Integrations that build themselves.

Datathere connects once to each system, creating persistent links for both mapping and production use. Connections can include REST APIs, SFTP, and cloud storage as well as structured files such as CSV, JSON, XML, TXT, and PDF via OCR.

Authentication follows commonly supported enterprise methods. Once connected, the same integration can serve sample data for setup and live data for ongoing execution.

Connected data sources provide representative samples used to establish context. Datathere interprets field roles, dependencies, and relationships across datasets to build a semantic model that reflects how information behaves. This enables precise mapping and transformation without manual field matching.

AI determines how datasets relate and automatically builds the logic to align them. It maps equivalent fields, combines multiple inputs when needed, applies unit or format conversions, and performs calculations or conditional rules. Each mapping includes reasoning and confidence values so teams can review and refine before approval.

Datathere runs automated checks to identify missing relationships, unresolved references, or logic that could fail at runtime. Teams review the proposed mappings, adjust transformations where required, and approve configurations for deployment. Certification locks the approved version, records ownership, timestamps, and configuration details, and preserves every certified state for audit and governance. Any later modification creates a new certified version, keeping a full historical trail.

Autopilot executes certified mappings according to defined rules. Runs can be triggered by API calls, file drops, or scheduled intervals, operating continuously without manual input. Each execution applies the certified logic exactly as approved and records detailed runtime data for analysis. Autopilot maintains consistent operation across environments and scales with data volume and frequency.

Datathere analyzes production runs to identify the source of errors and performance deviations. Issues are classified as data-related or logic-related, allowing targeted resolution. For mapping logic issues, Datathere generates additive AI recommendations that extend or refine existing rules, improving mapping accuracy and reducing recurring errors over time.