Industrial AI — agent development for every industry.
We design, build and operate AI agents that work on your live plant data — purpose-built for your industry, your assets and your decisions. Generative AI where it earns its keep: on the shop floor.
Measurable gains for industry.
Your data, wherever it lives.
Invansys industrial AI agents are vendor-agnostic — they connect to the systems you already run, on the edge, on the plant floor and in the enterprise. No rip-and-replace.
Canary Time-Series
Native access to the Canary historian via Web / Read API — live and historical tags.
OPC UA / OPC DA
Standards-based access to controllers, gateways and SCADA across the plant.
DCS Systems
Integration with all major distributed control systems across process industry.
PLCs
All leading PLC families and Modbus-capable controllers.
MQTT / Sparkplug B
Lightweight edge and IIoT telemetry from remote and distributed assets.
SQL / REST / CSV
Relational databases, web services and flat files — batch or streaming.
Cloud IoT
AWS IoT and Azure IoT pipelines — for hybrid and multi-site estates.
SCADA & more
SCADA sources, OSI/AVEVA PI and other historians via open interfaces.
How an industrial AI agent is built.
A clean pipeline from raw signals to decisions — deployed where your security posture requires.
Data sources
- Canary time-series
- OPC UA / DA
- DCS & PLC
- MQTT / Sparkplug B
- SQL / REST / cloud
Ingest & normalize
- Unified tag model
- Alignment & resampling
- Quality & gap handling
- Context enrichment
AI agent
- Anomaly detection
- Predictive maintenance / RUL
- Generative-AI insights
- Continuous scoring
Act & visualize
- Writeback as historian tags
- Axiom dashboards
- Alerts: Teams / email / CMMS
- Reports & audit trail
Your data. Your infrastructure. Your terms.
On-premise, private cloud or hybrid — you choose how and where your industrial AI runs. Your operational data never has to leave your plant, you own your deployment and your data, and open access means you are never locked in.
Data sovereignty & privacy
Deploy fully on-premise or air-gapped. Your process data stays within your walls — critical for nuclear, defence and regulated plants.
You own your data
Historian archives and AI results are yours, in open formats with API, ODBC and export access. No proprietary trap, no data held to ransom.
You choose the model
Owned/perpetual or subscription — whichever suits your budget and policy. No one-size-fits-all SaaS mandate, no lock-in by default.
Typical SaaS-only analytics
- Your data leaves the site to a third-party cloud
- Rented access — stop paying, lose the platform
- Proprietary formats make leaving costly
- Provider controls uptime, updates and roadmap
- Recurring cost with no ownership at the end
The Invansys way
- Data stays on your infrastructure — on-prem or your cloud
- You own the deployment; it keeps running on your terms
- Open access: API, ODBC, export — no format lock-in
- You control uptime, security posture and upgrade timing
- Choose owned or subscription — whatever fits
How anomaly detection works.
No black box — a transparent, four-step loop that runs continuously on your live data.
Learn normal
Unsupervised models learn each asset's healthy operating envelope from its own historical data — no manual thresholds.
Score live data
Every reading is scored against that baseline in real time, producing a continuous health score per asset.
Write back & alert
Scores are written back as first-class historian tags and surfaced in Axiom; degradation triggers alerts to the right people.
Act early
Teams intervene weeks before failure, with remaining-useful-life estimates guiding maintenance planning.
Demonstrable today
Our reference agent already runs live: a Python agent reads data through the Canary Web Read API, scores equipment health with isolation-forest models, and writes results back as tags that trend natively in Axiom alongside raw process data. Ask us for a live walkthrough.
What an industrial AI agent does
- Watches live historian data continuously — no sampling, no batch reports
- Detects anomalies, scores risk and predicts failures on your assets
- Answers plain-language questions about plant state, powered by generative AI
- Writes results back as historian tags — visible in the dashboards you already use
- Escalates to people via email, Teams or CMMS only when it matters
How we deliver
- Discovery: your assets, failure modes and decision points
- Agent design on your own historical data — vendor-agnostic sources
- Pilot on live data with measurable success criteria
- Production rollout, cloud or fully on-premise, with lifecycle support
From "something's off" to "here's what, and when."
Anomaly detection tells you an asset is deviating. Predictive analytics estimates how long you have; fault diagnosis points to the likely cause — so maintenance is planned, not reactive.
How long do I have?
- Remaining-useful-life (RUL) estimation for critical assets
- Failure-probability and time-to-failure trends
- Lead-time-to-failure alerts, days to weeks in advance
- Feeds maintenance planning and spares readiness
What's wrong, and why?
- Fault classification per asset type from data signatures
- Root-cause candidate ranking to speed investigation
- Severity scoring to prioritise the right work first
- Findings written back as tags and surfaced in Axiom
Faults we model, and the signatures they leave.
Representative failure modes by equipment type — each with the data pattern our models watch for.
Rotating — Pumps & Compressors
Heat Exchangers & Cooling
Motors & Drives
Valves & Actuators
Compressed Air & Utilities
Process & Thermal
An agent for every industry.
Asset Integrity Agent
Compressor, pump and pipeline health scoring with early-warning escalation and RUL estimates.
Performance Agent
Heat-rate and PR deviation detection, soiling and degradation analytics, dispatch-ready summaries.
Batch Review Agent
Golden-batch comparison, excursion detection and review-by-exception documentation support.
Kiln & Mill Agent
Energy-per-tonne optimisation, refractory risk indicators and quality-drift alerts.
Process Stability Agent
Multivariate drift detection across units with root-cause candidate ranking.
Network Agent
Pump efficiency, leakage signatures and remote-station health across the network.