AI-Ready Data Services

AI-ready data is data that is fit for a specific AI use case. It is available and accessible, representative of the situations the model will face, quality-checked, described with metadata, traceable through lineage, governed for privacy and permissions, and delivered through pipelines that keep it current. Because requirements differ between a forecasting model and a generative AI assistant, we always define readiness relative to the use cases you plan.

A well-run warehouse is a strong starting point for predictive models built on structured data, but it is rarely sufficient on its own. Generative AI depends heavily on unstructured content such as documents, emails and tickets, which warehouses usually do not hold. Machine learning also needs labeled, versioned training sets and features with point-in-time correctness. We build on your existing platform and add the missing pieces.

We connect to sources such as SharePoint, file shares, Confluence and ticketing tools, extract text and tables with parsing and OCR, remove duplicates and outdated versions, and enrich each document with metadata such as owner, date, type, topic and sensitivity. Content is then chunked and embedded for retrieval, with source permissions preserved so AI applications only use what each user is allowed to see.

We detect personal and sensitive information with pattern-based and AI-assisted classifiers, then mask, redact or exclude it according to your policies. Access controls from source systems are carried through to indexes and datasets. Processing runs in your cloud environment where possible, and lineage records which data feeds which model or index, which simplifies privacy reviews and responses to data subject requests.

For one or two target use cases, an assessment covering data inventory, profiling, readiness scoring and a remediation roadmap typically takes a few weeks. Preparing data and building production pipelines follows in iterations, with the first usable dataset or index often available within one to two months, depending on the number of sources and access arrangements.

Yes. Many clients already have data scientists or AI engineers and need help with the data engineering, governance and pipelines that support them. We can work as a project team, a dedicated team or a managed service, with weekly written updates and sprint demos. We also connect this work with our generative AI and machine learning services when you need help on the model side.