in approximately 8 days. This pattern matches spindle bearing wear — last replaced 14 months ago (recommended interval: 12 months).
08:30 · Monday review
Rachel
Plant manager
200-person auto parts factory

Rachel starts every Monday with an OEE review. She used to spend 45 minutes exporting data from three systems and building the same pivot table. Now the report is waiting when she opens Tablize.

What she keptA weekly OEE report that runs every Monday at 7 AM and lands in Slack.
plant-ops · weekly review
Monday · 08:30
Rachel
Show me last week's OEE by line and shift. What dragged us down?
production_log · 3 lines · 21 shifts
Tablize
✓ sql.query · production data by line × shift 312 ms
✓ python.analyze · OEE decomposition + stoppage Pareto 1.4 s
Overall plant OEE last week: 72.4% (target: 80%). Line B night shift pulled the average down significantly.
LineDay shiftNight shiftAvg
Line A81.2%76.8%79.0%
Line B74.5%58.3%66.4%
Line C78.9%73.1%76.0%
Line B night shift lost 3.2 hours to changeover delays (expected: 1.5h) and 1.8 hours to material shortage. Changeover is the #1 availability loss — accounting for 42% of total downtime across all lines.
Capabilities

Six things your factory needs.
All through conversation.

No modules to buy. No screens to configure. Tell the agent what you need — it sets up the monitoring, the analysis, and the automation.

01
Equipment monitoring

Every sensor, one conversation away.

Connect MQTT devices — temperature, vibration, current, pressure — and the agent watches them 24/7. No SCADA license. No HMI programming. One sentence sets up a threshold alert that fires to Slack, email, or webhook.

Try this prompt "Bind CNC-001 temperature sensor and alert me if it stays above 65°C for more than 10 minutes."
iot.bind_devicewatch.createnotify.slack
02
OEE analysis

Availability × Performance × Quality — computed, not configured.

Upload production logs or connect your MES export. The agent computes OEE by line, shift, and day — then drills into stoppage reasons, cycle time gaps, and yield loss. No predefined report templates. Ask the question you actually have.

Try this prompt "Compute OEE for Line A last week, broken down by shift. What was the biggest availability loss?"
sql.querypython.analyzeapp.dashboard
03
Quality control

SPC without the six-figure software.

The agent computes Cpk, Ppk, and control limits from your inspection data. Ask for an X-bar/R chart or a defect Pareto — it writes the Python, runs it, and renders the result. When a process drifts out of spec, a watch fires before the next batch ships.

Try this prompt "Calculate Cpk for the bore diameter measurements on Part A-200. Show me the control chart for the last 30 days."
python.analyzewatch.createreport.save
04
Energy management

Find the waste your utility bill hides.

Smart meters push data over MQTT. The agent computes consumption by machine, line, shift, or per-unit-produced. It spots anomalies — weekend draws that should be zero, compressors running during idle shifts — and alerts you before the bill arrives.

Try this prompt "Show me energy consumption per unit produced for each line this month. Flag any line where unit cost is 20% above average."
sql.querywatch.createapp.dashboard
05
Predictive maintenance

Catch failures while they are still trends.

Vibration climbing 2% per day is invisible on a dashboard. The agent spots it because it runs trend analysis on your time-series data and flags degradation curves before they cross the alarm threshold. Ask "how long until this fails?" and get an estimate grounded in your own history.

Try this prompt "Analyze the vibration trend for CNC-002 over the last 90 days. Is it degrading? How long before it crosses the 5mm/s alarm threshold?"
sql.querypython.analyzewatch.create
06
Automated reports

The shift report that writes itself.

Daily production summary, weekly OEE review, monthly energy report — configured in one conversation, delivered on schedule. The agent pulls the latest data, computes the metrics, writes the narrative, and sends it. No BI tool. No cron job you have to maintain.

Try this prompt "Every Monday at 7 AM, generate a production summary for last week: output by line, top 3 downtime reasons, OEE trend, and energy per unit. Send to the ops Slack channel."
script.savejob.schedulereport.save
How it
compares

Not a replacement for MES.
The analysis layer you never had.

Tablize doesn't schedule work orders or control PLCs. It monitors, analyzes, and reports — the part that traditional systems do worst.

Tablize Traditional MES SCADA / HMI Industrial BI
Setup time Same day 3–6 months 1–2 months 2–4 weeks
AI analysis Built in — — —
IoT / MQTT Native Proprietary Proprietary Limited
Custom reports One sentence IT project Fixed screens Drag-and-drop
Alerting Any SQL condition Preconfigured Tag-based Limited
Deployment Docker, one machine On-premise project Dedicated server Cloud or on-prem
Starting price $20/mo $100K+ $50K+ $20K+/yr
Where it fits

The AI analysis layer
between your devices
and your decisions.

Tablize doesn't replace your PLC, your SCADA, or your ERP. It sits on top — reading sensor data, production logs, and quality records — and gives you the analysis and automation those systems were never designed to do.

Tablize AI monitoring + analysis + automated reports + alerts
reads from
Your existing systems MQTT sensors, CSV exports, databases, cameras
controls
Physical equipment PLCs, actuators, machines — Tablize never touches these
Who it's for

If one of these sounds like you.

Factory owner 50–200 employees

No MES, no BI, no IT department. Equipment status lives in someone's head. Reports live in Excel. Tablize is the entire digital layer — deployed in a day, not a fiscal quarter.

Lean / CI engineer Any size plant

Your MES has the data but not the answers. You export to Excel, pivot, chart, repeat. Tablize sits on top of your existing data and gives you the analysis layer your MES never will.

Equipment OEM Selling to factories

You ship machines to dozens of customers and need a monitoring portal for each. Tablize is the white-label platform — connect MQTT, generate dashboards, deliver value without building your own software.

System integrator Delivering projects

Your projects need a data analysis layer and every customer wants it different. Tablize cuts your delivery time from months to weeks — connect, ask, ship.

Deployment

One machine. Five minutes. Done.

Tablize runs as a single binary inside Docker. No cluster. No microservices. No external dependencies beyond PostgreSQL. Deploy it on a server in your plant, behind your firewall, with your data never leaving the building.

Self-hosted

Docker Compose on any Linux server. Your machine, your data, your network. Air-gapped deployments supported.

Tablize Cloud

Fully managed. We run the infrastructure. You bring the MQTT connection and start asking questions.

Get started

Your sensors are already talking.
Start listening.

Upload a production log, connect an MQTT broker, or just drop a CSV of last month's downtime. Ask the question you'd normally spend a morning on. See what comes back in two minutes.

Try free with your data See pricing