B2B data & sales intelligence
Data ops automation for B2B data companies
When data is the product, every duplicate and stale field is a quality defect your customers can see. I build the pipeline layer that keeps a large contact database clean, enriched, and queryable, proven on a production database of 863,484 contacts with zero duplicates.
Where the money leaks
Duplicates erode the product
Two records for the same person means wrong counts, double outreach, and churned customers. Dedup by hand does not survive past a few thousand rows.
Enrichment burns money blindly
Paying to fill every empty field enriches noise. Without a rule for which fields actually get filtered on, the invoice grows faster than the data quality.
The data team is a queue
Every segment request and count lands on an analyst. The people who could answer questions from the data spend their week exporting CSVs instead.
What I build for b2b data & sales intelligence
Cleaning pipelines that scale
Normalization to canonical fields, exact dedup on email, fuzzy matching for the Bob-versus-Robert cases, all as repeatable scripted stages with QA checks before anything goes live.
Targeted enrichment
Fill the gaps in the fields your product actually filters by, from your existing source exports first. In one engagement that meant 1.2M+ fields enriched without a single new data purchase.
Plain-English search for the whole team
A natural-language interface over the database (custom GPT backed by SQL functions), so “how many CFOs in Texas fintech” is a ten-second question, not a ticket.
Proof, not promises
The reference build: 863,484 contacts imported, deduplicated to zero duplicates, 1.2M+ empty fields enriched, 145k net-new contacts merged, 8/8 QA checks passed, for a US sales-intelligence company whose team now searches it in plain English.
Read the full case studyCommon questions
Our data lives across several tools. Can you still work with it?
Yes. The reference project started as 20 CSV exports in two different formats. Consolidating messy, multi-source data into one canonical schema is the first stage of the pipeline, not a blocker to it.
We serve EU clients. Can this be GDPR-clean?
Yes. For a European venture-capital firm I built the contact-merge engine to run inside their own EU platform, so the data never left their infrastructure. The same pattern applies to any compliance boundary: the pipeline moves to the data, not the other way around.
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