With the rapid rise of AI, one thing is clear: reliable document data has become the essential raw material for training models and automating business processes.
In this context, Intelligent Document Processing (IDP) platforms play a strategic role. Their function is no longer limited to extracting information, but extends to ensuring the quality, security, and compliance of document data, thereby providing reliable input for AI and business systems.
One key question remains for CTOs: how to choose an IDP platform that truly meets today’s requirements—data quality, data sovereignty, automation performance, and cost-effectiveness?
This article sheds light on the new criteria to consider before making any decision.
The “All-LLM” Myth in Automated Document Processing: Innovation or Disguised Dependency?
Many IDP platforms are now adopting a “full LLM” approach, which is appealing because of its promise: a single model capable of understanding and extracting everything. The reality is far more complex.
Yes, LLMs offer remarkable adaptability, particularly when it comes to processing unfamiliar documents, analyzing images, or summarizing highly diverse content. They open up new possibilities for use. However, relying solely on LLMs is far from sufficient—and can even pose significant risks.
The limitations of LLMs when it comes to document processing are well known:
- Inability to pinpoint the exact location of extracted data within a document—a critical issue for compliance and back-office teams
- Possible hallucinations, producing data that is false but appears consistent
- Costs that are difficult to predict due to token-based billing and are subject to market volatility
- Very high energy consumption, incompatible with strengthened CSR policies
- Technological dependency (lock-in) on a single, potentially non-European supplier
- Lack of transparency in processing and limited explainability, making it difficult to audit within regulatory frameworks (GDPR, AI Act, etc.)
However, the processing of business and strategic data cannot depend on an opaque model that the organization does not control. Document processing requires precision, explainability, and a guarantee of compliance—three areas where LLMs alone are not sufficient.
⚠️ KEY INSIGHT
Today, CTOs must focus not on a single technology, but on the effective coordination of multiple complementary AI systems.
Betting everything on LLM is a risky move. An open, orchestrated platform is the only guarantee of sustainable performance.
Multi-AI orchestration for consistently optimal performance
In practice, no single AI technology can handle every possible use case involving documents. Whether they are paper documents or digital files, structured or unstructured, with or without photos, containing handwritten or typed data—or even both…
The performance stems from the intelligent combination of several AI families for data extraction and verification. For example:
- Symbolic AI for structured documents and business rules
- Multimodal models for analyzing images and text
- Handwriting OCR for extracting handwritten data
- Table extract for interpreting tables
- NLP for semantic analysis and data extraction from free-form text
- LLM for contextualizing, summarizing, or processing unknown documents
- Face Match for facial comparison
- Specialized engines for detecting anomalies and fraud…
⚠️ KEY INSIGHT
The ability of an IDP platform to continuously integrate the latest state-of-the-art technologies, and to combine and apply them appropriately depending on the document context, are factors that deserve careful consideration.
Data sovereignty and security: an issue that has become non-negotiable
Organizations that handle sensitive documents must ensure that data never leaves their sphere of responsibility.
However, many IDP tools rely on:
- AI APIs hosted in the United States
- Third-party models
- Partially outsourced infrastructure
- Dependencies on non-European providers
In regulated sectors, this situation is incompatible with the GDPR and the AI Act, the protection of personal information and sensitive data, regulatory requirements, and internal cybersecurity policies.
To secure data, CTOs must require a platform from a vendor that:
- IIsolates AI execution
- Ensures no calls to external services
- Provides full auditability
- Controls workflows, models, and infrastructure
Without this, no trust in the documentation is possible.
A modern IDP must ensure document integrity, not just data extraction
Data has become a strategic asset. In fact, simply extracting data is no longer enough; it is essential to combine it with trust.
A modern IDP platform must include:
- Business compliance and consistency checks
- Detection of image alterations or manipulations
- Detection of AI-generated fake documents
- Metadata analysis
- Facial recognition (KYC use case)
- Verifications against external databases (PPE, FINESS, SIRENE, RPPS, etc.)
- Full and legally enforceable traceability
The goal is not merely to extract the data, but to verify the document’s authenticity and the reliability of its data before feeding it into the information system.
⚠️ KEYINSIGHT
Unverified data = biased AI, risky decisions.
Data reliability is more than ever the key to operational performance.
Data has become a strategic asset: without strict control over its quality and location, AI becomes a risk rather than a driver of growth.
Frugal AI: Balancing Innovation, Performance, and Efficiency
AI technologies must now meet an additional requirement: maximizing performance while minimizing their energy footprint.
How can an IDP platform be more frugal? Here’s one way to achieve that:
- Prioritize lighter, use-specific models (CPU)
- Enable LLMs and GPUs only when relevant (e.g., summarization, unknown documents…)
Frugal AI involves deploying the right technology at the right time—and only when it delivers measurable value. Once again, this calls for a coordinated approach, rather than a one-size-fits-all model that is applied systematically regardless of the type of document.
⚠️ KEY INSIGHT
An academic study from 2025¹ showed that CPUs offer cost savings of 35% to 70% compared to GPUs, with energy consumption reduced by 50% to 75%, while sometimes achieving up to 10 times the performance.
1 : Sun, R., Wang, V., Zhang, J. (2025). A Graph Analytics Supercharge Case Study of GPU Versus CPU on Performance, Greenness, and Cost. In: In, C.S., Londhe, N.S., Bhatt, N., Kitsing, M. (eds) Information Systems for Intelligent Systems. ISBM 2024. Smart Innovation, Systems and Technologies, vol 430. Springer, Singapore.
AITECA: the IDP platform designed for discerning CTOs
After analyzing the challenges related to data quality, sovereignty, performance, and cost-efficiency, one conclusion stands out: a modern IDP platform must be open, secure, orchestrated, and sovereign.
This is what AITECA offers, based on three key pillars:
- A sovereign and secure platform
On-premises hosting, isolated AI execution, ISO 27001 compliance, and no calls to external generative AI services. - Smart integration of all relevant AI technologies
Symbolic AI, multimodal AI, multilingual deep OCR, large language models (LLMs), anomaly detection, and fraud detection… - Efficient and optimized AI
Energy footprint measurement, selective model activation, controlled performance.
AITECA thus transforms document data into a true strategic asset, ready to provide IT systems, business units, and internal AI with relevant information that can be trusted.
