AI Data Preparation
AI Data Preparation for Finance Workflows
For teams that need to turn messy documents and finance data into reliable, AI-ready data products.
- Finance-aware process understanding from accounting and reporting
- AI-ready outputs for RAG, automation and downstream systems
- Traceable, privacy-aware and compliance-minded delivery
Input -> Structure -> Output
Output focus
Clean structures, traceable fields and AI-ready outputs for finance, compliance and document-heavy workflows.
Why AI projects fail on unstructured data
Many AI initiatives start with model questions and underestimate the reality of the source data.
PDFs, scanned documents, ERP exports and inconsistent tables may still be readable for humans, but they are rarely directly usable for AI systems.
What is missing are stable fields, sensible segmentation, trustworthy metadata and a reliable foundation for retrieval, validation and automation.
Typical causes
- clean text
- sensible segmentation
- stable field logic
- traceable metadata
- usable output formats
Typical consequences
- imprecise answers
- unstable RAG setups
- high manual rework
- low trust in the system
Three common use cases
The offer is deliberately narrow: data and document preparation for AI, RAG and automation use cases in finance-adjacent environments.
| Direction | Problem | Process | Output |
|---|---|---|---|
| RAG Corpus Ingestion | PDFs, DOCX, policies, manuals, OCR-heavy documents | Text extraction, cleanup, segmentation, metadata | JSONL, chunk sets, retrieval-ready corpus |
| ERP & Accounting Cleanup | ERP exports, accounting data, open-item lists, reporting files | Normalization, mapping, deduplication, field checks | CSV, Parquet, validated analysis set |
| Compliance Transformation | XRechnung, XML, structured business documents | Field mapping, validation, format checks, transformation logic | XML, validation files, structured downstream processing |
RAG Corpus Ingestion
For internal knowledge bases, guidelines, process documentation and mixed document inventories.
ERP & Accounting Cleanup
For finance exports that must be standardized and checked before analytics, forecasting or AI usage.
Compliance Transformation
For structured business documents where field logic, validation and standard conformance matter.
What you actually receive
Not abstract AI consulting, but concrete and operationally usable deliverables.
Deliverables in focus
- Cleaned raw data or document content
- Structured datasets in JSONL, CSV or Parquet
- Optional validated XML outputs in compliance contexts
- Chunking structures for RAG or search implementations
- Field definitions and mapping logic
- Metadata concepts for documents and datasets
- Validation rules and quality checks
- Handover documentation for internal teams or implementation partners
Suitable for
How a project works
Starting small is explicitly possible. Many engagements begin with a limited sample dataset or a tightly scoped pilot.
Step 1
Intake & target picture
Understand the data landscape, source systems and targets, and identify risks and exclusions.
Step 2
Analysis & structure design
Review patterns, inconsistencies and edge cases, then define target structure, fields and validation logic.
Step 3
Preparation & validation
Clean, map, deduplicate and segment the data, enrich metadata and apply quality checks.
Step 4
Handover & next steps
Deliver the final output package, documentation and recommendations, with optional support for the next implementation step.
A narrow pilot is often the fastest way to de-risk a later AI implementation.
Why this work fits my profile
- Finance-adjacent background with a focus on accounting and process quality
- Hands-on understanding of structured and unstructured business documents
- Traceability over black-box promises
- Strong fit for finance, compliance and document-heavy environments
- A practical bridge between business precision and technical implementation
Typical starting points
Frequently asked questions
Do you work with sensitive finance data?
Yes. For pilot phases I prefer anonymized or reduced samples and a clearly defined secure exchange only after scope alignment.
Is this only relevant for large AI projects?
No. Smaller pilots often benefit the most from proper structure before larger investments are made.
What formats can be processed?
Typical inputs include PDF, DOCX, spreadsheet exports, CSV, ERP lists and structured formats such as XML.
Do you replace a full data engineering team?
No. The service is intentionally focused on data and document preparation for AI, RAG and automation use cases.
Pricing & entry points
Clear pilots instead of vague AI promises. Most work starts with a well-bounded scope.
| Service | Entry point | Suitable for | Scope / outcome |
|---|---|---|---|
| Mini Pilot / Sample Review 0.5 to 2 work days | from β¬350 | For teams that want to evaluate whether their documents or datasets are suitable for AI, RAG or automation before committing to a larger scope. |
Fully credited if a follow-up project starts. |
| RAG Corpus Ingestion 4 to 8 work days | from β¬1,800 | For document inventories that need to be prepared for RAG, internal knowledge bases or AI-powered search. |
|
| ERP & Accounting Cleanup 5 to 10 work days | from β¬2,500 | For ERP exports, accounting datasets and reporting files that must be standardized before analytics or AI usage. |
|
| Compliance Transformation 7 to 15 work days | from β¬3,500 | For structured business documents that require field-level traceability, validation and standards alignment. |
|
Pricing logic
- Exact pricing depends on data quality, format diversity, scale, validation depth and edge cases.
- For clearly defined pilots I prefer fixed pricing.
- For more complex or iterative scopes, delivery can also be effort-based.
- The focus is on clear entry prices and bounded scopes, not open-ended retainers.
Why this pricing range makes sense
- Finance-aware data preparation
- Clean structures instead of ad-hoc scripts
- Traceability instead of black-box shortcuts
- Less rework and fewer downstream errors
The next sensible step
If you already have documents or datasets intended for AI, RAG or automation, the real work usually starts before the model does.
Next step
If needed, start with an anonymized sample or a tightly scoped mini pilot.