Why IDP Can Fail and How to Make It Work for Your Business

For years, companies have tried to bring order to the chaos of paperwork. Documents appear in every part of the business, from onboarding and compliance to billing and support, which eventually slows processes and stretches teams thin.

Intelligent Document Processing (IDP) has emerged as a powerful solution, as it enables businesses to extract value from unstructured data. But here’s the reality: IDP can fail too, not because the technology lacks potential, but due to inherent ambiguities in real-life business use cases—ambiguities that require business-specific modeling and integration work to resolve.

The Problem with Most IDP Implementations

The most common reasons behind IDP failure include unclear objectives, poor data quality, and a lack of compliance. More than that, the documents themselves reflect not only inconsistencies in naming and formatting, but differences in how people define and use the underlying business concepts.

When IDP is not embedded into your operational infrastructure such as your CRM, onboarding platform, risk engine, or compliance pipeline, it becomes an added burden, not a solution. Employees end up switching between systems, manually moving data, or adjusting processes just to make the tool work. But it should work the other way around.

Even when deployed, these systems can be fragile. They struggle to consolidate information when customers’ names are misspelled. They fail silently when documents differ in the order or format in which they present information, or when they use different naming conventions to express the same ideas.

This fragility often stems from a common issue: IDPs are too often built as one-size-fits-all templates. They don’t reflect your company’s unique rules, fields, or document logic. To truly work, an IDP must be designed to handle the full diversity of your data and be built to fit the way your business actually runs.

How We Build Automation That Actually Works

That’s where we come in. Every project we build and every system we develop is tailored to each company’s specific needs, business workflows, and data diversity.  By deeply integrating with existing tools and understanding the nuances of each business, we ensure that automation adds value from day one.

Here are a few examples of how we’ve helped businesses overcome these challenges and turn document processing into a real advantage.

Healthcare: Reducing Compliance Burden

In the healthcare space, one of our clients in drug discovery was overwhelmed by inconsistent document formats tied to clinical trials. These files often included complex tables, embedded values, and highly specific medical terminology, making manual processing slow and error prone.

We developed a custom document processor powered by AI that could parse a wide range of formats and intelligently map values to relevant medical concepts. To improve long-term accuracy, we also built in an interactive training layer that allowed their team to refine the model on real documents over time.

Crucially, we integrated this solution directly into their existing research and compliance workflows. As a result, data extraction happens as part of their normal process, without the need to switch platforms or introduce new tools. This reduced administrative burden and ensured that compliance-grade data could be extracted quickly.

Hiring: Making Resume Review Fast and Searchable

For hiring platforms, resumes are notoriously messy, often free-form formats, inconsistent structures, and endless variations. One of our clients struggled to turn those documents into usable insights at scale.

We created a robust resume parser that extracts key applicant details like experience, skills, and education. To support recruiter workflows, we paired it with an interactive viewer that overlays metadata onto the visual layout of each document, allowing teams to search, filter, and highlight exactly what they need.

Just as importantly, we integrated the entire solution directly into their existing recruitment system so their teams could continue working within the tools they were already using. Teams could actually work in line on an exact representation of the resume itself, in a context-aware way. Resume evaluation became faster, more consistent, and far more scalable.

Media: Structuring a Global Content Library

A global digital media platform came to us with a familiar problem: massive volumes of unstructured content—articles, media pages, images—all disconnected and difficult to organize. Their teams couldn’t categorize or search content reliably, let alone personalize user experiences.

We built the Asset Data Directory that transformed their asset library into a structured, searchable system. The system indexed millions of articles in deep and complex ways. Information inside the articles was dynamically enriched, linking to relevant information. The text content was semantically indexed to create a much more domain-friendly search experience. Document metadata, ontology, and taxonomy were interpolated and indexed relationally, and the documents overall were also indexed in a document database.

The result was an end-to-end pipeline that gave both internal benefits like extensive control over content syndication to concrete consumer benefits like improved recommendation accuracy and increased engagement.

Ready to Make Document Processing Smarter

We help teams around the world work more efficiently by integrating IDP solutions directly into existing workflows, ensuring systems run smoothly without disruption. From financial services to healthcare and media, our projects have delivered measurable improvements in speed, accuracy, and scalability.

If you're exploring how to make your document operations more intelligent and adaptive, we’d be glad to start a conversation.