USE CASE

Data extraction

Stop guessing which model extracts your data best. Test extraction across multiple LLMs on your actual documents—invoices, contracts, forms—and know what works before you ship.

You've tested it in ChatGPT. Will it work on 10,000 documents?

Everyone's excited about AI extraction. But testing a prompt on a few examples and knowing it works reliably on thousands of messy real-world documents are very different things. Most teams either get stuck waiting for engineering, or ship too early and damage customer trust.

Defaulting to one model without evidence

Most teams default to the most popular model without testing alternatives. Trial-and-error happens during customer meetings, not before — leading to 4-5 hour troubleshooting cycles when edge cases surface.

Customers expect deterministic results

80% accuracy sounds solid until your customer notices the 20% that's wrong. Enterprise clients expect near-perfect extraction from a probabilistic system—without testing, you won't know where that gap is.

Domain experts locked out of iteration

Prompt improvements fall to the tech team despite them lacking the business context. Operations and customer success can't touch the system—even when they know exactly what's wrong.

OUR SOLUTION

How Lovelaice solves this

Your product team runs extraction experiments on real documents. No engineering ticket required. See what's accurate, what breaks, and what it costs.

Step 01

Bring your messy documents

The invoices with typos, contracts in multiple formats, forms with handwriting. Include your edge cases: missing unit prices, multiple languages, credit notes, split totals. That's the data that reveals real challenges.

Step 02

Test across 15+ models simultaneously

Run the same prompt across GPT-4o, Claude 4, Gemini 2.5, DeepSeek R1, and more. See side-by-side extraction accuracy and true cost per document — including hidden token overhead from reasoning models.

Step 03

Let domain experts evaluate

Invite your finance team or procurement specialists to rate outputs. No technical skills required—just their domain expertise. Move prompt iteration where it belongs: with the people who understand the data.

Step 04

Ship with confidence

Take validated configurations to production. You know the accuracy rate, the cost, and how it handles edge cases—before your customers find out the hard way.

USE CASES

Where teams use this

Procurement & finance

Purchase orders, invoices, expense reports. Extract structured data from documents that arrive in dozens of formats—and know your true cost per extraction before you scale.

Legal & compliance

Contract clause extraction across private equity, commercial leases, and regulatory filings. Different contract types need different model optimization—test before you commit.

Healthcare & clinical data

Structured lab report processing, clinical documentation, medical coding. When the target is 95% accuracy and you're at 80%, you need to know which model closes that gap.

Enterprise operations

Work orders, maintenance records, customer correspondence at scale. Processing 50K+ documents means cost differences of 10-100x between models matter enormously.

What teams discover

When teams properly benchmark extraction models, the results are consistently surprising—and the cost savings are real.

11x

Cost difference between GPT-4.1 ($0.014) and Claude Opus ($0.00125) at equal accuracy

86% vs 43%

Accuracy gap from prompt structure alone—XML formatting vs basic placeholders, same model

90%

Invoice extraction accuracy achieved in a single benchmarking session across 54 tests

Explore other use cases

Discover more ways Lovelaice can help your team.

Stop guessing. Start knowing.

Bring your documents, run your first experiment, and see results in one session. We'll guide you through the entire process.

Start for free