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.
- Expert-led preparation informed by accounting, reporting and process logic
- Reliable outputs for RAG, analytics, Document AI and downstream implementation
- Traceable, privacy-aware and compliance-minded delivery
Low-risk validation scope
Anonymized samples or in-environment collaboration
Traceable, finance-aware delivery
Input -> Structure -> Output
Output focus
Clean structures, traceable fields and AI-ready outputs for finance, compliance and document-heavy workflows.
How collaboration can start
A sensible first step does not always require immediate access to raw sensitive finance data. The initial collaboration mode can be chosen to fit the environment.
Anonymized sample
For many first assessments, an anonymized sample or reduced document excerpt is enough to evaluate structure, field logic and likely risks.
Representative or synthetic structure
If real data cannot be shared yet, a representative structure or synthetic example can still be enough to align on target format, mapping and validation logic.
In-environment collaboration
Where governance or confidentiality requires it, the work can start within your environment or a tightly controlled secure setup.
Why AI projects fail on unstructured data
Many AI initiatives start with model questions and underestimate the reality of the source data.
This is often not only a model problem and not only an engineering problem, but a preparation and validation problem between business logic and technical implementation.
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: expert-led preparation and validation for finance-adjacent, document-heavy workflows so downstream AI and implementation work becomes more reliable.
| 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 this is β and what it is not
The positioning is intentionally precise: not a generic outsourcing service, but expert-led preparation and validation for sensitive document and data contexts.
What this is
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Expert-led preparation and validation
The work is guided by structure quality, field consistency and downstream usability, not by volume alone.
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Finance-aware document and data structuring
Suitable for accounting-adjacent, compliance-heavy and document-heavy business environments.
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Traceable outputs for downstream use
Internal teams or implementation partners receive structured outputs, mapping clarity and documented validation context.
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A low-risk way to start
Collaboration can begin with anonymized samples, representative structures or tightly controlled in-environment work.
What this is not
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Not generic bulk labeling
The service is not designed as volume-only tagging without business-aware structure review.
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Not a black-box AI service
You do not receive opaque outputs without field logic, validation context or reviewability.
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Not a blind automation promise
The work does not pretend that poor structure can be skipped over with tooling alone.
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Not a shortcut around review
In finance and compliance contexts, validation and internal review remain part of the serious path forward.
What you actually receive
Not abstract AI consulting and not a black-box service, but concrete deliverables that internal teams or implementation partners can actually use.
Deliverables in focus
- Cleaned raw data or document content with a clear target structure
- Structured datasets in JSONL, CSV or Parquet
- Optional validated XML outputs in compliance contexts
- Documented field decisions and mapping clarifications where relevant
- Traceable, well-documented preparation where sensitive or regulated workflows require that extra discipline
- Validation notes, quality checks and important caveats
- Chunking structures for RAG or search implementations
- Handover-ready results for internal teams or implementation partners
- Concrete deliverables instead of generic automation promises
Suitable for
How a project works
Starting small is explicitly possible. Many engagements begin as a limited-scope validation engagement to test structure quality, field consistency and downstream usability before more is built.
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 scoped sample review is often the fastest way to de-risk later AI or implementation work without inflating the scope too early.
Why this work fits my profile
I work in the layer where business logic, document reality and technical implementation need to align. That bridge is where this kind of work becomes useful.
- 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
- A practical bridge between business precision and technical implementation
Typical starting points and project reality
The value of this work sits at the point where business precision and technical implementation need to meet. That is exactly where many finance-heavy AI projects become fragile.
- 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 need access to real finance data immediately?
Not necessarily. Many first assessments can begin with anonymized samples, representative structures or work performed inside your environment.
Is this a generic labeling or automation service?
No. The service is intentionally expert-led, finance-aware and focused on preparation, validation and downstream reliability.
What happens after the pilot or sample review?
You receive a clear assessment of structure quality, risks, downstream usability and the most sensible next step for internal teams or implementation partners.
How can collaboration start in sensitive environments?
Depending on the setup, with anonymized excerpts, synthetic structures or tightly controlled in-environment collaboration.
Does this help with EU AI Act readiness?
Where EU AI Act obligations are relevant, especially in higher-risk AI contexts, this work supports the kind of data governance, traceability and documentation discipline expected around data preparation and validation. It does not replace legal or compliance advice and does not by itself certify organisational compliance.
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 preparation, validation and reliable handover between business precision and technical implementation.
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 |
|---|---|---|---|
| Scoped Sample Review 0.5 to 2 work days | from β¬350 | For teams that want a low-risk first step to validate whether their documents or datasets can support AI, RAG or downstream implementation work. |
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. |
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| 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. |
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| Compliance Transformation 7 to 15 work days | from β¬3,500 | For structured business documents that require field-level traceability, validation and standards alignment. |
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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 document-heavy or finance-adjacent data that should become usable for AI, RAG or analytics, the reliable work usually starts before the model does.
Next step
If needed, start with an anonymized sample, representative structure or a tightly scoped validation engagement.