Curious about what keeps experts, CEOs and other decision-makers in the Intelligent Document Processing (IDP) space on their toes? Get food for thought on IDP-related topics from the industry’s leading minds.
In this opinion piece, Avi Rafalson, Co-Founder and CEO of Intelligent Document Processing (IDP) vendor DOConvert, addresses the recurring debate between general IDP models and specialist IDP models and shares his perspective on the matter.
In theory, general purpose IDP models promise elegance and scale. Train once, apply everywhere. But in practice, where documents aren’t pristine, where clients bend formats, and where “almost right” can still mean operational chaos, we’ve found that generalism often fails to deliver.
What we’ve learned from deploying Intelligent Document Processing across real world environments is that documents don’t live in a vacuum. They carry business meaning, follow messy conventions, and vary not just across industries but across vendors, partners, even departments. In this context, a one size fits all model often becomes a one size fits nobody well solution.
When Theory Meets Reality
Generalist models shine in benchmarks. They handle a wide array of templates, fields, and document types with impressive baseline accuracy. But business flows are not benchmark tests. They are living systems with nuance, rules, exceptions, and consequences. And this is where general models hit their limits.
We’ve seen this repeatedly: models that extract correct fields but misunderstand intent; models that fail not because they’re wrong, but because they’re context blind. For example, two invoices may look similar on the surface but have very different downstream requirements based on currency rules, tax jurisdictions, or ERP integration logic. A generalist model can’t adapt unless someone teaches it what matters and how.
The Case for Specialist Models
By contrast, specialist models start with the outcome in mind. They’re not trained to handle “documents” broadly, they’re trained to do a job. That might be capturing a customer order and validating it against an internal catalog, or extracting line items from a goods receipt and mapping them to a delivery confirmation system. These models perform better not because they’re technically superior, but because they’re situationally aware.
In our experience, building a specialist model means more than narrowing the input, it means tuning the entire system: the field extraction, the layout assumptions, the validations, and most critically, the learning loop. These models evolve by learning from the edge cases, not ignoring them.
And increasingly, we’re seeing models that don’t just learn from the document itself, but from the business cause of the data. When connected through bi directional integration with downstream systems, these models begin to understand the implications of each field, what it triggers, where it flows, what happens when it’s wrong. The model becomes responsible not just for extraction, but for preparing the data for what comes next.
Which brings us to a key realization: edge cases are not the exception. In business documents, they are the norm. Pricing discrepancies, foreign characters, scanned stamps, and missing data happen daily. A model that isn’t built to learn from them is a model that will stall.
Learning Through Purpose
This is where the real intelligence emerges. Not in bigger datasets, or more compute, but in alignment. When a model is trained to complete a specific task within a business process, it has a reason to improve. And when it’s surrounded by human feedback, clear validations, and system memory, it doesn’t just extract, it understands. Or at least, it behaves like it does.
That’s how we’ve started to think of models: not as tools, but as teammates. They’re not expected to be perfect. But they are expected to learn, to adapt, and to contribute to the flow of work with increasing trust.
The Road Ahead
The industry is moving fast toward bigger, more universal document models. That may have value, especially as a foundation. But for organizations that rely on accuracy, compliance, and business alignment, the frontier isn’t size. It’s specificity. It’s about models that don’t just “see” documents, but grasp what needs to happen next.
Maybe the next leap in IDP isn’t another architectural breakthrough. Maybe it’s building the first model that knows how your documents think and acts accordingly.
About the Author
Avi Rafalson is the CEO of DOConvert, a company at the forefront of Intelligent Document Processing solutions. With a passion for driving digital transformation, He leads the development of Poly, a model purpose-built to behave like a teammate, context aware, business aligned, and ready to handle the complexity of real-world documents.
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