Paper & PDF Chaos vs. Digital Precision
For any team that has tackled the challenge of processing paper documents, scanned PDFs, or unstructured content, one thing is clear: this isn’t a problem of intellect or even technology—it’s a problem of scale. Dozens (or sometimes thousands) of formats, lack of standardization, endless annexes and administrative decisions—amounting to hundreds of thousands of documents and millions of pages. At such volumes, even the most well-organized operations teams buckle under the weight of manual work.
That’s where AI steps in—but not the kind that "does everything for you." The real value lies in AI that, when properly steered, can extract relevant data faster, more cost-effectively, and often with fewer errors than humans. This unlocks efficiency, reduces workloads, and accelerates decision-making processes.
In this write-up, I’ll walk you through how we at Alterdata used Generative AI and Google Cloud tools to process tens of thousands of documents and what actionable insights your organization can take from this approach.
Where We Started: Scale and Disarray
Like many organizations, we began with what was essentially a "digital archive"—a repository of over 40,000 documents in various formats (PDFs, JPG scans, TIFF files), with inconsistent naming conventions, varying lengths, and no shared structure.
These documents contained sensitive data, administrative decisions, contract annexes, location approvals, cost invoices, technical reports, acceptance protocols, legal correspondence, and a wide range of other document types.
This blend of formats and content rendered traditional approaches—OCR + regex + manual verification—entirely unscalable within any reasonable time or budget.
Proof of Concept: Can GenAI Handle It?
We started with a two-week proof of concept on a curated set of 500 documents. This wasn’t just a quick demo—it involved intense iteration, prompt engineering, and performance testing against low-quality scans, inconsistent language, and highly irregular structures.
Our goal was to test whether the AI model (in this case, Google Gemini via GCP Functions) could:
- Identify the document type (e.g., location permit vs. annex vs. contract),
- Extract key fields (case numbers, dates, addresses, pages, vendors),
- Process documents of various quality and structure,
- Normalize extracted data into tabular format (for BigQuery).
The outcome? After two weeks, the model achieved over 90% accuracy in classification and about 95% field-level extraction precision. Considering the diversity and complexity of the input—this was more than sufficient to move forward to production.
Architecture: Simplicity That Scales
The final solution was built with scalability, transparency, and robustness in mind. It included:
- Google Cloud Storage for storing document files,
- Cloud Functions to orchestrate pipeline logic and model calls,
- Gemini (via Vertex AI) for classification, extraction, and handling multipage files,
- BigQuery as the central structured data store,
- A custom analytics dashboard and validation interface for reviewing AI outputs.
This architecture was purposefully designed to handle the realities of real-world document workflows: frequent test cycles, evolving data formats, and edge-case anomalies.
- It supported iterative improvements (prompt tuning, exception handling, edge-case logic),
- Every decision was verifiable—AI assisted the team, but didn’t operate autonomously,
- It was built for batch-scale processing without hanging on individual file failures or requiring manual oversight.
Iterations and Lessons: Complexity, Not Magic
The hardest problems weren’t purely technical or operational—they emerged at the intersection of technology and business logic:
- How do you design a document type taxonomy that’s comprehensive enough?
- How do you validate data that appears in different formats (e.g., abbreviated vs. full addresses)?
- What must the model extract every time, and what only when feasible?
We ran hundreds of iterations across prompt variants and control logic before reaching production-ready data quality. We also implemented business validation mechanisms and anomaly-flagging rules (e.g., numerical inconsistencies).ci (np. niezgodności w liczbach).
Outcomes: 30,000 Documents in 2 Weeks, 2,000 Work Hours Reclaimed
In the production phase, we achieved:
- Processing of nearly 30,000 documents in under two weeks,
- 95% extraction accuracy for critical data fields,
- Data ready for integration with ERP systems and BI platforms,
- And roughly 2,000 hours of manual labor saved.
But the real breakthrough was in data interpretation. With well-structured outputs, the client gained visibility into information they previously didn’t even know existed—revealing duplicate obligations, inconsistencies, and unnecessary expenditures.
The result? Concrete business decisions that led to several million PLN in annual savings. This underscores a key insight: the true value of GenAI lies not just in speed, but in surfacing hidden insights that unlock better decisions.
What You Can Do in Your Organization
If you’re facing similar unstructured data challenges:
- Start with an audit: What types of documents do you have? How many? In what formats? What data do you need from them?
- Select key document types and define the critical fields for extraction.
- Run a PoC: Test what works and what doesn’t.
- Iterate: Combine prompt engineering, validation cycles, and feedback loops.
- Scale: Automate, monitor, and refine.
Remember: AI won’t replace humans, but if it can do the job 10x faster and 10x cheaper—and give you access to insights you never had before—then its value goes far beyond time and cost savings. It can drive renegotiations, optimize operations, and deliver measurable business impact.
Want to Talk About How AI Can Help with Your Documents?
Let’s talk.
At Alterdata, we combine data, AI tools, and real business needs—to deliver tangible results.
Book a free consultation and see how we can help.