Company exports are riddled with inconsistencies — industries in three languages, headcount ranges that don't match your picklist, country codes mixed with full names. Asphorem maps and normalises everything before you import.
Your file rows are never uploaded. AI matching uses unique column values only.
Part of the CSV Normalizer — Asphorem's tool for standardising any CSV data.
Company data comes from too many sources — CRM exports, data providers, manual spreadsheets. Each source has its own conventions, and none of them match your target system.
Upload your company file, define the target schema, and let AI normalise the values. Your company data never leaves your machine — only unique picklist values are shared with the AI.
Drop any CSV — from a data provider, a CRM export, or a manually-built spreadsheet. Asphorem detects the columns and shows you a preview of what's inside.
Tell Asphorem which column maps to which field in your CRM or database. AI suggests the most likely match for each column — you confirm, adjust, or skip.
Define the allowed values for each picklist field. The AI maps every cell — catching cross-language variants, abbreviations, and inconsistent formats across the entire file.
Your company list has standardised column names, consistent picklist values, and the same row order as your input. Import it into your CRM without hitting validation errors.
Asphorem is not a connector — it prepares the file before you import it, so it works with any platform that accepts CSV uploads.
Your company file is loaded and processed locally — no rows are transmitted to our servers. Company names, domains, revenue figures, and every other field in your file stay on your machine.
When you use AI matching for picklist columns (like industry or company size), only the unique values found in that column are sent to the AI. Not the full rows, not the company names or domains — just the distinct values that need normalising.
If a picklist column contains values you'd prefer not to share, you can always map it manually instead.
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