There are millions of open source models, but determining what model to use and for what is hard unless tested individually in applications. LMM is the first stage gate that filters and identifies local models that are worth your time for further validation and evaluation.

Pharmaceutical firms spend millions of dollars every year on patient and physician advertisements. This application analyzes all advertisement modalities (website, email, print, digital, video etc.) and provides a confidence score on how likely they are to be slapped on the wrist with an FDA OPDP Violation Letter. System analyzes claims against labeling data, clinical trial data and historical OPDP violation precedents to come up with an accurate score.

Thousands of AI papers and product announcements launch every week, making it almost impossible to separate genuine breakthroughs from marketing hype. Engineers and tech leaders waste hours trying to figure out which models actually work, how they perform in production, and what is truly worth building.

Context engineering for enterprise AI requires strong ontologies, business semantics, metadata, and governance policies. Without a structured semantic backbone, agents and LLMs struggle with hallucinations and siloed domain logic. Ontology Modeler allows teams to rapidly create, visualize, and bootstrap seed ontologies for business processes.

Enterprises face fragmented, symbolic AI experimentation ('innovation theater') where business teams spend millions on disconnected pilots without standardized readiness criteria, economic ROI scoring, or cross-functional execution alignment. AI Council provides executive teams with a consolidated portal where teams can submit ideas curated by a copilot to ensure each idea comes with well thought out business plan with tangible ROI.

Enterprises spend months and millions reinventing standard industry schemas, struggling with disparate domain silos, inconsistent relational joins, and lack of canonical data models for modern Lakehouse and AI architectures.