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Seamline

CRM data cleansing that fixes the cause, not just the duplicates

Duplicate companies, ARR that doesn't match Stripe, tickets on the wrong account. We clean your CRM, link it to billing and support, and fix the sources that keep creating bad data, so the cleanup still holds six months later.

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When teams call us

  • The CRM has duplicate companies, and nobody is sure which record is the real one
  • ARR in HubSpot doesn't match what Stripe bills, and finance and sales each defend their own number
  • Paying customers are missing in the CRM, or churned customers still show as active
  • Support tickets and invoices aren't linked to the right account
  • You cleaned the CRM last year, and it's messy again
  • You want to put AI or automation on top of your data, but don't trust what's underneath

How we help

1. Revenue Data Diagnostic

Fixed scope · 2 weeks · from €1,500

A diagnostic of how your CRM, billing and support data connect, and where they don't.

You get:

  • How many customers match across systems, and how many don't
  • The gap between CRM ARR and billing ARR, with the main reasons
  • Where duplicates and bad records come from: which forms, integrations or workflows
  • A prioritised plan for the next 90 days

2. CRM cleanup & customer linking

Fixed price, scoped after the Check

We clean the data and fix what creates the mess.

  • Deduplication, with rules for which record wins
  • One customer ID linking CRM, billing and support records
  • Ownership, lifecycle stages and field definitions agreed with your team
  • One writer per field, and a short weekly review of new records that don't match

3. Revenue reconciliation

Set up once, run monthly

A monthly check that explains, customer by customer, why CRM and billing disagree.

  • ARR defined once, agreed with finance and sales
  • Every difference gets a reason and a fix at the source
  • ARR written back to the CRM by the sync, not by hand
  • The list of differences sent to an owner before the board pack

How it works

  1. Start with one question.

    For example: "Why doesn't our HubSpot ARR match Stripe?" or "Which renewals are at risk?"

  2. Map the systems.

    Which records exist where, how they connect, and where they break.

  3. Fix at the source.

    Clean the data, link the customer, and change the forms, workflows or integrations that cause the problem.

  4. Hand over.

    Everything runs in your own tools and is documented, so your team can maintain it without us.

What clean data makes possible

Clean data isn't the goal. It's what lets you trust the next step: asking questions in plain language, getting answers you can act on, and automating the work that follows.

Why it has to come first

Connect an AI assistant to systems that disagree on who the customer is, and it will still give you an answer.

Ask, "Is Brightpath healthy?" It reads the CRM, finds a friendly call from three days ago, and says yes. But it misses that "BRIGHTPATH LEARNING B.V." in Stripe is the same customer — and that they've just reduced their seats.

The answer is confident, fluent and wrong.

When every system's customer record is linked to the same canonical customer ID, those questions become much easier to answer — and the answers become easier to verify.

  • "Which customers renewing this quarter have an unpaid invoice or an urgent ticket?"A list in seconds, with the relevant invoice and ticket linked to each customer, instead of three exports and an afternoon in a spreadsheet.
  • "What changed at Northwind since our last call?"A short briefing before the meeting: payment status, open tickets, usage trends and contract changes. Every finding points back to its source.
  • "Why is ARR lower than last month?"A breakdown by customer and reason — churn, downgrades or discounts — instead of a number someone has to explain manually.
  • "Which accounts look healthy in the CRM but not in billing?"The discrepancies between systems, surfaced before they become surprises.

Then, automation — step by step.

We start with answers people can verify. Once the underlying data and business rules are reliable, we can move from surfacing information to recommending actions, drafting responses and automating workflows.

Each step earns the next.

What we don't do

  • Put AI on top of data that isn't ready for it
  • Buy, clean or enrich contact lists
  • Replace your CRM or sell you a new platform
  • Lock you into a monthly retainer you don't need

Tools we work with

HubSpot · Stripe · Chargebee · Zendesk · n8n · PostgreSQL / Supabase · Slack · Claude

Using something else? Ask. The method is the same; the connectors differ.

See an example: the pre-call brief, built with n8n →

See the method

Worked examples with fictional data:

Why doesn't your HubSpot ARR match Stripe?How do you build a customer health score from CRM, billing and support data?Which accounts approaching renewal have overdue invoices and open tickets?

Frequently asked questions

Start with one question

Tell us which numbers or records your team doesn't trust. We'll map the systems involved and show you what it would take to fix it.

Book a data diagnostic →