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For AI teams & enterprises

Evaluate your model,
Know where you stand

Evaluate your model, Know where you stand

Get unbiased, reproducible scores — the same tests we run on leading frontier models

Closed automatic evaluation

Instant access to run our hidden benchmarks.

Automatic statistics on failure rate, tool calls numbers

Performance report of your model across domains

Compare against frontier models

Talk to an expert

Human-in-the-Loop

Closed evaluation with expert review and detailed failure analysis of runs.

Domain experts

Qualitative feedback reports

Edge case identification

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Off-the-Shelf Datasets

License our pre-built RL gyms.

Non-exclusive commercial license

Immediate delivery

15+ Verticals available

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Bespoke RL-gyms

Bespoke environments for your specific domain.

TBD / project

Tailored to your business logic

Private, exclusive datasets

Full ownership of artifacts

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Why hidden benchmarks matter

Prevent overfitting

Public benchmarks are often contaminated in training data. Our private, hidden test sets ensure models haven't "memorized" the answers, providing a true measure of intelligence.

Real-world complexity

We don't just test multiple choice. Our RL Gyms simulate complex, multi-step agentic workflows that mirror actual production environments in retail, finance, and coding.

Dynamic evolution

Our benchmarks evolve weekly. As models get smarter, our tests get harder, ensuring the leaderboard remains a relevant signal for the frontier of AI capabilities.

1 def evaluate_generalization(model):
2 # Public benchmarks are often leaked
3 public_score = model.predict(GSM8K) 
# High risk of
4 # contamination
5
6 # Toloka Hidden Set ensures validity
7 private_score = model.predict(TOLOKA_HIDDEN_V4)
8
9 if private_score < public_score * 0.8:
10 return "Overfitting Detected"
11
12 return "True Generalization"

1 def evaluate_generalization(model):
2 # Public benchmarks are often leaked
3 public_score = model.predict(GSM8K) 
# High risk of
4 # contamination
5
6 # Toloka Hidden Set ensures validity
7 private_score = model.predict(TOLOKA_HIDDEN_V4)
8
9 if private_score < public_score * 0.8:
10 return "Overfitting Detected"
11
12 return "True Generalization"

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Evaluate your model,Know where you stand