PoC to Production: Accelerating AI Deployment with IOblend
💭 Did You Know? While a staggering 92% of companies are actively experimenting with Artificial Intelligence, a mere 1% ever achieve full maturity in deploying AI solutions at scale.
The AI Production Journey
A Proof of Concept (PoC) in AI serves as a small-scale, experimental project designed to validate the feasibility and potential value of an AI solution for a specific business problem. It’s about demonstrating that an idea can work under controlled conditions.
The transition to production, however, involves transforming this initial success into a robust, scalable, and integrated system that delivers continuous value in a real-world operational environment.
Businesses’ Hurdles in AI Operationalisation
Organisations frequently encounter a myriad of challenges when attempting to operationalise AI PoCs.
Data readiness is paramount. Production data is often messy, inconsistent, and siloed, vastly different from the curated datasets used in PoCs. This leads to issues with data quality, accessibility, diminishing the AI’s accuracy of prediction.
Integration complexities with legacy IT systems pose a significant hurdle. A PoC might run in isolation, but a production system demands seamless connection with existing databases, ERPs, and CRMs, often revealing unforeseen dependencies.
A prevalent issue is the lack of a clear business case and misalignment with strategic objectives. Many PoCs, while technically innovative, fail to demonstrate tangible ROI or address critical business needs, losing momentum and funding.
IOblend: Accelerating AI from Concept to Reality
IOblend’s solution is an all-in-one data integration accelerator, providing real-time, production-grade, managed Apache Spark™ data pipelines that can be set up incredibly quickly.
Streamlined Data Management: IOblend tackles data chaos head-on. Its in-built data management, quality, and validation steps automate much of the laborious data preparation, ensuring that AI models are fed with consistent, high-quality data. This significantly reduces developer time and enhances the reliability of outputs.
Seamless Integration: Designed for versatility, IOblend simplifies the integration of streaming and batch data using a Kappa architecture. This boosts the efficiency of operational analytics and MLOps, enabling businesses to connect to any data source, perform in-memory transformations, and route results to any destination with minimal effort.
Cost-Effectiveness and Scalability: By leveraging low-code/no-code development and automated data governance, IOblend drastically cuts down the costs and development efforts associated with data projects and AI initiatives. It supports robust, cost-effective delivery of centralised and federated data architectures, capable of processing well over one million transactions per second with low latency.
Unlock the full potential of your AI initiatives with IOblend and transform concepts into commercial realities.
IOblend: See more. Do more. Deliver better.
IOblend presents a ground-breaking approach to IoT and data integration, revolutionizing the way businesses handle their data. It’s an all-in-one data integration accelerator, boasting real-time, production-grade, managed Apache Spark™ data pipelines that can be set up in mere minutes. This facilitates a massive acceleration in data migration projects, whether from on-prem to cloud or between clouds, thanks to its low code/no code development and automated data management and governance.
IOblend also simplifies the integration of streaming and batch data through Kappa architecture, significantly boosting the efficiency of operational analytics and MLOps. Its system enables the robust and cost-effective delivery of both centralized and federated data architectures, with low latency and massively parallelized data processing, capable of handling over 10 million transactions per second. Additionally, IOblend integrates seamlessly with leading cloud services like Snowflake and Microsoft Azure, underscoring its versatility and broad applicability in various data environments.
At its core, IOblend is an end-to-end enterprise data integration solution built with DataOps capability. It stands out as a versatile ETL product for building and managing data estates with high-grade data flows. The platform powers operational analytics and AI initiatives, drastically reducing the costs and development efforts associated with data projects and data science ventures. It’s engineered to connect to any source, perform in-memory transformations of streaming and batch data, and direct the results to any destination with minimal effort.
IOblend’s use cases are diverse and impactful. It streams live data from factories to automated forecasting models and channels data from IoT sensors to real-time monitoring applications, enabling automated decision-making based on live inputs and historical statistics. Additionally, it handles the movement of production-grade streaming and batch data to and from cloud data warehouses and lakes, powers data exchanges, and feeds applications with data that adheres to complex business rules and governance policies.
The platform comprises two core components: the IOblend Designer and the IOblend Engine. The IOblend Designer is a desktop GUI used for designing, building, and testing data pipeline DAGs, producing metadata that describes the data pipelines. The IOblend Engine, the heart of the system, converts this metadata into Spark streaming jobs executed on any Spark cluster. Available in Developer and Enterprise suites, IOblend supports both local and remote engine operations, catering to a wide range of development and operational needs. It also facilitates collaborative development and pipeline versioning, making it a robust tool for modern data management and analytics
Real-Time Risk Modelling with Legacy & Modern Data
Risk Modelling in Real-time: Integrating Legacy Oracle/HP Underwriting Data with Modern External Datasets 💼 Did you know that in the time it takes to brew a cup of tea, a real-time risk model could have processed enough data to flag over 60 million potential fraudulent insurance claims? The Real-Time Risk Modelling Imperative Real-time risk modelling is
Unify Clinical & Financial Data to Cut Readmissions
Clinical-Financial Synergy: The Seamless Integration of Clinical and Financial Data to Minimise Readmissions 🚑 Did You Know? Unnecessary hospital readmissions within 30 days represent a colossal financial burden, often reflecting suboptimal transitional care. Clinical-Financial Synergy: The Seamless Integration of Clinical and Financial Data to Minimise Readmissions The Convergence of Clinical and Financial Data The convergence of clinical and financial
Agentic Pipelines and Real-Time Data with Guardrails
The New Era of ETL: Agentic Pipelines and Real-Time Data with Guardrails For years, ETL meant one thing — moving and transforming data in predictable, scheduled batches, often using a multitude of complementary tools. It was practical, reliable, and familiar. But in 2025, well, that’s no longer enough. Let’s have a look at the shift
Real-Time Insurance Claims with CDC and Spark
From Batch to Real-Time: Accelerating Insurance Claims Processing with CDC and Spark 💼 Did you know? In the insurance sector, the move from overnight batch processing to real-time stream processing has been shown to reduce the average claims settlement time from several days to under an hour in highly automated systems. Real-Time Data and Insurance
Agentic AI: The New Standard for ETL Governance
Autonomous Finance: Agentic AI as the New Standard for ETL Governance and Resilience 📌 Did You Know? Autonomous data quality agents deployed by leading financial institutions have been shown to proactively detect and correct up to 95% of critical data quality issues. The Agentic AI Concept Agentic Artificial Intelligence (AI) represents the progression beyond simple prompt-and-response
IOblend: Simplifying Feature Stores for Modern MLOps
IOblend: Simplifying Feature Stores for Modern MLOps Feature stores emerged to solve a real challenge in machine learning: managing features across models, maintaining consistency between training and inference, and ensuring proper governance. To meet this need, many solutions introduced new infrastructure layers—Redis, DynamoDB, Feast-style APIs, and others. While these tools provided powerful capabilities, they also
