Developing specialized AI benchmarks for professional workflows
Create a robust evaluation suite that tests frontier models against real-world, complex tasks in specific industries, rather than generic AI puzzles.
From AI Engineer — Why Frontier Models Fail as Buy-Side Analysts - Janie Gu, Google DeepMind & Suyeol Yun, LinqAlpha at 01:15
Problem: Existing AI benchmarks are often irrelevant to real work, overly expensive to maintain, and contain high variance due to implementation flaws, making them unreliable for industry advancement.
For: Companies, startups, and labs looking to validate, improve, or sell models for specialized enterprise use cases (e.g., finance, healthcare).
Products from this video
Examples
- MandateBench: Evaluates models on their ability to perform tasks spanning from a fund's mandate to a full investment memo, graded on the reasoning path.
Other takes on AI evaluation and benchmarking
- Voice agent benchmarking harness for picking a model stack
- Evaluation pipeline for high-stakes AI agents (trace, curate datasets, expert-reviewed failure modes, CI quality gates)
- AI technical due diligence service for investors and acquirers evaluating automation claims
- Event-driven adversarial PR review service as a standing agent job
- An independent evaluation company for foundation models and agents
- Per-agent tracking and evaluation tables for ROI reporting