AI-Powered Mapping
Field Mapping
AI analyzes your source and destination schemas to generate mappings with confidence scores and written reasoning for every decision.
Transformations
When source and destination formats differ, AI generates transformation logic and validates it against your data before you accept.
Join Discovery
When your mapping involves multiple sources, AI identifies how they relate and recommends join conditions with confidence ratings.
Aggregation
AI detects when your source data is more granular than the destination and recommends grouping strategies with preview output.
Plain English Editing
Edit any part of the mapping by describing what you need in natural language. AI updates the underlying logic and tests it against your data.
Certification
Lock your mapping configuration before production. Every change after certification requires written justification in the audit trail.
AI-Generated Field Mappings
Upload your source schemas and select a destination. AI analyzes field names, data types, sample values, and semantic meaning to generate a complete mapping in a single pass. When fields need format conversion — dates, numbers, string formatting — AI generates the transformation logic automatically and shows you the output on your data.
- Confidence scores with reasoning for every mapping decision
- Automatic format conversion: dates, numbers, string parsing, conditional logic
- Multi-source schemas mapped to one destination simultaneously
- Sample output preview on your data before accepting
const last = $0?.split(' ').slice(1).join(' ') ?? '';
return (last + ', ' + first).trim();
Transformation Expressions
When your source and destination formats differ, AI generates the transformation logic automatically. Every expression is validated against your source data before you see it — you review sample output, not code. If the first attempt produces errors, AI self-corrects using the error feedback until the expression passes.
- Date parsing, string formatting, number conversion, conditional logic
- Every expression validated in a sandboxed environment before production
- Preview output on your data before accepting
Multi-Source Join Discovery
Most real-world integrations involve more than one data source. Orders live in one system, customers in another, product catalogs in a third. Figuring out how those sources relate — which fields match, what kind of join to use, how to handle case mismatches or format differences — is traditionally the most time-consuming part of the project. datathere handles it automatically.
When your mapping includes multiple sources, AI examines field names, data types, and sample values across all of them to identify relationships. It recommends join types, generates match conditions, and rates each relationship with cited evidence so you can see why it was suggested.
- Automatic relationship detection across all connected sources
- Confidence rating (strong, moderate, weak) with specific evidence
- Handles case-insensitive matching, date format differences, composite keys
- Join type recommendations (inner, left, right) with reasoning
- Human-readable explanations alongside technical conditions
Source grain is order line items (1 row per product per order). Destination expects order totals (1 row per order).
Aggregation and Grain Analysis
When your source data is more granular than what the destination expects — order line items vs. order totals, daily transactions vs. monthly summaries — AI detects the mismatch and recommends how to aggregate. It suggests grouping fields, aggregation functions, and calculated fields. You preview the aggregated output on a sample of your data before committing.
- Automatic grain mismatch detection
- SUM, AVG, COUNT, MIN, MAX, and array aggregation
- Multi-level grouping with calculated fields
- Nested array flattening for complex source structures
Plain English Editing
You should never have to read or write code to manage a data mapping. When something needs to change — a field mapping, a transformation, an aggregation rule, a join condition — describe what's wrong in plain language. "This should be uppercase." "Split first and last name." "Only include orders from the last 90 days." AI updates the underlying logic, tests the change against your data, and shows you the result. Accept or reject with one click.
- Edit field mappings, transformations, aggregations, and joins
- AI generates the update and tests it against your data
- See which records pass and fail before accepting
Unmapped Field Transparency
AI accounts for 100% of your destination fields. When a field can't be mapped, you get a specific reason: what data was available, what was attempted, and why it wasn't sufficient. This eliminates the ambiguity of staring at an empty mapping and wondering whether the system missed something or the data isn't there.
- Specific technical reasoning for every unmapped field
- Closest candidate fields identified with explanation
- Full destination field coverage — every field accounted for
Certification Before Production
Before a mapping runs in production, you certify it. Certification validates every component — field mappings, join conditions, transformation expressions — and locks the configuration. The certified version is what runs. Any modification after certification requires unlocking with a written justification that's recorded in the audit trail. You know what configuration is active in production and who last changed it.
- All mapping components validated before certification
- Locked configuration — the certified version is what runs
- Unlock requires written justification in the audit trail
- Version tracking — see who certified, when, and what changed