AI-ready data platforms
AI is only as capable as the data platform underneath it.
Most organisations experimenting with AI are doing so on data infrastructure that was never designed for it. Separate systems for transactional and analytical workloads, databases that cannot handle the vector search that modern AI applications require, platforms that deliver fast reads or fast writes but not both simultaneously. The result is AI that performs well in a controlled environment and fails to scale in production – not because the model is wrong, but because the data foundation cannot support it.
An AI-ready data platform is not a single product. It is an architecture that handles transactional, analytical, and vector workloads simultaneously, delivers the sub-second query performance that production AI demands, and does so on data that is trusted, governed, and continuously updated. Dot Group designs and builds these platforms: migrating from existing infrastructure where necessary, extending it where that is the smarter choice.
The platform requirements for production AI
The gap between AI in a pilot and AI in production almost always comes back to the same infrastructure limitations. Pilots run on curated, static datasets in controlled conditions. Production AI runs on live data being updated continuously, at volumes that dwarf the pilot environment, with query patterns that combine the transactional reads of an operational system with the analytical complexity of a data warehouse.
SingleStore is the operational database we most frequently deploy as the foundation for AI-ready architectures. Its ability to handle transactional writes and complex analytical queries on the same data simultaneously, at single-digit millisecond response times and millions of upserts per second,makes it purpose-built for exactly these workloads.
Unlocking unstructured data for AI
Enterprise data is mostly unstructured – documents, emails, contracts, support tickets, product descriptions – and it is this data that holds much of the context AI applications need to be genuinely useful. IBM DataStax is built for this: managing unstructured and multimodal data for AI at scale, with vector search capabilities that enable semantically relevant content retrieval rather than keyword matching.
Astra DB delivers near-zero latency for high-speed vector search, while Langflow, DataStax’s open-source development tool, allows teams to prototype and deploy retrieval-augmented generation and multi-agent AI applications through a low-code interface.
A governed foundation across the whole estate
IBM watsonx.data provides the governed data foundation that sits across the estate – an open lakehouse architecture that makes structured and unstructured data accessible, catalogued, and ready for AI and analytical workloads without requiring it all to live in one place.
It integrates with Confluent event streams, SingleStore operational data, and DataStax unstructured data, providing the metadata, lineage, and governance layer that turns a collection of data sources into a coherent, trustworthy data estate.
The Dot Group Advantage
Building an AI-ready data platform is not a single project, it is a programme that touches database infrastructure, data pipelines, governance, and the applications that sit on top.
Dot Group brings the architecture expertise to design it as a coherent whole, and the delivery experience to build it in a way that keeps the business running while the foundation is being put in place. We work with the IBM watsonx portfolio, SingleStore, and DataStax as integrated components of a unified architecture – not as separate products to be bolted together after the fact.
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Your AI initiative is only as strong as the data platform it runs on.
Talk to us about your current architecture and your AI ambitions. We will show you where the gaps are and what it would take to close them.