Traditional BI presents dashboards and reports for someone to review and interpret. Agentic analytics adds AI agents that continuously monitor data, explain what changed and why, and can recommend or trigger the next action, with a person approving anything consequential.

A natural-language interface lets any employee ask questions in plain language and receive grounded, sourced answers, without needing to write queries or navigate a BI tool.

Agents are grounded in your verified data sources rather than relying on model memory alone. Design and evaluation practices follow guidance such as the OWASP Top 10 for LLM Applications, with evaluation and approval gates in place before any action is taken on unclear or low-confidence insights.

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, and Google’s Gemini models, deployed through governed enterprise platforms where required.

GEM implements governance frameworks covering access control, data quality, and audit trails, aligned to regulations such as the General Data Protection Regulation (GDPR) and, where relevant, healthcare data requirements under HIPAA.

Yes. GEM’s data engineers unify data from various sources into a single, governed platform that agents and dashboards can both draw on.

You retain ownership of the data models, dashboards, and insights. GEM can provide ongoing support and optimization under separate arrangements.

Cost depends on the number of data sources, use cases, and model usage. GEM provides a business case and estimated ROI before implementation begins.

High-impact actions, such as triggering a workflow or updating a business system, pass through human approval gates. Lower-risk actions, such as surfacing an alert, can be automated within agreed rules.