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TrustInSoft commercializes a source code analyzer called TrustInSoft Analyzer, which analyzes C and C++ code and mathematically guarantees the absence of defects, immunity of software components to the most common security flaws, and compliance with a specification. The technology is recognized by U.S. federal agency the National Institute of Standards and Technology (NIST), and was the first in the world to meet NIST’s SATE V Ockham Criteria for high quality software. The key differentiator for TrustInSoft Analyzer is its use of mathematical approaches called formal methods, which allow for an exhaustive analysis to find all the vulnerabilities or runtime errors and only raises true alarms. Companies who use TrustInSoft Analyzer reduce their verification costs by 4, efforts in bug detection by 40, and obtain an irrefutable proof that their software is safe and secure. The experts at TrustInSoft can also assist clients in training, support and additional services.

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.