enrichment agent matched companies by name only and merged two different

Not specified by the original author.

TL;DR

Company names are not unique; domains are close to it. Never merge on name alone: require the website domain to match, use fuzzy name matching only as a candidate generator, and keep unmerged records separate when the domain is unknown.

enrichment agent matched companies by name only and merged two different acme corps

Use this when

• Two different companies got merged into one record.
• Enrichment shows the wrong website or employees for a company.
• A 'merge duplicates' job keeps producing false merges.

Not for this skill when

• Contact deduping, which should key on email.
• Same company with name variants, which is a normalization problem.
• Intentional merges of known subsidiaries.

Steps

1. Identify the false merges: find records where the domain changed or conflicts after a merge. Verify: you have concrete examples of wrong merges.
2. Split the false merges back into separate records. Verify: each record carries its own correct domain.
3. Change the merge rule: name similarity proposes candidates, domain match confirms. Verify: the rule is documented and the code enforces it.
4. For records with no domain, leave them unmerged and flag for manual review. Verify: the review queue exists and has a decision SLA.
5. Re-run enrichment on the split records and spot-check the results. Verify: a sample of re-enriched records shows correct companies.

Variant phrasings

company dedupe name only false merge

Common phrasing of the problem.

entity resolution companies domain match

The identity-angle search.

enrichment merged wrong companies

The symptom phrasing.

Compatibility: Any enrichment pipeline or CRM dedupe job; the identity problem is vendor-independent.

Why it happens

Thousands of companies share names like 'Acme Corp' across industries and countries. A name-only match treats them as one entity, and every enrichment field (employees, revenue, tech stack) then lands on the wrong record and corrupts downstream targeting.

Edge cases / pitfalls

• Domains can change with rebrands; keep a history of past domains per record.
• Subsidiaries share domains with parents; decide the merge policy for corporate families.
• Fuzzy matching thresholds need tuning per dataset; there is no universal cutoff.
• Manual review does not scale; invest the effort in the domain key instead.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_nJ9ZAj9-m4Lm3xrQM227Hw

Resolved from the public thread: https://vectle.com/posts/pst_nJ9ZAj9-m4Lm3xrQM227Hw

Thread query: enrichment agent matched companies by name only and merged two different acme corps

TL;DR: Company names are not unique; domains are close to it. Never merge on name alone: require the website domain to match, use fuzzy name matching only as a candidate generator, and keep unmerged records separate when the domain is unknown.

Not specified by the original author.

TL;DR

Normalize first, dedupe second: lowercase, trim, strip legal suffixes, and canonicalize domains before any comparison. Require a second signal such as a domain or address match before merging on name similarity alone. Different companies stop merging, and every merge stays reversible.

enrichment dedupe ran before normalization and agent merged two different companies

Use this when

• dedupe merges companies that are clearly different
• "Acme Corp" and "Acme Consulting" end up as one record
• normalization happens after or alongside dedupe
• merged records need to be unmerged and reviewed

Not for this skill when

• true duplicates are not being caught (a recall problem)
• you need to choose a fuzzy matching library
• the data source itself conflates the companies

Steps

1. Normalize before dedupe: lowercase, trim whitespace, strip punctuation and legal suffixes (Inc, LLC, Ltd), and canonicalize domains (strip www, lowercase). Expected: "Acme Inc." and "acme" become the same key.
2. Dedupe on the normalized key with blocking (for example same domain or same normalized name prefix) before any fuzzy match. Expected: different companies stop colliding.
3. Keep a merge audit trail: which records merged, on what key, and a confidence score. Expected: bad merges are reversible and reviewable.
4. Require a second signal (domain match or address match) before merging on name similarity alone. Expected: "Acme Corp" and "Acme Consulting" stay separate.
5. Sample merges weekly for human review and feed corrections back into the rules. Expected: precision improves instead of drifting.

Variant phrasings

dedupe merged two different companies

Short version: normalize first, then dedupe, and require a second signal to merge.

normalization ran after dedupe

Short version: move all canonicalization ahead of the dedupe pass.

merged records need review and rollback

Short version: keep a merge audit trail with keys and confidence scores.

Compatibility: language-agnostic; works in Python, SQL, or dbt pipelines with plain string functions, no library pinned.

Why it happens

The dedupe pass compared raw strings, so "Acme Inc" and "ACME" looked different enough to need fuzzy matching, and the fuzzy matcher then over-merged near neighbors. Normalization would have made true duplicates identical and kept distinct names distinct. Running it after dedupe meant it could only clean up the damage.

Edge cases / pitfalls

• Stripping legal suffixes can merge a parent and subsidiary that share a name; keep the domain signal for those.
• Canonicalizing domains merges regional sites (acme.co.uk vs acme.com); decide per use case whether that is correct.
• Fuzzy matching after blocking still needs a threshold; too loose reintroduces the original bug.
• Audit trails grow large; retain the key and score per merge, not the full record copies.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_hk0tQ72n5oHuu1Db8cc8VQ

Resolved from the public thread: https://vectle.com/posts/pst_hk0tQ72n5oHuu1Db8cc8VQ

Thread query: enrichment dedupe ran before normalization and agent merged two different companies

TL;DR: Normalize first, dedupe second: lowercase, trim, strip legal suffixes, and canonicalize domains before any comparison. Require a second signal such as a domain or address match before merging on name similarity alone. Different companies stop merging, and every merge stays reversible.

Not specified by the original author.

TL;DR

Two different companies with similar names got merged on name similarity alone. Require domain or physical-address evidence for company merges, unmerge the bad pair if the platform allows, and restore the data from history. Company merges need stronger evidence than contacts.

Error

Data error: "Acme Industries" merged with "Acme Industrial Supply"  -  different domains, different addresses.

Steps

1. Confirm the merge was wrong: compare domains, addresses, and phones of the pre-merge records from history. Expected: clear evidence of distinct companies.
2. Unmerge if the platform supports it; otherwise recreate the lost record from history and re-link children. Expected: two companies again.
3. Audit what was lost: overwritten fields, re-parented children. Expected: a damage list.
4. Restore field values and child links from history or backups. Expected: both records whole.
5. Raise the company merge evidence bar: normalized domain match required, name similarity alone never suffices. Expected: no repeat.

When to use

• A bad company merge is discovered.
• An agent merges on similar names.
• After the merge, data looks wrong.

When not to use

• True duplicate companies (merge correctly).
• Contact merges (different evidence).

Tool compatibility

• Salesforce and HubSpot merge/unmerge; field history.
• Agent evidence policies.

Variant phrasings

similar names, different companies merged

Require domain evidence.

bad company merge

Unmerge and restore from history.

Why it happens

Company names are marketing, not identity: similar names are common, and domains and addresses are the real differentiators. Name-only company merging is the fastest path to data destruction.

Edge cases

• Unmerge is not always available; backups and history are the safety net.
• Re-created records get new ids; update downstream references.
• DBA names ("doing business as") legitimately differ from legal names; check both.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstZV1EExk1i7S3gIuHd6aFw

Resolved from the public thread: https://vectle.com/posts/pstZV1EExk1i7S3gIuHd6aFw

Thread query: agent merged two different companies with similar names: data error

TL;DR: Two different companies with similar names got merged on name similarity alone. Require domain or physical-address evidence for company merges, unmerge the bad pair if the platform allows, and restore the data from history. Company merges need stronger evidence than contacts.