Atlas — CDP Analytics Orchestrator
613× cheaper analytics — a request becomes a delivered dashboard, end to end.
- •Orchestrator agent: a plain-language request → a delivered dashboard
- •613× cost reduction · 54M+ records → dashboards in ~4 minutes
- •Snowflake data access via MCP · Azure DevOps work-item sub-agent
- •Human-in-command: approval required before every step
Tap to enlargeAtlas is an AI orchestrator I built for a Top-5 global pharma client’s analytics team. It takes a plain-language request — "market share for this segment last quarter" — and coordinates every step to a delivered, interactive dashboard: discovering the data, querying it, tracking the work, and generating the visualization. All at 613× lower cost than the prior manual approach, and always under human approval. Presented on the Snowflake conference stage.
An Orchestrator, Not a Chatbot
Coordinates sub-agents and live data tools — not a single-shot chatbot
Atlas coordinates sub-agents and tools rather than answering in isolation. It connects to the data warehouse through MCP — listing tables, inspecting schemas, previewing rows, and running SQL against real pharmaceutical market data — and hands work-item management to a dedicated DevOps sub-agent that opens, updates, and closes tickets as the work progresses.
From Request to Delivered Dashboard
Request → data → dashboard → delivered, in one governed loop
The full loop runs in one conversation: capture the request, show what data exists, check the backlog for duplicates, gather requirements, open a tracked issue, query the warehouse for real numbers, generate an interactive dashboard (market share, KPIs, revenue, trends), then deliver the URL and close the issue. Days of specialist analytics work collapse into minutes — a 613× cost reduction over 54M+ records.
Human-in-Command by Design
Transparent, collaborative, accurate — approval before every step
Atlas never proceeds without an explicit yes. Every step — running a query, creating a ticket, generating a dashboard — is proposed and confirmed first. It stays transparent (showing each step), collaborative (asking before acting), and accurate (real data, never hardcoded examples).
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