Tools Solutions Pricing Blog
Log in Start for free
Asphorem for Sales Ops

Pipeline data that imports clean, every time

Sales Ops keeps the pipeline trustworthy, which means catching every bad deal stage, mismatched account name, and inconsistent territory value before it corrupts a forecast. Asphorem standardises sales data at the file level.

File rows never leave your browser. AI matching uses unique column values only.

Works with any CSV-based import

HubSpot Salesforce Pipedrive Zoho Shopify Klaviyo + any tool that accepts CSV

What slows Sales Ops down

A forecast is only as trustworthy as the values feeding it, and uploads rarely arrive clean.

Deal stages that don't match the pipeline

An imported file says 'Negotiation', your pipeline stage is 'Negotiation/Review'. The deal imports with no stage and drops out of the forecast.

Territory and segment values fragmented

'EMEA', 'emea', and 'Europe' as three values means territory reports and quota rollups are wrong.

Account lists that will not dedupe

Account names formatted differently across files never match, so the same company imports two or three times.

Spreadsheet cleanup before every upload

Every territory realignment or list buy means manual reformatting before the data is safe to import.

How Asphorem fixes it

Match every incoming value to your pipeline, and review it before anything is written.

01

Match values to your exact pipeline

Define your canonical deal stages and segments once. The AI maps every incoming value to them and flags anything it cannot place.

02

Reusable mappings for recurring uploads

Territory files and list buys arrive in the same shape every time. Save the mapping and skip straight to a clean file.

03

Split and restructure columns

Split a combined column into separate outputs, or add a constant column, so files match your CRM schema without manual editing.

04

Review before anything imports

Every value substitution is shown before export. Fix what is wrong with one click, and the fix is saved for next time.

The AI that actually understands your data.

Not just fuzzy search. Asphorem's AI matches picklist values semantically, across typos, abbreviations, different languages, and numeric range formats.

Raw CSV values
Deal Stage
closd won Closed - Won CLOSED WON Won
Close Date
15/01/2024 Jan 15 2024 2024-01-15T00:00
Industry
tech TECHNOLOGIE IT
Your canonical values
Deal Stage [Picklist]
Closed Won
Close Date [Date]
2024-01-15
Industry [Picklist]
Technology
Cross-language "TECHNOLOGIE" → "Technology" · "Santé" → "Healthcare"
Typos & variants "closd won", "Closed - Won", "Won" → "Closed Won"
Date format normalization "Jan 15 2024", "15/01/24" → "2024-01-15"
Manual override Review and correct any match individually, or bulk-reset unmatched values in one click
Local matching fallback Disable AI and run name normalisation locally, for offline use or sensitive data
EU & US number formats "1.500,50" or "1,500.50" → normalised output regardless of input style

When this matters

The bulk uploads where one wrong value quietly distorts the whole pipeline.

Annual territory realignment

Reassigning accounts means a bulk update file with consistent territory values. Normalise before the import runs.

Importing a purchased account list

A bought list never matches your segment and industry values. Clean it to your schema first.

Migrating deals from a legacy CRM

Old deal stages will not match the new pipeline. Map every legacy value to a canonical stage before import.

Cleaning a partner-sourced opportunity list

Partner deal data arrives in their format. Standardise stage, value, and account fields to yours.

Common questions from Sales Ops teams

What if an incoming file has deal stages that are not in my CRM?

Asphorem flags any value it cannot match to your canonical list. You decide: map it to an existing stage, add it as a new canonical value, or mark it for manual review. Nothing imports with an unresolved stage unless you approve it.

Can I add a territory or owner column to the output that was not in the source file?

Yes. Add a constant column with any label and value you define. Every row in the output gets that value. This is useful for tagging imported records with a region, team, or data source before they reach your CRM.

How do I clean exports from a legacy CRM with completely different field names?

Map each old field name to your target field name, set the column types, define your canonical values, and save the configuration as a preset named after the source system. Every future export from that system applies the preset automatically, with no manual reformatting.

Do I have to re-clean historical files when my pipeline stages change?

No. Updating your canonical mapping only affects future files. You can re-process historical exports through the new mapping if needed, but existing clean files are not changed automatically.

Clean your first file as Sales Ops

Free plan included. No credit card required.

Start for free →