I've seen too many organizations choose software based on the prettiest demo. And then find themselves, six months later, with a tool nobody uses because "it doesn't work the way we thought".
It's not their fault. The Document Intelligence market has exploded in recent years, and everyone promises the moon: generative AI, semantic search, natural language processing, magic words that make eyes sparkle but often hide very different realities.
According to OpenText, Intelligent Document Processing combines AI, machine learning and automation to transform unstructured documents into usable data. But not all solutions are the same, and the difference is in the details.
After years spent in this sector, first as a customer looking for solutions, then as a producer, I've developed an approach I want to share. It's not a magic formula, but a structured way to avoid the most common rip-offs.
First rule: start from the problem, not the solution
It sounds obvious, but how many times have I seen it done the other way around. "We want to implement AI for documents" is not a requirement. It's a solution looking for a problem.
The real requirement is something like: "Our lawyers lose 2 hours a day searching for precedents in our archives" or "We can't respond quickly to audit requests because the documents are scattered everywhere" or again "We have clients who ask us for information and we don't know in which of the 50 folders the answer is".
These are concrete problems. From here you can start thinking about what you really need.
As Nanonets highlights in their IDP blog, the difference between IDP and traditional automation lies in the ability to understand context, not just extract text, but understand its meaning. This is what makes the difference between a useful tool and a frustrating one.
In my experience, most organizations looking for Document Intelligence have one of these three problems, or a combination: difficulty finding information in existing archives; paper or scanned documents that aren't searchable; or excessive time spent reading documents to extract specific information.
Once you're clear on the problem, you can evaluate solutions with an objective criterion: does it solve this specific problem? How well? At what cost?
The test nobody does (and everyone should)
Want some practical advice? When you evaluate Document Intelligence software, ask to run a trial with your real documents. Not the demo ones, clean and perfectly formatted. Yours. The ugly ones, poorly scanned, with stamps and signatures on top.
Because most demos use "perfect" documents, native text, well formatted, in English. And with those any system works well. The problem is that your documents aren't like that.
Try searching for something specific that you know where to find. Does the system find it? In how much time? With what accuracy? Then try a more complex search, like "contracts with exclusivity clause signed in 2024". Does it work?
I've seen spectacular demos collapse miserably when we moved to the client's real documents. Better to find out before signing the contract.
The security question: the right questions to ask
When you ask about security, don't settle for "we use AES-256 encryption" or "we're ISO 27001 certified". They're important, sure, but they don't tell you what you need to know.
The questions you should ask are others. Where are my documents physically processed? This is the fundamental question. If the answer is "on cloud servers", ask where these servers are, who accesses them, and what happens to the documents after processing.
Are documents used to train the AI? Some providers do it, others don't. If confidentiality is important to you, you need to know.
What happens if I want to delete all my data? Ask for the exact process. "We delete it" isn't enough, do they also delete from backups? In how much time? How can you verify it?
And finally: does it work offline? If the answer is no, it means every search passes through external servers. This has implications both for security and availability.
OCR: the most underrated feature
If you have historical archives, scanned documents, or you still receive paper material, OCR (optical character recognition) is probably the most important feature. And also the one where there's the biggest difference between solutions.
Basic OCR, the kind you find for free in many tools, works decently with clean documents, well printed, straight. But try with a third-generation photocopy, with a fax from the '90s, with a stamped and countersigned document, and you'll see the difference.
Advanced OCR systems handle complex layouts like tables and columns, recognize text even in difficult conditions, and maintain the structure of the original document. As OpenText explains in their IDP guide, the difference between basic OCR and advanced IDP lies precisely in the ability to understand the structure of the document, not just the text.
My advice: during evaluation, test with the worst documents you have. If OCR works with those, it will work with everything.
Artificial intelligence: distinguishing marketing from substance
"We use AI" has become the new "we use the cloud", everyone says it, but it means very different things.
What you want to know is: what kind of AI? To do what? And above all: where does it run?
Semantic search, the kind that understands that "automobile" and "car" are the same thing, is AI. The automatic extraction of dates, amounts, names from documents is AI. The generation of responses based on the content of your documents is AI.
But there's a huge difference between a system that uses local models, that run on your infrastructure, and one that sends your documents to external services like OpenAI or others. In the second case, every search you do means that your documents are processed by third parties. As a Harvard Business Review analysis on the EU AI Act highlights, the confidential nature of enterprise data is one of the main barriers to LLM adoption. For certain organizations, sending documents to external services is simply unacceptable.
Also, ask how the responses are generated. A good system cites sources for you, "I found this information in document X, page Y". A less good system gives you generic answers without telling you where they come from. In the second case, how can you trust it?
Costs: don't just look at the price
The initial price of a software is only part of the total cost. And often it's not even the biggest part.
What you have to consider is the cost over time. Some pricing models seem cheap at first but grow quickly with usage because they charge you per query, per document, per user, per gigabyte of storage.
Do a realistic calculation. How many documents do you have? How many searches will you do per day? How many users will use the system? Project these numbers over 3-5 years and compare the total costs.
And don't forget the hidden costs. How much does integration with your existing systems cost? Do you need dedicated hardware? Who takes care of maintenance? How much does technical support cost?
A system that costs more at the beginning but has fixed and predictable costs could be much more convenient in the long run compared to one with a nice starting price but that then fleeces you with variable costs.
Adoption: the factor that decides everything
You can buy the most advanced software in the world, but if nobody uses it, you've thrown money away.
Adoption depends on one thing only: does the software make life easier or harder? If to do a search I have to open a separate application, log in, navigate three menus, set filters, I'll use it twice and then go back to searching by hand.
If instead I can ask a question in natural language and get an answer in two seconds, I'll use it every day.
During the evaluation, have the system tested by real users, not by the IT department, but by the people who will have to use it daily. Observe whether they understand right away how it works or whether they flounder. Ask them what they think, unfiltered.
I've seen projects fail not because the software was poor, but because it was too complicated for daily use. Usability isn't optional, it's the factor that decides whether the investment will pay off or not.
My final advice
If I have to sum it all up in a few words: don't buy a demo, buy a solution.
Start from the problem you want to solve. Test with your real documents, not with sample ones. Ask the uncomfortable questions about security. Calculate the total costs over several years. And involve the people who will have to use the system in the decision.
If you do all this, you'll avoid most of the rip-offs and you'll find the right tool for your organization. Whether it's DocZoom or another, the important thing is that it works for you.
If you're in a software selection phase, you can move on to Request Demo and set up a test on real documents and shared criteria.
