Mesrop's Team

Data Ingestor turns the hours a data curator spends hand-reformatting every CRO's differently-shaped Excel/CSV into a one-click review. The pain I keep meeting at the scientists I work with: the ingest parser is hardcoded to one vendor. Each new vendor needs its own bespoke parser, and when a vendor quietly changes its Excel, last quarter's parser breaks. Maintaining that pile is the real cost. Data Ingestor has no per-vendor code. You declare the fields you need; Claude judges the file's structure — where the table actually starts, whether a sheet is row-per-record or key-value — and proposes a column mapping with honest per-field confidence and a plain-English reason for each pick. Anything uncertain is flagged yellow, never silently guessed. Nothing is saved until a human clears every field. The design rule: Claude judges, Python reads. The model may see a bounded sample in order to judge, but every exported value is read from the file by deterministic code; a no-LLM validator re-checks it against your declared constraints. Beyond a wrapper: a learning loop (a repeat file from the same lab auto-maps at full confidence with zero Claude calls), a canonical schema + vendor-alias crosswalk with provenance, and a headers-only privacy mode.

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