Intelligence at the Edge

Deliver the most accurate predictive behavior data for every asset & process

Predictive Objects is a predictive analytics software platform based on Artificial Intelligence and Meta Active Machine Learning.
Tellmeplus packages intelligence with every asset & process to optimize business operations, provide a combined view of the asset performance
and a simulation of its evolution over time.


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Automating Prescriptive Analytics

Predictive Objects improves the outcome of business and industrial processes through actionable customer and asset
intelligence. The platform leverages machine learning, AI and big data to automate the creation and deployment of predictive models,
augmenting business experts for faster and more accurate predictions that run where the data is produced and where
decisions need to be made.

Predictive Objects can be deployed and run anywhere: inside connected objects or machines, at the edge of the network,
in industrial IoT platforms, in public or private clouds - bringing the decision to the object and to the data in real time.

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Data Acquisition

Predictive Objects removes the data selection bias by providing access to all
data available within the organization - internal or external. This enables Predictive Objects
to process the entire Gaussian distribution of data, and detect weak signals that would
otherwise be ignored. The process is greatly accelerated through massive automation,
shortening projects from months to hours


Ingest any structured data of any type, internal or exogenous data (open data, web), as batches and/or streams.


Check and cleanse all data, homogenize and normalize sources, verify integrity.


Anonymize data before processing through record transposition for maximum protection.

Feature Engineering


Raw data produced by sensors or other assets are not directly usable by machine learning algorithms. Predictive Objects automates or facilitates the preparation of the datasets, and augments data with information that will make predictions more valuable for the business.


Turn raw data into temporal sequences of information based on the data type and density.

Oriented Tagging

Add business-related information to the existing data: asset identification and classification, cost or revenue information.

Objective Functions Tailored for Business

Predictive models that do not account for business objectives are difficult to use by business users,
and model selection can be severely impacted. Predictive Objects lets the business expert define
a cost matrix that targets operational costs or gains, and computes the business value of each model.

Fail Fast, Learn Fast

Deployment to production of predictive models is immediate
to any targeted asset: object, device, platform, cloud.
There is no need for coding, dramatically shortening the
time it takes for projects to deliver value.

Compressed time-to-convergence

The Meta Active Machine Learning, Predictive Objects’ wisdom that is shared across projects and enriched at each model computation, dramatically accelerates the search for the best model, shortening to hours a process that used to take months of exploration by data science teams.

Iterative process

Find quickly if a model work, test hypothesis, and ensure ongoing efficiency: the performance of models is continuously assessed to test their accuracy, precision and recall. Production and deployment of new and revised models is ongoing.

Explainable AI

Business experts can easily build models, test hypotheses, prove or disprove assumptions, and hence get value instantly. Through the use of symbolic algorithms, models are driven by variables and based on a decision tree that explains not only the prediction but also the impact of each variable on this prediction.

Asset Intelligence & Simulation

Predictive Objects run seamlessly and consistently on all types of assets, regardless of their level of computing power or their state of connectivity:

  • Material assets such as machinery, industrial equipment, production lines, vehicles, airplanes, connected objects (IoT), etc.
  • Immaterial assets such as business or industrial processes, customers, contracts, work orders, purchase histories, etc.

Performance monitoring

Thanks to their ability to get deployed and embedded on any platform, in any runtime, Predictive Objects packages intelligence with every asset, providing a combined view of the asset, its performance, and its evolution over time (predicted behavior, deviance from the model and therefore associated risk).

Embeddable low-footprint models

Predictive Objects produces models that are require minimal processing power, enabling the deployment of predictive models on any type of object or platform. Embedding intelligence inside the assets - or as close to them as possible - lets these assets make their own decisions with no impact from network latency.


The simulation capabilities of Predictive Objects make it possible to test "what if" scenario for each individual asset or cluster of assets. By adjusting variables, in the past, in the present or in the future, business users can visually evaluate the impact that any actions would have had, or that they will have, on the future performance of the asset.

Edge Intelligence

Processing centrally all data collected by distributed assets induces network latency,
quickly drains power on the connected asset, and oftentimes is not even
practical or doable.Edge AI embeds intelligence within assets for efficient and
secure production of predictions as close as possible to where the data is generated - inside the asset.

Real time analytics at the edge

Predictive Objects runs seamlessly inside any connected asset, at the edge of the network, or in industrial IoT platforms, placing the predictive model as close as possible to the system it impacts. This way the asset makes decisions locally, enabling offline operation if necessary.

Real-time predictions

Edge AI speeds up predictions by removing the need for a network round-trip to the cloud or to a central server - assuming sending data is even practical, which is not always the case with high-frequency sensor measures, large numbers of individual assets, or poor connectivity environments.


Rather than sending all data from connected assets to a remote cloud or infrastructure for analytics purposes, the IIoT will rely on the capacity to collect and process the raw data as close as possible to the machines that generate them. Hence reducing network security issues and additional bandwidth requirement.

IoT Platforms

Some of leaders and major brands have now partnered with Tellmeplus to provide added value services to their customers and expend IoT projects ROI.

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