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Revaly helps subscription and recurring-revenue businesses stop losing money to failed transactions by transforming payment systems into revenue engines. Its AI-driven payment intelligence evaluates issuer signals and network behavior in real time to keep legitimate payments from failing. Instead of letting small errors or timing issues derail a customer’s purchase, Revaly corrects problems proactively before the user hits “pay.” When a payment does decline, the platform automatically determines the best moment and method to retry, using behavioral science to protect customer trust. Companies using Revaly see major increases in recovered revenue, improved profitability, and significantly reduced involuntary churn. The platform integrates with more than 100 billing, CRM, and payment tools, eliminating the need for operational changes. Performance metrics across retail, software, and media industries consistently show double-digit recovery improvements. Ultimately, Revaly gives businesses a predictable, scalable way to grow by ensuring their earned revenue successfully processes.

AnalyticsCreator is a metadata-driven design application for data warehouse automation and data product engineering across the Microsoft data stack. Its Governed Control Model connects business meaning, data structures, transformation rules, dependencies, lineage and technical implementation in one controlled project model. Data teams design the required architecture in AnalyticsCreator, then generate native Microsoft assets from that design. Generated outputs can include SQL Server objects, SSIS packages, Azure Data Factory pipelines, supported 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. Because generated outputs are native Microsoft technology, no AnalyticsCreator runtime is required in production. Organisations retain ownership of the resulting implementation and can integrate generated assets into Git, Azure DevOps and CI/CD workflows. Lineage, documentation and dependency information remain connected to the design, helping teams understand change impact before regenerating affected assets. Design Intelligence extends this governed project context into AI-assisted data engineering by providing authorised AI tools and agents with 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 and repeatable data product engineering.