A nightly AI agent sweeps the CRM for duplicates, missing fields and stale deals, merging and enriching records automatically. Each morning the sales team gets a short Slack digest — about seven judgment calls instead of hundreds of messy records.
Typical results
96%
Field completeness, up from 61%
7
Items for human review each morning
16 hrs
Monthly cleanup eliminated
Figures reflect typical results for this kind of automation.
The challenge
With 50 salespeople writing into one CRM, entropy always won: 2,400 duplicate records, required fields blank on four deals in ten, and zombie opportunities inflating the forecast. An ops analyst burned two days a month on manual cleanup, and reps still called prospects a colleague had already closed.
Before and after
Duplicate records in CRM
−91%Required-field completeness
+57%Monthly manual cleanup
−100%The automation
We built an AI agent that runs every night on a Make schedule. It fuzzy-matches duplicates and merges the safe ones with full history preserved, enriches missing company and contact fields from licensed data sources, and flags deals that have sat past their close date with no activity. Anything ambiguous — a risky merge, a deal worth a conversation — lands in a morning Slack digest with one-click actions for the ops team.
How it works
- 01
Nightly sweep
At 2 a.m. the agent scans every account, contact and deal touched that day.
- 02
Duplicates merged
Fuzzy matching finds duplicate records; safe merges happen automatically with full history preserved.
- 03
Gaps filled
Missing company and contact fields are enriched from licensed data sources.
- 04
Stale deals flagged
Opportunities sitting past their close date with no activity get flagged or moved by rule.
- 05
Morning digest
The team wakes to a Slack summary and about seven judgment calls — not hundreds of messy records.
Starts from
- CRM records
- Enrichment sources
Delivers
- Merged & enriched records
- Morning Slack digest
- Human review queue
The impact
Duplicates dropped 91% — from 2,400 records to around 210 — and required-field completeness climbed from 61% to 96% within two months. The two days of monthly manual cleanup are gone, the digest averages seven items, and the pipeline finally reflects deals that actually exist.
Questions about this automation
How does the agent decide which duplicates are safe to merge automatically?
Fuzzy matching scores each pair of records on email, domain, company name and phone. Pairs above a high-confidence threshold merge automatically, keeping the full activity history and the most recent field values. Anything below it, or any merge that touches an open deal, goes to the morning review queue for a person to approve with one click.
Can a bad merge be undone?
Yes. Before each merge the agent stores a snapshot of both records and logs what changed, so the ops team can restore the originals from the log. Because ambiguous cases go to review instead of being merged, reversals are rare, and the threshold can be raised if the team wants the agent to act more cautiously.
What happens to deals that sit past their close date?
The team sets the rules. A deal past its close date with no logged activity for a set number of days can be flagged for its owner, moved to a stalled stage or listed in the digest for a decision. Nothing is closed out silently, and every change the agent makes is visible on the deal record.