Optimize your extraction models' accuracy in 1 click 🪄

At Mindee, our mission has always been to make data extraction both powerful and accessible. However, while analyzing our dashboards and customer support feedback, we noticed something surprising: the vast majority of our users never tweak their model guidelines.

Why? Because it feels complex, or simply because it’s hard to know where to start if you don’t have an AI familiarity.

The problem is, relying soIf you have landed here from our recent announcement, you already know the headline: Mindee now offers 1-click data schema optimization.

Let's dive deeper into exactly why we built this, how the agent works under the hood, and what it means for your document processing pipeline.

‍

Behind the magic: Why guidelines actually matter

Standard catalog models are excellent starting points, but they are inherently generalized. When you process highly specific, niche, or messy documents, extraction models need context to perform perfectly.

IMAGE GUIDELINES FILLED

Historically, providing that context meant manually tweaking field guidelines—essentially doing prompt engineering. Our data showed that almost nobody was doing this because it requires trial, error, and AI expertise. Here is exactly what our new background agent does when you click that button:

‍

Real-world scenario: Complex vendor invoices

Let's ground this in a practical example. Imagine your operations team receives massive, multi-page PDF files containing various types of French invoices.

Relying on standard OCR means you might miss edge cases—for example, a specific vendor might use an unusual acronym for their tax totals, or bury the invoice date in a weird footer.

Instead of opening a support ticket or guessing how to rewrite the field descriptions to catch this anomaly, you simply ensure you have 5 examples in your history and click the button. The AI agent recognizes the pattern, updates the guidelines to anticipate that vendor's quirk, and your accuracy spikes without you writing a single line of instruction.

‍

The developer and business impact

We designed this feature to solve a massive operational pain point: improving accuracy usually means dedicating engineering resources or breaking existing code. We eliminated that friction entirely.

• Zero API breaking changes: Your API keys, endpoints, and technical architecture remain completely untouched.
• Reduced maintenance: You no longer need to build and maintain in-house validation scripts to correct recurring extraction errors.
• Immediate ROI: Higher extraction accuracy translates directly to fewer manual reviews and faster processing times for your team.