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Summary – Scattered documents—paper, scans, and notes—create information silos, data-entry errors, hidden costs, and hinder IT automation, impacting margins and team morale.
Multimodal LLMs outperform traditional OCR by structuring forms, handwritten text, and technical diagrams with under 3% error rates, delivering contextual extraction, normalization, and natural-language search for dashboards and workflows.
Solution: deploy an open-source AI pipeline for capture, extraction, and API integration to feed reliable data into your ERP/CRM, free up processing time, and transform your archives into an agile management lever.
In many Swiss organizations, documentation remains an untapped treasure, scattered across paper forms, scanned PDFs, handwritten notes and photos. This heterogeneity creates information silos, inflates administrative costs, and significantly slows processes – from quote generation to archiving intervention reports. In contrast, AI-driven digitization turns these “raw” documents into structured, ready-to-use data.
At the heart of information system (IS) modernization, this step becomes the starting point for agile management, improved data quality and enhanced productivity. Shedding light on this hidden reserve becomes a strategic lever for any company seeking to go beyond simple time savings and aim for operational excellence.
Documentation: an Overlooked Productivity Lever
Documentation is the last major productivity frontier. Heterogeneous formats generate errors, costs and IS bottlenecks.
Legacy formats impede agility
Within Swiss SMEs and mid-sized enterprises, processes often still rely on physical forms or order-form scans. Every manual entry carries a risk of error: a miscopied number, an incorrect date or an omitted product line. ERP or CRM systems cannot be fed directly. The result is delayed, manually driven processing, where each department devotes time and resources to validate information before using it.
Workflows grow heavier and digitalization initiatives struggle to overcome their main hurdle: turning documents into actionable data.
One Swiss industrial player demonstrated that integrating fifty paper-based workflows into its ERP cut internal approval times by 70 %. This case shows that by prioritizing heterogeneous formats first, you free up processing capacity that can be immediately reallocated to higher-value tasks.
Cost and errors of manual handling
Manual data entry not only produces errors, but also incurs hidden costs: hiring additional staff, overtime, internal support calls and increased quality audits. These expenses accumulate in the budget and erode operating margins.
Beyond the numbers, the human impact is significant: employees complain of low-value repetitive tasks and see their motivation decline. Turnover can rise, leading to knowledge loss and disruptions in business continuity.
The same Swiss company estimated that 30 % of its administrative budget was spent correcting entry errors. By automating data extraction, it was able to reassign those resources to strategic market analysis and product innovation.
Data as fuel: unlocking insight from docs
The information housed in documents is an untapped knowledge source: project histories, customer feedback, technical specifications, quality reports… All these elements hold continuous-improvement levers once they’re structured and analyzable.
By converting these documents into data, you can identify trends, anticipate bottlenecks or even automate dashboard generation. Data quality improves, and strategic decisions rest on up-to-date, reliable information.
A logistics service provider recently digitized all its intervention reports, turning them into operational performance indicators. Data analysis reduced fleet downtime by 15 %, demonstrating the strategic value of archives that had been lying dormant.
Multimodal LLMs vs. Traditional OCR
Multimodal large language models (LLMs) surpass traditional OCR’s limits. They understand document structure and context.
Limitations of traditional OCR
Classic OCR extracts text but is blind to meaning: it can’t distinguish a “date” field from free-form remarks, nor a specification table from a paragraph. The output is often raw and requires time-consuming cleaning to ensure data accuracy. Traditional OCR error rates can reach 20 %, depending on document type.
Contextual understanding of multimodal LLMs
Multimodal LLMs combine vision with natural language processing: they automatically identify key fields (names, quantities, dates), tables and free-text areas, and grasp business intents. The result is a logically structured output ready for use.
This contextual understanding lets you distinguish a quote from an invoice, identify assembly instructions in a technical diagram or capture a handwritten note from a maintenance visit. Automation thus becomes more precise and robust.
The same public institution implemented an open-source multimodal LLM to analyze its forms: manual correction rates fell below 3 %, and daily volume doubled, proving the superiority of context over mere character recognition.
Handwriting and complex content extraction
Handwritten text, often problematic for OCR, becomes readable thanks to models pre-trained on millions of samples. Annotations on site photos or quality-report comments are thus converted into exploitable data.
Multimodal LLMs also extract relationships between elements: a quantity linked to a part name, a due date tied to an order or an instruction associated with a signature. These interconnections are preserved in the output structure, simplifying IS integration.
A construction firm used this technology to automate the reading of handwritten quality-control reports. The model recognized 95 % of annotations and placed each piece of information into a structured format ready for statistical analysis.
Edana: strategic digital partner in Switzerland
We support companies and organizations in their digital transformation
AI Pipeline for Document Extraction
Extraction, structuring, integration: a transparent pipeline for leaders. Value is created by seamlessly feeding data into the IS.
Capture and extraction
The first step is to photograph or scan a document via a native mobile app or a desktop scanner. Images are then sent in real time to a hosted AI service, which detects text zones, tables and diagrams.
The multimodal LLM processes each page, automatically pinpoints critical fields (customer code, amount, etc.) and produces a structured intermediate format. Users receive an almost instantaneous preview and can validate or correct the detected data.
A Swiss financial services firm deployed this mobile capture for its field teams: reimbursement requests now process in minutes instead of days.
Structuring and normalization
Extracted data is converted into a standardized JSON data pipeline or fed directly into an existing business model. Each field is typed (text, number, date), validated against business rules and mapped to the internal reference system.
This normalization ensures data consistency within the ERP or CRM, avoids duplicates and maintains a clear history for each entity. Automated workflows can then trigger actions without human intervention.
In a large Swiss industrial group, migrating delivery notes through this pipeline improved inventory accuracy and cut stock-discrepancy disputes by 40 %.
Integration and intelligent archiving
Once structured, data is injected via APIs into target systems – ERP, CRM or specialized business solutions. Original documents, enriched with extracted metadata, are archived in an intelligent repository.
An internal AI search engine then lets you query the entire archive in natural language: “Documents mentioning on-site interventions at location X in June 2024.” Results are instantaneous and relevant.
A Swiss logistics provider found that archive retrieval, once taking minutes per query, now takes seconds—boosting after-sales responsiveness and customer satisfaction.
Use Cases for AI Document Digitization
A variety of use cases demonstrate the universality of AI document digitization. Every function—from finance to engineering—benefits.
Invoicing and procurement
Automated processing of supplier invoices shortens validation and account-reconciliation times: extracting amounts, identifying accounting codes and matching purchase orders. Payment workflows become smoother and less prone to delays.
In the service sector, an accounting firm implemented this process: month-end close time dropped from 10 to 4 days, freeing up time for financial analysis and strategic advisory.
This case shows how finance can gain agility and reliability without changing its ERP—simply by connecting the extraction engine to the existing procurement module.
HR and compliance
Paper HR forms (contracts, pay slips, certificates) are extracted and indexed, ensuring compliance with data protection laws and GDPR. Recruitment and onboarding workflows accelerate because every document is accessible and verifiable automatically.
An IT services company automated the collection of training certificates and policy acknowledgments. Compliance checks, once tedious, are now instantaneous.
This example highlights the impact on regulatory compliance and internal transparency—a key concern for executive and HR teams.
Technical drawings and quality checklists
Technical diagrams or hand-drawn sketches are analyzed by AI vision to extract annotations, dimensions and symbols. Quality checklists are converted into structured data and integrated into the production management system.
A mechanical engineering company digitized its inspection reports, enabling real-time monitoring of non-conformities and automatic triggering of maintenance or adjustment workflows.
This feedback shows that even highly specialized visual content can be processed reliably, supporting traceability and continuous improvement.
AI Digitization: A Rapid Return on Investment
Document modernization through AI delivers one of the most tangible ROIs in digital transformation: reduced administrative costs, improved data quality and accelerated key processes. It also lays the foundation for any IS modernization—whether for BI, business workflows or migration to cloud solutions.
All companies have an untapped resource in their paper and digital archives. Unlocking these data opens the door to more informed, agile and secure management, while preserving your technological independence with modular, open-source solutions.
Our experts are ready to analyze your document chain, define the pipeline best suited to your context and guide you toward operational excellence. Together, let’s turn your silent archives into living, structured data that drive your growth.
