One Customer Record,
Every System Agreeing.
LedgerUp keeps customer, product, and pricing data consistent between your CRM and your ledger — no duplicate customers, no stale rates, no invoice errors born from bad reference data.
Data drift vs data discipline
Most invoice errors aren’t billing errors — they’re reference-data errors that billing faithfully executed.
Without automation
- “Acme Corp,” “Acme Corporation,” and “ACME” as three QuickBooks customers
- Sales updates the deal; the ledger keeps billing the old rate
- New customers keyed into the ERP by hand, typos included
- Last-minute deal changes never reach the invoice
- Nobody owns reference data, so everyone cleans it annually
With LedgerUp
- One customer identity across CRM and ledger — duplicates detected and merged
- Contract terms drive pricing; changes propagate to billing automatically
- New customers created in the ledger from CRM and contract data, correctly
- Deal-term changes reconciled against billing before the next invoice
- Drift caught continuously, not at year-end cleanup
How customer data sync works
Reference data that stays right, so invoices start right.
Records matched across systems
Ari links each customer’s CRM record to its ledger counterpart — matching on identity, not string equality, so “Acme Corp” and “ACME Corporation” resolve to one customer instead of becoming a duplicate.
New customers created once, correctly
When a first deal closes, the ledger customer is created from CRM and contract data — legal name from the signed agreement, billing address and contacts from the deal record. No hand-keying, no typos.
Pricing flows from contracts, not memory
Contracted rates, discounts, and terms are the billing source of truth. When an amendment or renewal changes them, downstream billing updates — the stale-rate invoice class of errors disappears.
Drift detected before it bills
A rep edits deal terms after signature; an address changes; a duplicate sneaks in. Ari surfaces the inconsistency — CRM says X, contract says Y — for a quick resolution before the next invoice inherits it.
Works with your existing stack
LedgerUp connects to the tools you already use — no migration required.
Data sync use cases
Where clean reference data prevents downstream pain.
Duplicate Customer Cleanup
Years of manual entry leave most QuickBooks files with duplicate customers fragmenting payment history. Ari detects likely duplicates and manages merges — then prevents new ones.
Three “Acme” variants merge into one customer with consolidated invoice history; the next “Acme” deal maps to it automatically.
Last-Minute Deal Changes
A rep changes seats or discount the day before signature. The signed contract, the CRM, and billing get reconciled — whichever was stale gets flagged instead of billed.
The contract says 45 seats, the CRM still says 40; Ari flags the gap, the deal record updates, and the invoice bills 45.
Multi-System Onboarding
A new customer needs to exist in the CRM, the billing system, and the ledger with the same identity. One close-won event creates the aligned records everywhere.
Close-won at 2 PM: customer exists in QuickBooks with legal name from the contract, billing contact from the deal, and the first invoice queued.
Clean data is what makes automation safe
Every automated workflow downstream depends on reference data being right.
Invoicing
Invoices are only as correct as the customer and pricing data behind them.
See invoice automationAll integrations
The CRM, billing, and ERP connections that sync runs across.
See integrationsCPQ to invoice
The quote-to-billing handoff that depends on matched records.
See CPQ-to-invoiceCustomer data sync FAQ
Common questions about keeping customer data clean with LedgerUp.
Which systems does the sync cover?
HubSpot and Salesforce on the CRM side; QuickBooks, NetSuite, Sage Intacct, Xero, and Stripe on the billing and ledger side. Customer identity, billing contacts, addresses, and contracted pricing stay consistent across whichever combination you run.
How does duplicate detection work?
Matching on identity signals — normalized names, domains, addresses, existing cross-references — rather than exact strings. Likely duplicates surface for a human merge decision; confirmed matches merge with history preserved, and future records map to the surviving identity.
Which system wins when records disagree?
By field, per your rules. A common setup: the signed contract governs pricing and legal identity, the CRM governs contacts and relationship data, the ledger governs accounting categorization. Conflicts outside the rules flag for a decision rather than silently overwriting.
Does the sync overwrite data in our systems?
Only per the precedence rules you set, and every change is logged with its source and reason. Ambiguous cases flag instead of writing — the goal is no surprising edits, ever.
How does this prevent invoice errors specifically?
The most common invoice errors — wrong rate, wrong entity name, wrong contact, wrong quantity — are reference-data errors. When pricing comes from contracts, identity from a matched record, and changes reconcile before billing, that error class largely disappears.
Can we start with a cleanup, then keep it clean?
Yes — that’s the typical adoption path: an initial duplicate-detection and reconciliation pass over existing records, then continuous sync keeps the cleaned state from regressing.