A modern data platform for AI and machine learning

IntelliStream DataHub

Data without connection is merely noise, and without context, it holds little value for AI. Our data platform builds flexible knowledge graphs, providing your information with a digital nervous system where every data point understands its relationship to the whole. This is how we transform raw data into contextualized knowledge, the essential foundation for developing AI that actually delivers results.

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The product

Real screens, not mock-ups

The console your team would open every morning, exactly as it ships.

  • Your operation as one connected map
    Your operation as one connected map Assets, the work they do, and the data they produce — laid out as one picture you can follow, instead of five systems you have to reconcile by hand. Every derived value keeps its lineage, so any number traces back to the signals behind it.
  • Measurements as they arrive
    Measurements as they arrive Follow a signal as it comes in, compare it against others, and move between the last few minutes and the last year without waiting for a report to build. Each one stays attached to the asset it came from, so the context travels with it.
  • One log for everything that happened
    One log for everything that happened Model-detected anomalies and ordinary equipment alarms in the same list, filtered by asset, type or time. No switching between systems to piece together a shift. Open one and follow it back to the asset that raised it and the events around it.
  • Zoom in on the moment it happened Drag across the chart to zoom from aggregated points that show the average, high and low, all the way down to the individual raw datapoints, millisecond by millisecond. Aggregation and zooming run in real time across billions of datapoints.

DataHub & Machine Learning

Quality data is the foundation of successful AI and machine learning, and DataHub is what makes it possible.

From simple spreadsheets to complex, unstructured files. Unlike traditional databases, IntelliStream DataHub is built for the diversity and complexity of modern data, turning information chaos into a strategic asset. Designed to drive business agility and IT efficiency by driving costs savings, optimizing revenue, and mitigating risks.

  • Brings disconnected systems and siloed data together
  • Automated, continuous quality control instead of manual oversight
  • Scales seamlessly from proof-of-concept to full deployment
  • Robust security controls keep your information protected

What it does

Data you can trust, working on its own

Three things decide whether a data platform earns its place. Whether whoever is reading it, person or agent, can trust what they are looking at. Whether it handles the routine work without being asked. And whether the business hears about a problem while there is still time to act.

Governance built in

Your data is organised into datasets — the parts of the operation that belong together. Access is granted per dataset and by role, so every person and every system sees exactly what they should and nothing more. How a dataset is meant to be handled is recorded against the dataset itself, next to the data it describes, rather than in a document nobody opens.

  • Access decided per dataset, not per spreadsheet
  • Handling rules kept where the data lives
  • A straight answer when someone asks who could see what

Intelligence that keeps working

Put a function on a measurement and it goes to work on every new reading — watching for values drifting outside their normal range, projecting where a trend is heading, turning a pattern into something the business can act on. You are told when something looks wrong instead of waiting for someone to notice it on a dashboard.

  • The routine watching runs by itself
  • Findings arrive as events the business can act on
  • Your people spend their time on the exceptions

An operation that reacts

A limit crossed, a state change, a prediction worth knowing about — each becomes an event the moment it happens, and the systems that care about it are told straight away. Decisions that used to wait for the morning report get made while they can still change the outcome.

  • Things are known when they happen, not the next day
  • Systems can act without a person in the middle
  • Less time gathering, more time deciding

Who reads it

Your people and your agents read the same model.

An agent that has to be told what a number means will get it wrong eventually. DataHub gives agents the same connected picture your engineers work from, through the same door, with the same permissions. Adopting agents does not open a second way into your data.

One door, one permission model

Agents connect over a standard interface, behind the same security checks as everyone else. No service account with blanket access, and no separate login for agents.

  • An agent inherits the permissions of whoever it acts for
  • Read-only stays read-only, because the token says so
  • Customer separation runs through the same code as the console

Tools that work on your own model

An agent can search your equipment, walk from a reading to the process it serves, pull history between two dates, and write back what it finds.

  • Equipment, relationships, time series and events
  • Record an event or append a reading, not only read
  • The same model your reports are built on

Answers sized for a machine

Search and browse results are trimmed to what matters, so an agent spends its attention on the few records that decide the answer rather than on everything it happened to look at.

  • Empty fields are never sent
  • Lists return summaries, full detail on request
  • Timestamps read the same as over the API

What that looks like on one pump

None of the five steps below live in the same system. That is the whole difficulty, and it is why an agent without a connected model gets stuck at step two.

One model: the pump, its parts, its people, its obligations 1 Spots the drift 2 Works out if it matters 3 Checks the part 4 Books the work 5 Leaves an audit trail

The spare is linked to the pump, the technician's qualification to the equipment, the compliance obligation to that same asset. That is what lets an agent get all five right. DataHub is not your purchasing system or your permit system, it is the model that tells the agent which pump, which part, which technician and which obligation belong together, so the calls it makes into those systems are the right ones. Where a person approves is yours to decide when you build the agent, not something the platform decides for you.

See the tools and how to connect

A look inside

See what your team would be working in

Built for the people who run the operation, not only for the ones who built the pipelines. And now for the agents they put to work alongside them.

  • The plant in its own language
    The plant in its own language Wells, manifolds, separators, pumps and the pipes between them, modelled with the relationships your engineers already use. Pick any node and follow what it feeds, what monitors it, and what stops if it stops.
  • What moves with what
    What moves with what Pick a signal and see which others follow it, how closely, and how far behind. Relationships that used to take an engineer a week to find are laid out in front of you.
  • Machine learning that keeps watch
    Machine learning that keeps watch Anomaly detection learns what normal looks like for each signal, so you are not maintaining long lists of fixed thresholds. Build AI agents that monitor your measurements around the clock and raise an event the moment something drifts out of pattern.
  • Every alarm points back at an asset
    Every alarm points back at an asset An anomaly arrives with the model that raised it, the threshold it crossed and the exact sensors it concerns. The question after "something happened" is already answered.
  • Your team is working in it on day one
    Your team is working in it on day one A guided walkthrough builds the first dataset, the first resource and the first measurement together with them, inside the product, so nobody has to sit through a training course to get started.

Data quality

Six dimensions, always in check

Our DataHub ensures your AI foundation meets critical quality standards, bringing together disconnected systems and siloed data, automates continuous quality control to eliminate manual oversight, and scales seamlessly from proof-of-concept to full deployment.

Accuracy

Timeliness

Consistency

Completeness

Uniqueness

Validity

Here's how we do it

Speed, scale, and control

With robust security controls, embedded performance monitoring, and flexible tools for experimentation and version control, IntelliStream's DataHub empowers data engineers, scientists, and business analysts to collaborate effortlessly through shared interfaces and transparent workflows.

Edge Computing

We've created our own smart data storage system that works significantly faster and costs much less than traditional cloud services.

Power Up with Open-Source

Build upon excellent work by the smartest people in the software industry, using game-changing open-source products.

Column-Based Data Scans

Our system can process over 10 billion pieces of information every single second - that's like reading through millions of spreadsheets in the blink of an eye.

Analytics That Just Deliver

Advanced analytics with a modern data technology. Optimized for artifical intelligence, our platform provides optimal accuracy and smart data processing.

Computing at Scale

We leverage advanced technologies like Infiniband and RDMA to access remote hardware directly, unlocking hyperscale performance few can match.

Live Data Processing

Our Data platform delivers scalable, real-time data processing solutions, ensuring you stay one step ahead in the market.

Smarter Data Compression

Slash storage costs with up too 25-100x data compression, compared to the usual 2-4x offered by traditional solutions.

Imagine turning a $25,000 monthly bill for 1 Petabyte of data into just $250–$1,000 monthly. That isn't just savings; it is a revolution for your budget.

One platform, infinite possibilities

Our Knowledge Graph-powered data-platform connects your data landscape, offering complete data lineage, automated governance, and accelerated feature engineering, all in one place supported by our expert Forward Deployed Engineers.

At the heart of our data-platform lies a revolutionary approach to data organization. Knowledge Graphs

Learn more about knowledge graphs
Knowledge graph of physical assets and the business functions they support Knowledge graph of physical assets and the business functions they support

The framework

A Semantic Data Management Framework

Our Knowledge Graph is more than just a smart data structure; it powers a suite of integrated capabilities designed to provide trust, transparency, and control over your entire data ecosystem.

Data Governance manages your information responsibly through policies that maintain integrity and security. Instead of manual data cleanup, our platform integrates quality checks directly into the data flow, ensuring information is accurate, secure, and ready for analysis.

Policy Subscription Model: Tailors data governance to specific team and AI needs. It ensures precise, compliant data access with full visibility and control, streamlining audit trails and regulatory adherence.

Data Lineage tracks and visualizes the complete story behind your data - from source to destination enabling quality monitoring, safely implement changes, confidently upgrade infrastructure, and dive deeper with metadata for a more trustworthy data ecosystem.