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Not all data comes from an API. Plans and offline measurement arrive as files: a media plan workbook, an audience panel export, a TGI extract. These files are human-maintained — columns get renamed, calculations get invented, empty rows and merged cells creep in, and the France file rarely matches the Germany one. File-Source Ingestion is the toorow layer that turns any such file into standardized, canonical data at the right grain, and places it correctly so it can be reconciled against your API-sourced actuals. Upload and inbound email are the same pipeline — a file dropped in the UI and a file emailed to a datastream address are ingested identically.

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1. The template is what you want, not what the file looks like

A datastream declares a template: the canonical fields to recover, the grain (e.g. daily), and where the data sits in your matrix. The template is the invariant; the variable file is mapped onto it. Two differently-laid-out files (FR vs DE) mapped to the same template yield the same canonical fields. A template is realized one of two ways: Either way, the result is locked as a versioned template and replayed identically on every later file — by upload or by email.

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2. Placement in the matrix — planned vs actual vs extrapolated

A file only becomes useful when toorow knows where it belongs. Every file source declares a class and its coordinates (metric / dimension / period): This placement is what lets toorow put a media plan (planned) next to your connector data (actual) and compute plan-vs-actual variance. A file with no declared class is refused rather than landing as orphan data.

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3. Robust to messy, drifting files

Recognition is tolerant — it maps columns by meaning, with a confidence score, not by position or exact name. It:
  • ignores added columns, empty rows, line breaks, and subtotal/total rows;
  • re-attaches renamed or differently-languaged columns;
  • detects the header even when it is not the first row, and reads a top label: value metadata block;
  • handles several variants in one datastream — a declared cell (e.g. a FR marker) or the filename becomes a market/country dimension, so France and Germany land on the same canonical fields.
When the file needs restructuring to reach the grain — for example a per-line date range exploded into daily rows — toorow spreads the values deterministically (sum-preserving, to the cent), so planned data lines up with daily actuals.

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4. Standardization is controlled — nothing is silently dropped

Before a template locks, a validation gate checks that every required canonical field was recovered at the right grain. If a required field is missing or a mapping is ambiguous, toorow flags it for you to confirm — you see the mapping result and the gate in the interface. You (optionally with your AI assistant) resolve it; only then does the template lock. On later files, benign drift (an added column) still lands; a required field that genuinely disappears flags for re-confirmation.
File-Source Ingestion stops at standardized canonical rows. Business metrics, currency conversion, and reconciliation itself run downstream in the modeled layer — this layer’s job is to recover the right data, at the right grain, correctly placed.

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5. In short

  1. Declare a template — pick a catalog one, save your own, or let your AI assistant build an adaptation.
  2. Send the file — upload it or email it to the datastream address (same result).
  3. toorow recognizes, reshapes to grain, validates the required fields, and places it in your matrix.
  4. It reconciles against your actuals — the plan and the observed sit side by side.