DataWise: Your Autonomous Industrial Data Quality System.

DataWise uses self-supervised machine learning to monitor industrial time-series data continuously. For every tag you ingest, DataWise automatically creates a set of small, specialised micro-models that learn what “normal” behaviour looks like for that specific signal.

Each micromodel watches for a specific kind of failure. Our proprietary detection engines run in parallel on every tag: bad values, no data, out of range, flat line, outlier, abrupt changes, noise distribution, sample rate and more. A separate multi-tag engine watches for correlation breaking – when signals that normally move together stop doing so. Because the models are self-supervised and tag-specific, DataWise adapts to each plant’s reality rather than imposing generic rules across signals that behave very differently.

The output flows through three stages. Anomaly Detection produces Data Quality Events (DQEs) – flagged incidents with type, severity, and context. Those events are then aggregated into a Data Quality Index (DQI), a continuous trust score at the tag, asset, and site level. From there, Albert (our AI agent) analyses root cause and prioritises what matters, and the Remediation engine applies the rules you configure to make your data AI-ready.

All of this runs continuously, in production, without manual intervention in the loop. The system scales by federation, so adding a new plant or new tags doesn’t require new configuration. DataWise baselines them and starts monitoring.

Less than you’d expect. DataWise connects directly to your historian or data lake using existing protocols. This doesn’t require any infrastructure changes, or complex configuration. Once connected, it begins baselining automatically.

Typical deployment runs in three phases, in under three weeks: connect and baseline (week 1), review detected events and tune remediation rules with your team (week 2), then live monitoring with alerts routing into your existing systems (week 3). The work on your side is mostly defining who reviews initial events and what remediation actions are appropriate.

That depends on how you deploy DataWise. It supports cloud deployment (AWS, Azure, GCP), on-premise deployment, hybrid configurations, and air-gapped environments for sites with strict isolation requirements.

For customers in regulated industries or with sensitive operational data, on-prem or air-gapped deployment keeps everything inside your boundary. For cloud deployments, DataWise is SOC 2 Type II and Cybervadis certified, with encryption in transit and at rest. We can walk through the specific architecture for your environment during a demo.

DataWise is built for industrial scale. The platform is tested in production environments analysing 12+ million tags daily, and it scales by federation – whether you start with a single plant of 5,000 tags or roll out across an enterprise of millions of tags across dozens of sites.

The self-supervised approach is what makes this work. Because each tag gets its own micromodel automatically, there’s no rule-writing bottleneck that breaks as you scale. Adding a new plant or new tags doesn’t require new configuration.

This is a common concern, and it’s why DataWise has the Triage stage built in. When DataWise begins monitoring a site for the first time, it does typically surface a significant volume of pre-existing issues (that’s the point). But our AI agent, Albert, analyses root causes and prioritizes them, so you’re not drowning in raw alerts.

Albert connects data quality signals to operational context: which tags are critical to which assets, which issues are systemic vs incidental, and which patterns indicate sensor problems vs process problems. You get a ranked, explained shortlist of what to fix first, not a firehose of flags. Many customers find that DataWise pays for itself in the first few months alone, by surfacing data issues they didn’t know they had.

DataWise sits between your data sources and your AI or data consumers. On the input side, it connects to historians (PI, AspenTech, Honeywell, Siemens, Rockwell, GE Digital, InfluxDB) and data lakes (Snowflake, Databricks, AWS, Azure, AVEVA Connect). On the output side, it delivers data quality metrics, validated data, and remediated data to downstream platforms such as AspenTech, AVEVA, C3.ai, Cognite, Honeywell, Seeq, TrendMiner, TwinThread, and your custom AI pipelines.

This means your data scientists and analytics teams don’t change their workflow. They keep using their existing tools, they just receive data that’s been continuously scored and remediated upstream. You can also gate models against DQI thresholds so models only train or predict on data that meets your trust bar.

DataWise is licensed at the enterprise level, with pricing based on the scale of your deployment. Price is typically driven by the number of tags monitored, sites covered, and which capabilities you need (Historian Analyzer, Consistency Monitor, Remediation Agent, etc.).

We work with customers to scope pricing against their specific use case, whether that’s a single-site pilot, a multi-site rollout, or an enterprise-wide commitment. Most customers start with a focused pilot on one plant or one critical process, then expand once value is proven. Book a demo and we’ll walk through pricing in the context of your environment.