When teams build IDP systems with LLMs, reaching around 90 to 95% accuracy can feel like a milestone. But the difference between 95 and 99% can be critical, and where DIY builds tend to stall without significant engineering effort.
Reaching that threshold with a DIY or entry-level solution feels like a milestone worth celebrating. But in reality, it’s a warning sign, especially for organizations aiming to automate high-stakes workflows.
In many sectors, even minor errors can trigger costly rework, compliance issues and even reputational damage. That’s why precision document processing isn’t just a technical goal or a nice-to-have. It’s a business necessity.
This article unpacks why ‘almost accurate’ isn’t good enough and what true automation-ready precision really looks like.
The accuracy ceiling: why that extra 5% is harder than it looks
In DIY LLM-based IDP builds, accuracy often plateaus in the mid-90s. It isn’t because the model can’t improve, but because the system isn’t designed to learn.
Foundation models run fixed prompts on fixed weights, which means the output you get on day one is the output you get on day 100. Without an adaptive harness that reshapes inputs, grounds outputs in source documents and captures corrections for future improvement, performance simply stalls.
That final few percent is where formatting anomalies, extraction failures and document variations tend to sit. Solving them manually becomes a long, expensive cycle of prompt tuning that never quite holds.
At scale, even a 5% error rate creates hundreds of human interventions. What starts as a mostly automated workflow becomes a constant burden of exception handling, slowing teams down and eroding ROI. Breaking through this ceiling is essential for enterprise-grade performance.
Mission-critical workflows demand more
In industries like banking, finance and insurance, precision is a regulatory necessity. Even a small error in records or submissions can jeopardise compliance or impact customer outcomes.
In these settings, 95% accuracy doesn’t reduce risk; it simply moves it further down the workflow. Without higher precision, businesses are forced to reintroduce manual checks, negating the very benefits automation was supposed to deliver.
Why most DIY IDP builds stop improving
Hitting 95% accuracy is often where DIY IDP projects begin to stall. Not because the remaining 5% is impossible, but because the underlying setup isn’t built to learn within your specific problem. It can’t absorb new context or evolve with every correction, so performance stays flat even as usage grows.
Many in-house approaches depend on prompt chains and tech that is unable to adapt. As documents evolve and edge cases appear, each adjustment can introduce new inconsistencies, making it harder to maintain accuracy over time.
Without an automated way to capture corrections, apply them and learn from new examples, teams risk spending more time maintaining the system than improving it. This leaves teams trapped in maintenance mode, unable to scale precision with complexity. True automation requires infrastructure that learns, continually and contextually.
Learn why in-house IDP systems often come with hidden long-term costs.
What true precision document processing looks like
Precision means high accuracy, sure. But it’s also about consistent, explainable outcomes across all document types. Achieving that level of stability requires a system that can learn safely from user corrections and apply those improvements across future documents.
Affinda supports this through model memory, which captures validated feedback and uses it to improve extraction performance in real time. This architecture supports your evolving document processes, enabling new variations and edge cases to be captured without disrupting your existing model?
We deliver a level of accurate document processing that supports true straight-through processing (where documents flow through the system without human review), even in complex, regulated environments.
Get the full framework for choosing build vs buy
Download our free whitepaper for a clear comparison of building an IDP system with an LLM versus buying a production-ready platform. See the trade-offs in accuracy, maintenance and long-term scalability so you can make the right decision for your team.
The ROI of getting to 99%+
Higher accuracy pays off, quite literally. Moving from 95% to 99% accuracy cuts errors from 5% to 1%. That may sound small, but it represents an 80% reduction in documents that need human review.
For enterprise teams, especially in regulated industries, this accuracy jump enables straight-through processing. That unlocks measurable gains in productivity, compliance confidence and overall cost-efficiency. It’s not just better performance, it’s better business.
Aim higher than 95% with Affinda
When automation stakes are high, 95% accuracy shouldn’t be your finish line but your baseline. Settling for ‘good enough’ leaves you exposed to risk, inefficiency and hidden costs that compound over time.
Affinda’s approach to precision document processing is designed for enterprise demands, enabling automation that removes effort. Our platform grounds every extraction in the source document, which helps prevent hallucinations and ensures the value returned is the value that actually appears on the page. Combined with instant learning from user corrections, this is what lifts accuracy beyond what DIY LLM chains can achieve.
If your current solution stops short, it’s time to raise the bar. Start your free trial today and experience what accuracy beyond 99% really means.
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