AnalyticsCreator is a metadata-driven design application for data warehouse automation and data product engineering across the Microsoft data stack.
At its core is the AnalyticsCreator Governed Control Model, which keeps business meaning, data structures, transformation rules, dependencies and technical implementation connected in one controlled project model. Data teams design the required architecture in AnalyticsCreator, then generate native Microsoft assets from that design.
Generated assets can include SQL Server objects, SSIS packages, Azure Data Factory pipelines, Microsoft Fabric components, deployment artefacts and Power BI semantic models. AnalyticsCreator supports dimensional, 3NF and hybrid modelling approaches together with ingestion, transformations, delta loading, historisation, Slowly Changing Dimensions, snapshots and repeatable data-processing patterns.
Changes made in the model can be propagated across dependent project assets, while lineage, documentation and impact analysis remain connected to the underlying design. This helps teams reduce repetitive engineering work, standardise delivery and understand the effect of change before regenerating affected assets.
AnalyticsCreator generates native Microsoft technology rather than requiring a proprietary runtime. Organisations retain ownership of the resulting implementation and can integrate generated assets into existing Git, Azure DevOps and CI/CD processes.
Design Intelligence extends this governed project context into AI-assisted data engineering by giving authorised AI tools and agents structured access to metadata, lineage, dependencies and design rules.
Typical use cases include enterprise data warehouse development, Microsoft Fabric adoption, SQL Server and SSIS modernisation, governed Power BI delivery, SAP-to-Microsoft analytics architectures and repeatable data product engineering.
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AlisQI is a modular, cloud-based Quality Management platform for process and batch manufacturers who want to reduce firefighting, improve predictability, and stay compliant by default.
Unlike traditional EQMS platforms that were built around documents and later adapted for analytics, AlisQI was designed from the start as a data-first system. Quality, lab, and production data are structured and connected in one operational backbone.
That foundation now enables practical AI capabilities inside daily workflows. Manufacturers can automatically extract data from diverse supplier COAs without predefined templates, generate structured digital forms from existing files or plain language, query their QMS conversationally, and detect recurring incident patterns across sites.
Core modules include Document Control, Training, Deviations, CAPA, Audits, Risk Management, Supplier Quality, SPC, and EHS, supported by targeted out-of-the-box Solvers that address specific operational problems.
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Claude Mythos 5.1
Claude Mythos 5.1 is Anthropic’s newest Mythos-class model, designed for advanced cybersecurity, biology, scientific research, coding, and long-running knowledge work. It is the same underlying model as Claude Fable 5.1 but uses different safeguards: Fable 5.1 is generally available, while Mythos 5.1 is restricted to trusted access programs with safeguards specifically designed for cybersecurity and life sciences research. The model sets a new performance frontier for agentic coding and demonstrates the strongest cyber capabilities of any Anthropic model released to date. In scientific research, Mythos 5.1 can work with specialized tools and complex workflows across molecular design, computational biology, and other technical domains. In Anthropic’s experiments, it designed high-affinity protein binders across multiple targets and achieved its strongest measured hit rate to date. It also optimized seven open-source protein and genomics deep learning models.
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