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datatruth Get your Truth Score

Accounting · Sage Intacct data quality for AIIntacct slices any way you like. If the dimensions were tagged.

Sage Intacct’s dimensional model is its power and its fragility. Departments, locations, projects and classes let you cut results any way you need, but only when they are applied consistently. We compare Intacct exports across entities, and against your CRM and payroll, to show whether a model could reason over them.

Get your Truth Score What to export

Starts with a 30-minute call. Commits you to nothing. Then exports only: no login, no connected app, nothing installed.

Illustrative example, not a client.

Source of truth

What Sage Intacct gets right.

Every system is built to be right about something. Knowing what Sage Intacct owns, and what it doesn’t, is where a cross-system check starts, and where the Truth Score™ (a score out of 100 for how far your data can be trusted) begins to take shape.

  • 01

    Consolidated results

    GL balances across entities, with eliminations.

  • 02

    Dimensional reporting

    Results by department, location, project, class and custom dimensions.

  • 03

    Payables and receivables

    Bills, invoices, payments and open balances.

What it isn’t the truth for

Operational truth. Intacct records what was posted; whether a project is on track, or a customer active, lives in the systems that feed it.

Inside the system

Common Sage Intacct data problems for AI.

What goes wrong inside Sage Intacct itself, with the objects and fields where it shows. Each one is checkable from an export.

  1. 01

    Dimensions that mean different things

    The same department, location or class value means something different in each entity, so consolidated comparisons and models lose meaning.

    DEPARTMENTIDLOCATIONIDCLASSIDcustom dimensions

  2. 02

    Untagged transactions

    Journal entries, bills and invoices posted without the department, project or class a model needs to slice results.

    GLENTRY.DEPARTMENTIDPROJECTIDCLASSID

  3. 03

    Duplicate vendors and customers

    Counterparties created per entity or by hand split spend and receivables history.

    VENDORIDCUSTOMERIDNAMEVAT number field

  4. 04

    Inter-entity mapping

    Inter-entity transactions and due-to, due-from relationships set up inconsistently distort eliminations.

    inter-entity accountsENTITY

  5. 05

    Statistical accounts used unevenly

    Non-financial measures captured inconsistently or mixed with financial postings make per-unit metrics unreliable.

    statistical accountsSTATJOURNAL

Between systems

Sage Intacct looks clean on its own. The gaps are next door.

The problems that sink AI sit between Sage Intacct and the systems beside it: the same customer, person or transaction recorded differently in each. These are the gaps to test for each pairing.

Accounting

Sage Intacct

Source of truth forConsolidated resultsDimensional reportingPayables and receivables

Sage Intacct ↔ CRM

SalesforceHubSpot

  • Invoiced customers the CRM doesn’t hold
  • Won deals with no matching invoice

Sage Intacct ↔ Payroll and HR

ADPWorkdayBambooHR

  • Payroll cost by department that disagrees
  • Departments in HR missing from Intacct

Sage Intacct ↔ Projects and time

PSA or time tracking tool

  • Billable hours with no matching invoice
  • Invoices against closed projects

Sage Intacct ↔ Spreadsheets

Board packallocations

  • Board numbers the ledger can’t reproduce
Illustrative: gaps typical between these systems, not client findings. Which ones exist in your data is what a Truth Score Read finds out.

What to export

What we need from you, in plain English.

Standard reports and list views export to CSV or Excel, run by your finance team from our field list. We do not need a Web Services user or a seat in your company.

What to exportFor whoever runs it
GL detail for the period in scope, with every dimensionGeneral ledger detail report: entity, account, date, amount, DEPARTMENTID, LOCATIONID, PROJECTID, CLASSID
Customers and vendors across all entitiesCustomer and Vendor lists: ID, name, entity, status, email
AR invoices and payments, if cash is in scopeAR invoice and payment reports: record number, customer, date, due date, amount, state
Dimension value lists per entityDepartment, Location, Project and Class lists with entity restrictions
  • Exports only, no access to your systems
  • NDA and data processing agreement first
  • The full list of sub-processors comes with our data processing agreement, before any data moves
  • Raw exports deleted after the engagement; we keep quality metrics, not your records

AI that depends on it

AI use cases that lean on Sage Intacct.

If one of these is in a proposal or your plan, Sage Intacct is one of the systems we compare. Each page sets out what the use case needs and where it breaks.

How a Truth Score Read tests Sage Intacct

From your exports to a signed verdict.

Within 5 working days of complete exports. A fixed fee, agreed before we start, that never moves with the verdict.

  1. 01

    Export Sage Intacct, and one system beside it

    The Sage Intacct exports listed above, plus at least one neighbour, such as CRM. Under NDA, using the field list we send.

  2. 02

    Match across systems

    Our engine matches each AR invoice and its related records across the exports by ID, name, email and domain, including the ones spelt differently in each system.

  3. 03

    Check by hand, then score

    Every red flag is checked by hand against sample records before sign-off. Then the Truth Score: each pillar shows what was measured and what wasn’t, and pillars with too little signal are marked, never guessed.

  4. 04

    Get the verdict in writing

    A signed memo: the red flags between Sage Intacct and the other systems, a Leverability read on the AI you’re considering (which AI ideas you can use now, after fixes, or not at all), a verdict (sign, fix first or walk away) and a short fix list your own team can work from.

How the Truth Score Read works

What you get back

See what comes back: a real sample memo.

One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.

Real engine output on generated test data, not a client.

Questions

Sage Intacct and AI, answered plainly.

Anything else, ask on the 30-minute call. It commits you to nothing.

Book the call
How do we share Intacct data without giving access?

Your finance team exports standard reports and lists to CSV from our field list, under NDA. No user, no Web Services credentials.

Our dimensions are well designed. What could go wrong?

Design is rarely the issue; application is. We measure how often each dimension is actually tagged, and whether the same value means the same thing in every entity.

Can AI help with our consolidation?

Only once inter-entity relationships and dimensions are consistent. We show which parts of the close are ready to automate and which would multiply errors.

Do you work with our Intacct partner?

We hand the findings and fix list to whoever you choose. We take no fees from partners and never do the work ourselves.

Find out what your Sage Intacct data can carry. Before the AI does.

Your Sage Intacct export and one other system in. The gaps between them in writing, within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.

Get your Truth Score See a real sample memo

Starts with a 30-minute call. Commits you to nothing.

Get your Truth Score