AI Cleans Your Data
Dirty data produces wrong decisions, failed automations, and embarrassing outreach errors. AI cleans, standardises, and validates your data at scale — turning the messy reality of business data into a reliable foundation for every system that depends on it.
And Their AI Solutions
Duplicate contacts and accounts
CRMs accumulate duplicates: the same contact added by two reps, the same company entered with slightly different names (SA Solutions vs SA Solutions Pvt Ltd vs SA Solutions Pakistan), or the same email address attached to two different contact records. AI identifies duplicates using fuzzy matching — finding records that are probably the same person or company even when they are not exact matches — and merges them, preserving all associated activity and data from both records. Deduplication that takes a week of manual review takes hours with AI.
Inconsistent formatting
Phone numbers stored in 6 different formats (with country code, without, with spaces, with dashes), company names with inconsistent capitalisation, addresses in different formats, and job titles with hundreds of variations of the same role (VP, VP of Sales, Vice President Sales, VP-Sales). AI standardises all of these to a consistent format: phone numbers to E.164 international format, company names to title case with consistent legal entity handling, job titles to a normalised taxonomy. Every downstream system that depends on this data works more reliably.
Incomplete and missing data
Contacts without email addresses, companies without industry classification, accounts without country codes. AI identifies the records with the most impactful missing data (based on which fields are used in your key workflows and automations) and either fills them from enrichment APIs or flags them for targeted manual completion. A data completeness score for every record: 100 percent complete records are fully usable in every automation; incomplete records are filtered to appropriate workflows only.
Outdated and stale records
Contacts at email addresses that now bounce, companies that have been acquired or closed, phone numbers that are no longer valid. AI identifies staleness signals: email hard bounces (mark as invalid), LinkedIn profile URL returning 404 (contact may have left), company website returning an error (company may have closed). Stale records are flagged and either updated from enrichment sources or archived — removed from active use without being deleted permanently.
Make.com Architecture
Audit your current data quality
Before cleaning, measure: what percentage of contact records have a valid email address, what percentage of company records have an industry classification, how many duplicate records exist (check by email domain and company name), and what is the hard bounce rate on your most recent email campaign. This baseline tells you where the worst data quality problems are and how to prioritise the cleaning effort.
Build the deduplication workflow
Make.com scenario: export all contacts, group by email domain and company name, pass groups of potential duplicates to Claude: These records may be duplicates. Identify which are the same person or company, and for the duplicates, which record should be the primary (most complete, most recently updated) and which fields from the secondary records should be merged into the primary. Generate merge instructions. The merge instructions are executed in your CRM via API — duplicates collapsed automatically with complete data preservation.
Standardise format across all records
Build Make.com scenarios for each formatting standardisation task: phone number normalisation (regex cleaning + country code addition from the contact's country field), company name standardisation (Claude applies title case and legal entity standardisation rules), job title normalisation (Claude maps raw job title strings to your standard taxonomy), and address formatting (consistent country name, postal code format). Run these standardisation workflows on all existing records and as an automated check on every new record created.
Implement ongoing data quality monitoring
Data quality degrades continuously without active maintenance. Monthly Make.com scenario: calculate the data quality score for each record (percentage of key fields populated with valid data), identify records whose quality score has dropped since last month (email bounced, phone invalid), and flag for re-enrichment or manual review. A data quality dashboard in Bubble.io shows the current quality distribution — the percentage of records at each quality level — and the trend over time.
How do I clean data without losing important history?
Never delete when you can archive. Mark records as inactive or invalid rather than deleting them. Merge duplicates by consolidating all activity and data from both records into the primary record rather than deleting either. Store the raw original data before cleaning so that any cleaning error can be reversed. Data cleaning should be reversible in the first 30 days; archive permanently only after the cleaned data has been running reliably through your systems.
What is the best order to tackle data quality problems?
Priority order: (1) Email validity (invalid emails break outreach automations immediately), (2) Deduplication (duplicates cause embarrassing double outreach and corrupt reporting), (3) Missing required fields for active workflows (incomplete records that block automation), (4) Formatting standardisation (improves downstream system reliability), (5) Stale record flagging (ongoing maintenance). Fix what breaks things first; fix what causes embarrassment second; fix what improves reliability third.
Want Your Business Data Cleaned and Maintained?
SA Solutions builds AI-powered data cleaning pipelines — deduplication, format standardisation, completeness scoring, and ongoing quality monitoring — for CRMs, Bubble.io databases, and spreadsheet-based data.
