07 / AI / ML / MCP
Book a demo →Data that agents can use.
Opportunity Atlas tables are clean, labeled, and relational — and queries can be pre-defined as templates so agents return structured answers, not just text.
For:BD leadersProductData teams
Why agents work well here
- — Tables are clean, labeled, and relational
- — Queries can be pre-defined as templates with named parameters
- — Agents retrieve structured rows, not free-text approximations
- — Lineage and provenance are queryable for audit
Example agent prompts
→ Identify the top 5 geographies for RPM expansion based on CHF prevalence and current under-utilization.
→ Find cohorts with the highest avoidable cost where reimbursement alignment already exists.
→ Rank MA contracts by Star-rating risk driven by medication adherence gaps.
Integration roles
MCP backend
Expose Opportunity Atlas views as MCP tools that any compatible client can call.
LLM knowledge layer
Ground LLM responses in structured, current healthcare data.
ML feature store
Reuse canonical entities as features for predictive models.
What this means for you
Your AI products and internal copilots can answer real opportunity questions — not by hallucinating, but by querying a transparent SQL layer they can cite.
See this in your data
Ready to put the Opportunity Atlas to work?
See the Opportunity Atlas run against your product, segment, or geography in a 30-minute walkthrough.