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Monitor your financials, continuously
Audit & Controlling Agents

AI agents that find the anomaly
before your auditor does.

Duplicate postings and missed eliminations surface at month-end — after the entries are posted. Keboola’s Audit & Controlling Agents replace those periodic checks with continuous control: always-on agents monitor your financial data, deliver exceptions into one Signals stream, and trace every alert to the source journal entries. Your data doesn’t need to be perfect before you start.

Trusted by 1,000+ companies

Apify
Carvago
Česká spořitelna
CreditInfo
DXC
EmbedIT
Firehouse Subs
Groupon
GymBeam
Heureka
HomeCredit
Innogy
ITIS HOLDING
Kofola
P3
Productboard
Rohlík
seznam.cz
ShipMonk
Shoptet
The Evans Network
Apify
Carvago
Česká spořitelna
CreditInfo
DXC
EmbedIT
Firehouse Subs
Groupon
GymBeam
Heureka
HomeCredit
Innogy
ITIS HOLDING
Kofola
P3
Productboard
Rohlík
seznam.cz
ShipMonk
Shoptet
The Evans Network
Apify
Carvago
Česká spořitelna
CreditInfo
DXC
EmbedIT
Firehouse Subs
Groupon
GymBeam
Heureka
HomeCredit
Innogy
ITIS HOLDING
Kofola
P3
Productboard
Rohlík
seznam.cz
ShipMonk
Shoptet
The Evans Network

The Problem

Periodic controls find the real problem too late.

Controls that run once a month, on stale data

Month-end controls check a static export. By then the entries are posted, the close is running, and an error found late becomes a correcting entry — or a restatement.

Intercompany mismatches nobody catches until audit

One entity books the receivable; the counterparty never posts the payable. The consolidated P&L is wrong and nobody knows why. Manual reconciliation catches some. The auditor catches the rest — in writing.

Controlling teams buried in exception reports

The ERP generates hundreds of exceptions with no ranking and no materiality cut-off. Someone reviews them line by line — so real anomalies get the same attention as noise.

AI on top of bad data gives confident wrong answers

Point an LLM at unreconciled ledgers and it answers confidently anyway. Without a governed data foundation, the model is only as reliable as the data it reasons over.

Audit & Controlling Agents

From month-end checks to continuous controls monitoring.

Watch three configurable agents at work — the Budget Drift Agent, Auto Reconciliation Agent, and Cash Flow Sentinel — delivering exceptions into one Signals stream, every one traceable to its source journal entries.

0-0%

Reconciliations automated — exceptions only reach the controller

0-0days

Faster close by design — reconciliation runs before the close, not during it

Zero

Audit surprises — issues caught in the pipeline, before the audit

How it works

ThegoverneddatafoundationandtheAgentOperatingLayerononeplatformsoeveryagentmonitorsdataitcanactuallytrust.

Monitor — rule-based agents, always on

Monitoring Agents apply rule-based checks continuously — cash runway risk, burn-rate spikes, gross margin compression, OpEx drift — plus rules you define, like duplicate postings or amount thresholds. The configurable Budget Drift Agent tracks actuals against budget daily, so drift is detected within hours.

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02

Discover — anomalies no rule could define

The Agent Operating Layer pairs the always-on monitoring rules with LLM-powered Discovery Agents — a layer built to surface what fixed rules can’t describe, from expense outliers to postings that don’t fit an entity’s pattern. Today, Kai analyzes every signal for root causes and suggested next steps, and every finding carries a confidence score that is always explainable.

Review — exceptions only, in one Signals stream

Every agent delivers into one Signals stream with one lifecycle — no per-agent dashboards to patrol. The configurable Auto Reconciliation Agent is built to clear 80–90% of standard reconciliations automatically, so controllers review only what’s left — designed to recover 2–4 days of close time.

03
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Prove — every alert traces to journal entries

Drill any signal to the source journal entries behind it. Every metric definition and chart of accounts change lands in a live Audit & Governance feed — your auditor can reconstruct any number, without asking your team.

Why Keboola

The data foundation and the agents — one platform, no black box.

Most AI audit tools assume clean, structured data before they work. Keboola builds the governed foundation andthe agents on the same platform — and keeps the logic in the open. An auditor can read the detection rules. Every signal carries an explainable confidence score. When the Budget Drift Agent says Germany is tracking 12% over payroll budget, you drill to the exact journal entries that drove it. That’s verifiable AI — not a confident guess.

Keboola
Monitor
Reconcile
Prioritize
Cash flow

Questions & answers

Frequently Asked Questions

Everything finance, IT, and procurement will want to know — up front.

Continuous controls monitoring means checks run against live financial data throughout the month, rather than once against a static month-end export. Keboola runs rule-based Monitoring Agents continuously — cash runway risk, burn-rate spikes, gross margin compression, OpEx drift — plus rules you define, such as duplicate postings or amount thresholds. An error becomes an alert within hours, while it can still be fixed with a simple correcting entry.
The Agent Operating Layer combines two layers: rule-based Monitoring Agents that apply rules an auditor can read, and LLM-powered Discovery Agents built to find anomalies no fixed rule could define — postings that don't fit an entity's pattern, expense outliers. Today, when a signal fires, Kai analyzes it for root causes and suggested next steps, and every finding carries an explainable confidence score — so a controller sees why something was flagged before spending a minute on it.
The Auto Reconciliation Agent — one of the configurable agents in the video above — is built to handle 80–90% of standard reconciliations: intercompany balances, bank transactions, account-level discrepancies. Finance reviews exceptions only; that's the mechanism behind the 2–4 days of close time it's designed to recover. The remaining 10–20% are genuine judgment calls, and they arrive ranked in one Signals stream.
Yes — every signal drills to the source journal entries behind it, and every metric definition and chart of accounts change is logged in a live Audit & Governance feed, so an auditor can reconstruct any number without asking your team. You can also ask Kai, the built-in assistant, why a number moved and get root causes with suggested next steps.
No — that requirement is what stalls most AI audit tools. Keboola builds the governed data foundation and runs the agents on the same platform, so cleansing, chart of accounts mapping, and monitoring start together. Creditinfo implemented in 2 months and cut month-end close time by 70%.
Neither. It changes what reaches them: instead of sampling a static export, your auditor gets transaction-level lineage and a complete change log, and your controllers get exceptions instead of hundreds of unranked ERP reports. Creditinfo, for example, cut close time 70% with a 2-month implementation — controlling moved from reactive to continuous.
Reconciliation automation is designed to recover 2–4 days, because the Auto Reconciliation Agent is built to clear 80–90% of standard reconciliations before the close starts. Multi-entity closes typically run 10–25 days; best-in-class is 3–5. Creditinfo reduced close time 70%. The mechanism is exceptions-only review — controllers stop re-checking everything that was already right.
Keboola connects every ERP, every spreadsheet, and every operational signal — without ripping out what you have. The platform builds on your existing warehouse — Snowflake, BigQuery, Databricks, or DuckDB — with no parallel platform and no data migration. The agents then monitor the governed data layer built from those sources, so every alert traces back to the original postings.
Three concrete differences. Data foundation: traditional CCM tools assume clean, structured data already exists; Keboola builds the governed foundation and runs the agents on the same platform. Transparency: the detection rules are readable — an auditor can open them — where most tools score in a black box. Output: agents deliver ranked exceptions into one Signals stream with an explainable confidence score, rather than dumping full exception reports for manual review.
Balu Gopakumar|Account Executive
Balu Gopakumar
Martin Lepka|CMO Keboola
Martin Lepka
Giorgio Pontillo|CRO
Giorgio Pontillo

Not sure if your data is ready for AI controls?

We'll tell you what needs to be true first — and how long it takes. The 30 minutes covers your current control environment and shows a realistic path to continuous controls monitoring.