CRM CLEANUP PASS · FIXED SCOPE

You inherited the CRM.
We clean it without breaking anything.

Years of other people's imports. Duplicates everywhere, fields nobody defined, lifecycle stages that were never set up. We take the mess off your plate — every change logged and reversible, counts reconciled to ±0 — so you can show exactly what was done, row by row, if anyone asks.

Sound familiar?

Three ways this usually starts.

The inherited wreck

"Mess" is too small a word for it.

New role, super admin access, and years of hands that didn't know the platform. Every contact marked as a lead. Properties nobody uses. Everyone expects you to fix it — nobody can tell you where to start.

  • We start where the data actually is: a scoped pass on your worst object first
  • You get a base you can trust plus the paper trail to show for it
Duplicate rot

Duplicates keep coming back.

You spent hours last week merging contacts by hand. This week there are more. Two reps called the same lead. Attribution doesn't add up.

  • Dedupe on an agreed key — zero duplicate pairs survive, verified by script
  • Ambiguous pairs are flagged with reason codes — never silently merged
Stuck email results

The list burns money either way.

Warming up since May, performance barely moves. A dirty base isn't the only cause of bad deliverability — but it's the one you can fix this week.

  • We remove disengaged and duplicate records — a necessary condition, not a magic fix
  • Honest boundary: we don't promise open-rate growth. Nobody honestly can
What we do

One pass. Nothing open-ended.

Acceptance criteria are agreed before work starts — and never softened afterwards.

What we do

Dedupe
on an agreed key — email, domain, or account name, normalized first
Normalize
whitespace and formula-injection guard — every edit logged, rule by rule
Flag
conflicts and ambiguous matches get reason codes, not silent "resolutions"
Guard
formula-injection neutralized so the file can't weaponize your spreadsheet

What comes back

Clean file
import-ready, same schema as your export
Change log
every edit before → after, reversible
Exceptions
rows needing your judgment, with codes
Reconciliation
input = clean + merged + exceptions, exactly ±0
README
a colleague can audit it without talking to us

What we will not guess

Ambiguous
if two rows might be different people, they get flagged — you decide
Conflicts
two different values for one record = exception, never auto-pick
Access
sandbox or file export only. We never ask for production access
Data
NDA on your paper; deletion on request — verified against the disk and confirmed in writing
Proof, not promises

Trust is a checklist, not a vibe.

Shape of a real change log entry (dummy data):

# changelog.csv — cleanup pass, demo dataset
row_id,field,before,after,action
00412,email,"  bob@acme.com  ",bob@acme.com,trimmed_whitespace
01033,[whole row],dup_of:00412,,merged
02177,email,bob+crm@acme.com,EXCEPTION,plus-tag vs base address → your decision
03402,email,,EXCEPTION,null-key → excluded

# scripted identity, ±0:
input(1821) = clean(1183) + merged(10) + exc(628)
Judge the work, not a client list: criteria agreed before work starts, a full change log after it, and a reconciliation you can re-run yourself — proof you can inspect. No invented client stories.

Numbers above are from a demo run on synthetic data — labeled as such, because inventing client cases is how this industry got its reputation.

FAQ

Questions people actually ask.

How fast?

Small sample checks can be turned around quickly when the file fits. Larger cleanup work is quoted as a fixed-scope batch after we see the file, declared key and acceptance criteria.

Can you set up lifecycle stages / properties too?

We prepare the data so those decisions become safe: staged values, documented rules, flagged conflicts. Platform configuration itself stays on your side — we don't touch production systems.

What about 50k+ contacts?

≤10k per batch is a pricing/turnaround boundary, not a technical ceiling — bigger bases run as a series of scoped batches, or a larger fixed-scope engagement, after a paid pilot proves the rule set on a real slice of your data.

ScopedFixed price after file review

Start with the messiest export you have.

Anonymize it if you like — the method shows either way. We reply with either a small sample-proof path or a fixed-scope cleanup quote. Agencies with migration work should use the $750 migration-data QA pilot.

Reply within 24h · prefer email? hello@amessystems.com

Related

Other places this bites.

Migration

Moving systems soon?

Clean and map before the import — whatever gets imported now, you live with for years.

Reporting

Two teams, two numbers?

That argument ends with scripted reconciliation.

Objections

"We'll just use GPT."

Fine for simple cases. See where DIY stops being enough.