Problem
No delivery structure
AI query results returned in a chat window do not replace scheduled deliveries, download history, or structured output formats.
For AI agents
Let AI agents work with company data through a safer execution layer.
DataPilot helps teams operationalize AI-driven data workflows with scheduling, delivery, visibility, and private execution patterns, without making raw direct database connectivity the default answer.
Relevant for teams using Claude, MCP, or other AI copilots that need to work with production data.
The raw MCP problem
Direct database access is not the whole solution.
AI agents that connect directly to production databases can generate and run SQL, but teams still need delivery loops, execution boundaries, operational visibility, and repeatable output patterns. Raw connectivity is a starting point, not a production pattern.
Problem
No execution boundaries
Direct database connectivity for every AI tool creates broad access patterns that compound as more agents are added to the stack.
Problem
No operational visibility
Without run history, audit logs, and delivery records, teams cannot tell what the agent ran, when, or what data was surfaced.
What DataPilot adds
A governed execution layer between AI tools and company data
Governed execution
AI agents work through approved query logic and metric definitions, not free-form SQL against production. Analysts control what runs and how.
Scheduled exports and delivery
AI-driven workflows can trigger scheduled exports, format outputs as CSV/XLSX, and deliver results through structured notification channels.
Data quality checks
Before AI-generated outputs reach stakeholders, quality checks ensure the underlying data meets defined thresholds and expectations.
Private network execution
AI workloads can execute near protected databases without opening broad network access from every agent or client tool in the stack.
Where it fits
Between AI tools and protected company data
As an operational layer
DataPilot is not a replacement for every AI model. It is the execution and delivery layer that sits between AI tools and the data they need.
For MCP and Claude workflows
Teams using Claude, MCP, or other AI copilots can route data access through DataPilot to add governance, scheduling, and delivery structure.
For organizations at scale
Organizations that want safer AI rollout patterns, not just "connect the AI and see what happens", benefit from DataPilot's operational controls.
Ready to add governance to your AI data workflows?
See the product or talk to us about your architecture needs.