Academic benchmarks measure the wrong things. We test AI on the tasks your business actually needs: document analysis, code generation, customer support, and more.
Most AI leaderboards use benchmarks like MMLU, HellaSwag, and HumanEval. These tests have serious limitations for business decision-making:
Tests like MMLU and HellaSwag use contrived questions that don't reflect real work. High scores don't mean the model works well in production.
Models are increasingly trained to score well on specific benchmarks, not to be genuinely capable. Leaderboard positions become misleading.
Academic benchmarks test isolated knowledge, not the ability to handle ambiguous, multi-step business workflows with real constraints.
Academic benchmarks only measure accuracy. They ignore latency and cost - critical factors for production deployment decisions.
Tests derived from real work: summarizing contracts, writing code, handling support tickets. If it's not something a business does, we don't test it.
AI judges plus human review for quality assessment. We catch the nuances that automated metrics miss.
We measure response time for every task. A model that takes 30 seconds isn't useful for real-time applications.
Every benchmark includes cost per task. Know exactly what you'll pay before you deploy.
Writing, reviewing, and debugging code across languages
Handling customer inquiries with accuracy and empathy
Processing data and generating business insights
Analyzing contracts and legal documents for key issues
Processing financial data and generating insights
Creating marketing copy, emails, and business content
Query generation and database optimization
Summarizing documents and extracting key information
Accuracy & completeness of responses
Response latency in milliseconds
Price per 1M tokens processed
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