They help businesses design, build, integrate, and operate GenAI applications such as knowledge copilots, customer assistants, document intelligence tools, analytics copilots, and workflow assistants.

A business application is grounded in trusted enterprise data, respects permissions, connects to business systems, and is evaluated and governed for a specific workflow.

Yes. GEM can design a RAG approach that retrieves relevant approved content, applies access controls, gives source citations, and evaluates answer quality before launch.

Depending on the use case, applications can connect to CRM, ERP, ITSM, HRIS, document repositories, knowledge bases, data platforms, and line-of-business tools through governed APIs and events.

GEM combines retrieval from trusted sources, permission-aware access, guardrails, evaluation, monitoring, audit logs, and human approval for high-impact actions.

Start with one high-value workflow, agree on the data and success metrics, validate it with real users, then scale the proven pattern.

GEM selects and configures foundation models based on your accuracy, latency, cost, and data residency requirements. Commonly deployed options include OpenAI’s GPT models, Anthropic’s Claude models, Google’s Gemini models, and Meta’s open-weight Llama models, deployed through governed platforms such as Azure OpenAI Service or Amazon Bedrock. GEM can also work with a client’s preferred model provider where supported.

Every application is evaluated for accuracy, safety, latency, adoption, and cost before go-live, using RAG evaluation practices similar to open frameworks such as RAGAS, combined with human review of representative real-world queries.

You retain ownership of the application, configurations, and data. GEM can provide ongoing support and optimization under separate arrangements.