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    AI Data Audit

    A data audit carried out with AI.A fast basis for an operational decision.

    We take an agreed sample of data, documents and exports. We run it through automated checks, matching and agent analysis. The output is a list of concrete findings, their operational impact and the recommended next step.

    Book an AI Data AuditSources
    Input
    Real data
    Exports, documents, sheets, email.
    Method
    AI checks
    Matching, cleaning, conflict detection.
    Output
    Decision
    What to fix, wire in or leave alone.
    § 01Sources

    We audit the data that creates work today.

    We do not start with a presentation. We start with data. We choose the sources that shape the operation and check what does not line up.

    SOURCESOUTPUTERPorders, items, statesCRMcompanies, contacts, salesWAREHOUSEstock, movement, availabilityINVOICESamounts, due dates, linksDOCUMENTSPDF, Excel, attachmentsEMAILbriefs, changes, exceptionsAI DATA AUDITCLEANINGMATCHINGCONFLICTSEXCEPTIONSFINDINGSIMPACTPRIORITYNEXT STEP
    The scope is agreed upfront. The audit does not take the whole company at once.
    § 02Process

    One pass, six fixed steps.

    We run the audit as production of an output. Each step either creates a finding or confirms that this part does not hold.

    01SelectSources and sample.02LoadExports, documents,structure.03CleanFormat, duplicates,blanks.04MatchLinks between systems.05CheckConflicts andexceptions.06OutputFindings andrecommendations.
    This is an audit process, not a data migration.
    § 03Findings

    A finding must be concrete.

    A general statement does not help the company. Every finding is tied to a place in the data, an operational impact and a next step.

    FindingsIllustration
    FindingDuplicate customer
    WhereCRM and billing
    ImpactHistory splits
    Next stepMerge records
    FindingMissing field
    WhereOrders
    ImpactManual lookup
    Next stepChange input form
    FindingDifferent price
    WhereERP and price list
    ImpactExtra approval
    Next stepSet source of truth
    FindingData outside system
    WhereEmail and Excel
    ImpactRetyping
    Next stepWire in intake
    FindingUnlinked document
    WhereInvoices
    ImpactWeak control
    Next stepAdd relation
    Examples of finding types. The real audit is built on your data.
    § 04Output

    You get a basis for a decision.

    The output is not a long presentation. It is a working map of where data slows the operation down and what is worth addressing first.

    AI Data AuditIllustration
    01 Sourceswhat we reviewed
    02 Data statewhere gaps are
    03 Conflictswhat disagrees
    04 Manual workwhere retyping appears
    05 Riskswhat can hurt
    06 Planwhat to do next
    The audit should end in a decision, not another round of general debate.
    § 05Scope

    A small scope is an advantage.

    The audit is meant to show what the data actually says. That is why we take a selected part of the operation, not an endless company program.

    THE WHOLE OPERATIONAUDIT SCOPESOURCESPERIODPROCESS
    Implementation can follow the audit. It does not have to.
    01
    Agreed sample
    Sources, period and process are clear upfront.
    02
    Limited access
    We take only the data needed for the audit.
    03
    Hand-off output
    Findings can stand alone or start an implementation.
    § 06Next step

    Send a data sample. We show what is inside.

    Describe the process where manual work appears today and prepare the exports or documents attached to it. We will say whether the audit makes sense and suggest the exact scope.

    Book an AI Data AuditAll products

    We reply within two working days.

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