Arena Assessment benchmarks every leading LLM on your actual documents — invoices, contracts, claims, medical reports — and tells you exactly which provider to use, for which document type, at what cost.
Arena Assessment is built for teams making high-stakes document AI decisions.
You need independent data to justify a six-figure infrastructure decision to the board. Vendor demos looked great — but you've been burned before by production performance that didn't match the sales pitch.
Your team has no baseline. Everyone has an opinion about which LLM is best, but nobody has tested them on your actual documents. You need a structured evaluation before committing resources.
Your current provider works — sometimes. Some document types extract perfectly, others need manual correction. Costs have scaled faster than expected. You suspect a hybrid approach could cut costs 30-60%, but you don't have the data to prove it.
Every team using LLMs to extract data from documents hits the same wall. The wrong choice compounds silently — until it becomes a crisis.
A European insurance company was about to commit to a single LLM provider for claims document processing. Arena Assessment benchmarked 6 providers across their real claims forms, medical reports, invoices, and policy documents.
The finding: GPT-4o scored 96.2% field accuracy on invoices but dropped to 78.4% on medical reports. Claude Sonnet led on medical documents at 94.1% but cost 2.1x more per extraction. Gemini Flash scored 91.8% on invoices at 1/4 the cost.
The recommendation: a hybrid stack — Claude for medical reports, Gemini Flash for invoices, GPT-4o for policy documents. The client implemented the hybrid approach and cut extraction costs by 62% with less than 1% accuracy loss overall.
We've seen every approach. None of them produce the data you need to decide with confidence.
Your team spends weeks building one-off evaluation code. Tests 2–3 providers on a handful of documents. No ground truth, no statistical rigor, no cost analysis. Then the code is thrown away.
Vendors run benchmarks on their best datasets and publish the results. Your invoices, contracts, and claims forms are not their demo PDFs. Their numbers don't transfer to your pipeline.
Manual spot-checks on 5–10 documents. No latency data. No cost projection. No field-level accuracy. No statistical significance. A gut feeling dressed up as a decision.
Skip evaluation entirely. Choose based on brand recognition or a colleague's recommendation. Discover the accuracy or cost problem 3 months later — when switching costs are already six figures.
We've watched dozens of teams try the DIY approach. Here's what it actually looks like.
A structured engagement, not a product trial. We do the work — you get the answers.
We understand your document landscape: types, volumes, languages, accuracy requirements, latency constraints, and compliance framework.
Remote · 60–90 minYou provide real documents. We build ground truth annotations and extraction schemas tailored to your use cases.
3–5 business daysMultiple LLM providers tested across four dimensions: accuracy, speed, cost, and compliance eligibility. Controlled conditions.
3–5 business daysDetailed technical report with per-model, per-document-type analysis. Live session with clear recommendations for your context.
Report + 90 min sessionNot a slide deck with opinions. A technical audit report built on your data.
Every deliverable is built from your data, not templates. Here's what lands on your desk.
Download a sample Arena Assessment report — real data, real methodology, real recommendations. See exactly what you'll get before committing.
Both tiers include the full methodology, report, and live walkthrough.
If our benchmark methodology cannot produce statistically significant results for your document types, you pay nothing. We assess feasibility during the Briefing phase — before any commitment. If we can't deliver rigorous, actionable data, we'll tell you upfront.
The assessment is the beginning, not the end. Your document AI landscape keeps evolving.
Your team uses the report to select providers, configure pipelines, and set accuracy/cost baselines. The data eliminates months of internal debate.
The benchmark results become your reference point. When a new model launches or accuracy drifts in production, you have data to compare against.
Most clients return for a follow-up assessment within 6–12 months. New models, new pricing, new document types — each is a trigger to re-benchmark. Returning clients receive preferential pricing and faster turnaround.
They pick the datasets, the document types, the evaluation criteria. Your invoices, contracts, and claims forms are not their demo PDFs. Their published numbers don't predict your results.
Ground truth annotation. Multi-provider API integration. Field-level accuracy scoring. Cost normalization. Statistical testing. Your team has better things to build. We've already built the infrastructure.
A benchmark from six months ago is already stale. What worked for your competitors may not work for your documents, your schemas, your volumes. You need a current answer, not a historical one.
An internal evaluation by the team that's already leaning toward a provider isn't independent. Arena Assessment delivers third-party data — controlled, reproducible, statistically significant — that justifies a six-figure infrastructure decision.
27001 · 27017 · 27018. Triple-certified information security. Your documents, your rules.
Processed in production for European banking, insurance, and logistics clients.
We built the benchmark because we needed it ourselves. Now we offer it as a service.
Fill in the form below. We'll get back to you within 48 hours with a scoping call invitation and a preliminary quote.