Data cleaning is the task that nobody loves – but everyone needs. Scattered Excel spreadsheets, duplicate entries, inconsistent formats: This is where it is decided whether to wear or tilt analyses. At ALGEBRA we develop AI agentsTreat the data like an experienced analyst – only faster, reproducible and with clean protocol.
More than automation: real Agentic AI
Classical scripts follow rigid rules. Our agents work agentic: They plan in several stages, select suitable tools themselves, check their results and correct themselves in short loops. About the Model Context Protocol (MCP) they access your systems securely and standardized – databases, files, APIs – without data leaving the house uncontrollably. More about this in our contribution Model Context Protocol (MCP).
Step 1: Aim before funds
First, let's clarify Why. The agent creates a profiling of the raw data (customer data, phone numbers, timestamps): detects columns, data types, gaps, patterns and inconsistencies. Result: a clear overview as a basis for every decision – for example, standardizing phone numbers or preparing an activity analysis.
Step 2: Cleaning up with a plan – not by chance
Instead of “filter hopping”, the agent plans specifically: remove duplicates, standardize formats, meaningfully supplement false values, correct inconsistencies. The sequence is aligned with the target, the execution is done via generated code or precise instructions – not a black box process, but transparently documented.
Step 3: Check → Improve → Complete
After the first cleanup, the agent checks the result. If necessary, follow a short loop (2–3 iterations): detect errors, adjust operation, recheck. Precise instead of endless.
Mini-example: standardizing phone numbers
Target format +49XXXXXXXXX, otherwise empty:
| Gross value | → | Adjusted |
|---|---|---|
| 1234567 | → | +49 1234567 |
| +49 123 4567 | → | +49 1234567 |
| 0049-123-4567 | → | +49 1234567 |
| NA | → | (empty) |
Bonus: Understanding versions of documents
Our agents compare not only data, but also documents and versions – such as tenders or contracts: They mark changed passages, show added values and evaluate which version is more current, complete and consistent. This creates intelligent version management that relieves people instead of replacing them.
Why this counts for companies
- Getting started faster: Data quality in hours instead of weeks
- Audit-proof: every step logged traceably
- Scalable: Same quality for growing data volumes and teams
- Directly usable: results flow into reporting, analytics or operational systems
Conclusion
Data cleaning does not have to be a mandatory program. With the AI agents from ALGEBRA, it becomes a transparent, controllable process – from analysis to correction to version verification. People remain at the center: in evaluation, interpretation and use. The rest is done by AI – calm, reliable, traceable.
Find out more: How we implement AI agents and RAG systems, read on our page AI Consulting.
Would you like to implement this in your company? We support you pragmatically – from the idea to the operation.