Title Pro → ACC matching
Two large datasets that each described the same properties using different identifiers — fuzzy matched, scored, and reconciled into a single canonical record set the client could query against.
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Data aggregation & cleansing
Data aggregation & cleansing
Pull from messy sources, normalize, dedupe, and turn it into something your team can actually act on.
When you need this
- You have data — but it's spread across vendor exports, third-party feeds, and PDF reports.
- Your records have the same customer listed under three slightly-different names.
- An external system updates faster than your manual import process can keep up.
- Your sales team is making decisions on numbers nobody fully trusts.
- You're paying for data you can't actually use because it doesn't line up with anything else.
What we do
- ETL pipelines from any combination of sources (CSV, XLSX, JSON, APIs, databases)
- Structured extraction from PDFs and other unstructured formats
- Public-records and third-party data enrichment (people, properties, companies)
- Deduplication, fuzzy matching, normalization rules
- Validation and confidence scoring so you know what's reliable
- Scheduled refreshes that land cleaned data where your tools expect it
How this gets built now
How I use AI coding agents on client work →
This is where AI helped me least
Writing a fuzzy matcher is a solved problem. Deciding what counts as a match is not. Whether a score auto-merges or goes to a person, whether two sources become one canonical record or get reconciled at query time — those are business calls whose consequences surface months later, as a number nobody trusts. The mechanical parts got faster. The deciding never moved, and of everything I do this is the bucket where that gap matters most.
Examples from real engagements
What this looks like
Multi-source auction aggregation
Hundreds of auction listings each week from a half-dozen different bidding platforms — pulled, normalized to a single schema, deduplicated, and made queryable through one portal.
Person vs trust classification
Property records often list a trust as the legal owner, hiding the underlying person. Built a classifier that flags trust ownership and links it back to the real beneficial owner where possible.
Got messy data that needs to be useful?
Tell me what you're trying to fix — I'll reply the same day.