Locating accurate information in a PDF report is time consuming and tedious. Whether it is a number, a clause or a methodological point, you often have to go through many pages or reread the document several times to find precise information. AI is changing this precise point: it now makes it possible to query a PDF directly and to obtain a targeted response in a few clicks, without exhaustive manual reading.

This problem is an old one. McKinsey in his report published in 2023 called”The economic potential of generative AI” already estimated that workers spent about 20% of their time to search for existing information. What is changing today is not the need, but the way to respond to it: generative AI now makes it possible to directly query these documents, without going through an exhaustive manual reading.

Many solutions know how to answer questions about a single PDF correctly,. They quickly show their limits as soon as volumes increase, documents multiply or use becomes regular. The challenge is therefore not to “speak to a PDF”, but to choose tools capable of making this interaction possible. reliable, repeatable and usable in a professional context.

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The concrete uses behind the search for information in PDFs

The teams are not looking to discuss with a document, but to access existing information more quickly. This need is available in a limited number of concrete uses, which are very stable from one organization to another.

The main uses behind the search for information in PDFs

1. Targeted search for information
Quickly find precise information in a document: a clause in a contract, a number in a report or a rule in a procedure. The objective is to avoid manually browsing dozens of pages or multiplying approximate searches. This is the most frequent use case and the one that generally triggers the adoption of these tools.

2. Fast understanding of long documents
Efficiently analyze a large PDF without reading it all. This includes summarizing key points, extracting important information, or comparing multiple documents on the same topic. The aim is not to reformulate the content, but to significantly reduce the time needed to extract essential information.

3. Question-answer rooted in the sources
Obtain answers based directly on the documents, with the ability to identify exactly where the information was found. Traceability is central: without clear quotations or without an explicit link to the source content, confidence in the responses produced quickly disappears.

4. Multi-documentary synthesis
Produce a consolidated vision from several PDFs dealing with the same subject: compare reports, cross several procedures, identify convergences or differences between documents. This use is particularly useful when information is fragmented across multiple sources and no single document is sufficient to answer the question asked.

5. Recurring use of the same documents
Interviewing the same PDFs regularly, alone or in a team. At this stage, the challenge goes beyond the one-time tool: it becomes necessary to organize the documents, to control the sources used and to guarantee the consistency of the responses over time. It is this need that marks the transition from casual to structured use.

On a technical level, these uses are mostly based on the same principle: the Retrieval-Augmented Generation (RAG), which combines searching documents and generating answers from relevant passages. If you want to learn more about RAG, we invite you to read our article on the subject: “What is RAG (enhanced recovery generation)?”

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How to effectively compare tools for interacting with PDFs

Some tools are general purpose AIs capable, among other things, of reading PDFs. Others are built around a RAG document engine.

The second criterion is the real level of automation. A tool that requires manually reloading each PDF at each session remains suitable for occasional use. Conversely, the persistent ingestion of documents and their reuse radically change the experience.

La quality of answers is a third key point. It is not measured by the fluidity of language, but by the ability of the tool to remain strictly anchored in documents, to manage complex PDFs and to limit approximate answers or hallucinations.

Next come the traceability and security. The ability to identify the sources used and to see exactly where the information was found in the document.

Finally, the ability to integrate into a workflow distinguishes isolated tools from solutions that can really be used in a team. When use becomes frequent, the question is no longer “does it work”, but “is it sustainable over time”.

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The most relevant tools today for interacting with PDFs

In 2026, the offer is wide, but few tools respond correctly to all the uses described above. They can be grouped into three main categories.

The first brings together the AI generalists suchlike GPT chat, Google Gemini or Claude. They are powerful, versatile and effective for analyzing a PDF from time to time, but not very suitable for recurrent documentary exploitation.

The second category concerns specialized tools “chat with PDF”, of which ChatPDF is representative. They meet a specific need, but remain limited as soon as volume, frequency, or collaboration come into play.

Finally, some solutions are thought of as a documentary infrastructure, with a logic of collections, governance and orchestration of models. It is in this category that we are positioned Delos Docs, with an approach oriented to frequent and reusable use.

The next section details these tools one by one, starting with the ones that actually structure usage over time.

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Delos Docs: structuring frequent use of PDFs

What the tool actually does: Delos Docs allows you to communicate with PDFs via an RAG architecture designed for a recurrent use. The tool is not limited to an isolated document: it allows you to create document collections, explicitly selected, with which the AI is authorized to interact.

These collections can be organized by profession, project or type of document, and then queried over time without having to reload or recontextualize the files at each session. This allows you not to have to redownload all the documents each time before interviewing them.

Strengths

• Creation of documentary collections with up to 50 documents at the same time
• RAG with answers rooted in the sources (possibility to see where each piece of information was found in the documents)
• Capacity to test multiple models on the same documentary base

Limits and points of vigilance

• Integration into a full suite, involving a global subscription
• Not suitable for a universal chatbot covering all internal documentation (large volumes, transversal uses)
• This type of large-scale deployment requires a specific build : document structuring, access management and dedicated technical layer on the company side

Delos offers a limited free offer at 15 days, allowing you to test All the functionalities of the tool and to become familiar with the logic of collections and documentary research.

Delos stands out not for “talking to a PDF,” but for its ability to turn a set of documents into resource usable over time.
For example, you could have a collection with all of your internal procedures to know in one click if a certain point is covered in the procedures.

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ChatPDF: simple, but very limited in scope

What the tool actually does: ChatPDF is a specialized tool that allows you to ask questions to a PDF via a dedicated interface. The experience is immediate: one file, one discussion, without settings.

Strengths

• Very quick to get started
• Effective in understanding a isolated document
• No apprenticeship required
• Documentary collections

Limits and points of vigilance

• Around €16 per month For a only functionality, documentary research
• No transversal use (writing, reasoning, automation)
• Limited free version
• Limited in terms of characters per pdf for the free version

At comparable cost, tools like GPT chat, Gemini or Delos offer a much wider functional scope. ChatPDF remains relevant for a specific need, but quickly loses interest as soon as the volume or frequency increases.

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Generalist AIs: ChatGPT, Google Gemini and Claude

What they actually do: General AIs allow you to import one or more PDFs and ask questions directly on them. They are able to summarize long documents, extract accurate information and, in most cases, indicate the passages used to formulate their answers.

They are particularly effective for ad hoc analyses : quickly understand a report, check a clause, compare two documents or prepare a summary.

Strengths

• Ability to manage long and complex documents
• Versatility: PDFs are just one use among others (writing, reasoning, analysis, etc.)
• Good value/price ratio compared to single-function tools

Limits and points of vigilance

• Mainly use session by session
• No multi-documentary discussions
• ChatGPT weak point: Not the possibility of having the passage of the document used, underlined (feature present in Gemini, see above)
• Not adapted, as is, to a unique chatbot able to use all internal documentation

These tools are therefore very efficient for occasional use, but show their limits as soon as the need becomes frequent or as part of a recurring business process.

Conclusion

Interacting with PDFs using artificial intelligence has become a key use for quickly accessing existing information, but not all tools meet the same need. General purpose AIs are effective in analyzing a document from time to time, while specialized “PDF chat” tools quickly reach their limits as soon as the frequency or volume increases.

When the use becomes recurrent, the problem is no longer just asking a good question to a PDF, but not having to rebuild the context each time. Reloading the same documents, repeating the same instructions and starting from scratch ends up cancelling out part of the gain brought by the AI. This is precisely where Delos stands out: thanks to document collections, teams can easily query the same PDFs, without having to re-import or recontextualize them for each use. Information remains available, structured and usable over time.

Beyond this logic of reuse, Delos allows maintaining the benefit of AI while going further : the choice of The most appropriate AI model for the task. The same documentary corpus can thus be questioned with different models, depending on whether it is a question of analysis, synthesis or precise research, without changing tools or losing context. It is possible to test Delos for free to get a concrete idea of these uses by following this linkage.

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