plant data for physical ai
Postgres-native infrastructure for PLC, SCADA, and historian data. Capture every tag at full fidelity. Query in standard SQL. Power the AI on your roadmap.
Plant floor
Streaming
+2.4 pts
Line A • 24h
30 days
vs. baseline
-0.8 pts
Line A yield
KwH/unit vs baseline
Cycle time
last 60 mins
ANOMALY • 14.23 • STATION 4 VIBRATION
// 2026 · THE READINESS GAP
The bottleneck to AI adoption is data infrastructure. You have to fix fragmented historians, vendor-locked formats, and cloud-only platforms that can't survive the plant floor.
of manufacturing in 2026
have the data structure to act
of plants stuck between curiosity and deployment. That gap is where Tiger Data lives.
[*] Redwood Software • 2026 Manufacturing Outlook Survey of 300 manufacturing leaders Read the report
// THE HISTORIAN PROBLEM
Traditional historians trap plant data in proprietary formats, cap retention at months instead of years, and force ETL before anything downstream can query it. The AI on your roadmap needs the opposite: full-fidelity history, open formats, and SQL-native access from line to enterprise.
TRADITIONAL HISTORIAN
Proprietary formats
Months of retention
ETL before query
Vendor lock-in
TIGER DATA
Postgres SQL open
Years of full-fidelity history
Query raw, in SQL
Line → plant → enterprise
The replacement
Tiger Data replaces the modern historian with PostgreSQL.
// chosen by the platforms that run the plant floor
The platforms actually running plant floors run on TimescaleDB
SIMATIC WINCC OA
Default time-series database
Default backend
SIEMENS ETM • SIMATIC WINCC OPEN ARCHITECTURE
WinCC OA 3.21, later this year
Tiger Cloud upgrade
CERN co-developed the NextGen Archiver on TimescaleDB
gold technology provider
gold technology provider
inductive automation • ignition
69% of Fortune 100 • 140+ countries
Modernize the industrial historian market
Joint guides • ICC 2026 in September
CERN runs TimescaleDB as the time-series backend for the NextGen Archiver on WinCC OA — the highest-stakes industrial telemetry workload on the planet.
// platform
Full fidelity capture
Sub-second tags retained for years. 10× compression.
Air-gap ready
Plant ops never depend on the cloud. OT stays in control.
Standard SQL
PostgreSQL across telemetry, events, MES, quality.
Line → plant → enterprise
Optional cloud sync for fleet OEE and benchmarking.
AI and agent-ready
Vector + hybrid search on contextualized history.
Chosen by Ignition and WinCC OA
Non-invasive. No rip-and-replace.
Full fidelity capture
Sub-second tags retained for years. 10× compression.
Air-gap ready
Plant ops never depend on the cloud. OT stays in control.
Standard SQL
PostgreSQL across telemetry, events, MES, quality.
Line → plant → enterprise
Optional cloud sync for fleet OEE and benchmarking.
AI and agent-ready
Vector + hybrid search on contextualized history.
Chosen by Ignition and WinCC OA
Non-invasive. No rip-and-replace.
reference architecture
where tiger sits
Tag_101=45.5
+ context
SQL • raw • years
fleet OEE
decisions
where tiger sits
Tiger sits where your historian sits today, with full SQL access for everything downstream
oee & downtime
quality & scrap
energy / unit
predictive maintenance
cross-line benchmarking
digital twins
agentic copilots
bi/lakehouse sync
// case studies
Three deployments. Three different industries. One common pattern: keep what works, query everything else.
by takton
MACHINE MONITORING
SMB MFG
challenge
Small and mid-size shops were locked out of machine monitoring by high costs, 10-device minimums, and complex deployment. InfluxDB couldn't handle high ingest across thousands of devices.
Solution
Built Sense Manufacturing on Tiger Cloud + AWS. Devices stream power and vibration every few seconds. Full-resolution telemetry in Tiger; relational data in Postgres — one SQL stack.
“We built around Tiger Data. It was a very key piece — definitely not an afterthought in terms of the architectural design.”
Outcomes
First commit → production
Lower TCO vs. competitor
avoided CNC rebuild
grupee
recycling
plant construction
challenge
As recycling plants grew, hundreds of machines and conveyors generated sensor data that legacy systems couldn't scale to monitor or analyze in real time.
Solution
Tiger ingests time-series data from machines across plants. Dashboards expose machine performance, enable predictive maintenance, and surface inefficiencies live.
“Tiger gives us the visibility to maintain our fleet proactively — across every plant, every machine, every shift.”
Outcomes
compression on raw sensor data
of machines, real-time
predictive maintenance
OPEN SOURCE STACK
SMART FACTORY
OPEN SOURCE
challenge
Their previous database struggled with slow queries, inefficient retention, and high maintenance overhead on IoT sensor data — blocking predictive analytics for their manufacturing customers.
Solution
Adopted TimescaleDB with continuous aggregates and automated retention policies. Chose it over InfluxDB specifically for predictive maintenance workloads.
“TimescaleDB let us scale predictive maintenance to more manufacturing clients without growing the ops team.”
Outcomes
queries → real-time analytics
via auto retention + caggs
predictive maintenance
Start with one line. Sit Tiger next to what you run today.
Joint reference architectures, Co-selling. Embedded options.
