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Pain-point 8:

Machine Learning + custom GenAI is a pipe dream.

Apply Machine Learning to real-time data sets + employ AI-led decision-making across all your systems.

The ultimate value of machine learning and GenAI technologies to businesses will only found when they can be broadly used across all of their existing technologies and systems without wholesale change.

Our Rayven Platform can take machine learning use and the adoption of AI-led decision-making out of the classroom and make it a reality, fast. Using the platform's extreme interoperability and bidirectional data flow capabilities, coupled with its inbuilt Machine Learning Engine and custom workflow builder, businesses can analyse their real-time data sets with algorithms and create workflows that take action based on a desired outcome rather than specific logic, enabling AI-led decision-making to improve your business and its operations, simply.

This approach not only allows you to adopt Machine Learning and predictive analytics technologies fast, but enables you to use it across all your current technologies without wholesale change.

Related business
symptoms:

Inefficient Data Analysis + Limited Insights: Machine learning excels at analysing large volumes of data to extract meaningful insights. Businesses that struggle to process and interpret the data they generate in real-time will struggle to improve further.

Novel Technology Adoption is Slow + There's No Roadmap: It's possible to quickly fall behind competitors and waste resources without a holistic view to new technology adoption. This leads to more silos, limits the return (and capabilities) from technology investments, and dramatically slows transformation.

Limited Operational Forecasting + Machine-Lead Optimisations: Without machine learning and AI, businesses miss out on advanced predictive analytics capabilities and AI-led optimisations. This limits their ability to find efficiencies and increase yields, but also improve safety and compliance.

Join the organisations around the world already achieving more with Rayven:

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How the Rayven Platform solves it.

Our platform enables you to add machine learning analysis to real-time and Big Data sets across your business, simply. By integrating and analysing all your data sets together, you can uncover the best course of action based on a particular goal and direct instant intervention to facilitate AI-led decision-making.

Integrate your data sources,
systems + solutions:

Rayven can connect to anything using various methods (explore them all here), including:

  • TCP/IP
  • Gateways
  • APIs
  • Protocols + Standards
  • Cloud Integration
  • FTP
  • Custom Scripts.

Analyse data sets + push it (and directions) across connected technologies:

Rayven enables you to analyse all of your data together and in real-time using machine learning algorithms. The insights generated from the analysis can then be displayed via dashboards, pushed to other connected systems for viewing, or even used in extended workflows to direct connected machinery and software to take a particular action based on real-time conditions.

Create / upload, train + deploy
algorithms, simply.

Our Machine Learning Studio is native and built into the Rayven Platform. It enables you to upload any Python algorithm or create your own; before then training, testing, and deploying it via the platform’s Workflow Builder.

Using it you can also model and test algorithms using your data to discover trends and find ways of better operating.

Completely programmable via a drag-and-drop logic, our Machine Learning Studio comes pre-built into our platform and enables non-developers to adopt dig deeper into their data or adopt predictive analytics within their real-time solutions.

Create custom
workflows + logic:

Our Workflow Builder, featuring a drag-and-drop interface, makes it possible to create rules, connect systems, and put in place sophisticated business processes without writing a line of code.

The codeless interface enables you to combine different data sources, perform complex calculations, and add AI and automated decision-making to your solutions.

The step-by-step.

Step 1: Connect

Our platform has out-of-the-box integrations with major platforms and 20+ universal connectors (nodes) that work at the protocol-level + are easy to configure, enabling you to connect any data feed and system.

Our platform's approach enables bidirectional data flow in the native protocol of each connected device in various formats, including JSON, XML, CSV, plain text, and more.

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Step 2: Process, Normalise, Store + Access

Following ingestion, data undergoes processing which includes validation, normalisation, and potentially transformation, aligning it with a standardised format for analysis and storage.

The processed data is then stored in scalable and secure databases or data lakes, which is adaptable for cloud or on-premises setups.

Step 3: Prepare, train + test your algorithm

Prepare your data: Select training data from your wider solution and apply ready-to-go filters and normalisers that are natively available within our Machine Learning Studio.

Create, import + train: Anyone can use the Machine Learning Studio's drag-and-drop interface to create an algorithm from scratch or import another Python-based model, before running iterative training.

Deploy + predict: Deploy your trained model to streaming data in the Workflow Builder with a single click and visualise the results in your dashboard for real-time future insights.

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Step 4: Add Formulas + Machine Learning to your solutions

The wide verity of Output node enables you to push information from Rayven to other applications. It can send either specific fields or the entire JSON content to another system, or even create and push a CSV file.

You can also customise output data using columns, which adds a field to the data that is to be accessed via API to help it find the right data to act upon.

Rayven has free + low-cost options, making it affordable for every business.