Intelligent automation for auditing

Artificial intelligence has opened up extraordinary possibilities. But a generative model can produce an answer that is plausible and wrong. In auditing, probable is not enough: the process must be repeatable, explainable and reviewable.

For critical workflows, this Windows application starts with rules, parameters and source data. Artificial intelligence is used where interpretation adds value and never replaces professional validation.

In one platform, the team normalises bank statements, trial balances and payroll data; reconciles statements with trial balances; analyses SAF-T, journal entries and suppliers; selects document samples; interprets minutes; automates Tax Authority extractions for companies and municipalities; and creates, updates or maps leads.

Twelve tools share the same principle: automate the work without turning the analysis into a black box.

In this SAF-T example, the file is validated, the rules are executed and the exceptions are prioritised. In deterministic engines, the same file, parameters, random seed and version produce the same result. Every alert keeps the rule, reason, score and link to the source, allowing independent review.

In AI-assisted modules, such as minutes analysis, the output is identified, structured and reviewed by a person. Robustness is not a promise of infallibility: it means making uncertainty visible, not inventing information when data is missing and leaving evidence.

The platform can also evolve with its users. A recurring problem is qualified, turned into a prototype, validated with real teams and, when appropriate, converted into a new module without disrupting the remaining workflows.

The next step is a thirty-day pilot: two workflows, measured repeatability and a product-evolution session. Measure time, validate the evidence and help define the next module.