Generative AI Evaluation for Enterprise
Bridge the trust gap to deploy production-grade genAI applications.
Lack of Trust Stalls Enterprise AI Adoption
Only 10% of enterprises have Gen AI in production, and more than 30% of Gen AI projects are abandoned after POC. The number one reason why companies stall is a lack of trust stemming from:
Poor Performance
Models hallucinate, exhibit unsafe behavior, or pose security risks.
Unproven ROI
Use cases are not adopted and targeted workflows remain unchanged.
Escalating Costs
Unmonitored usage leads to extensive cloud or vendor bills.
Bridge the Trust Gap for Enterprise Gen AI
The path to deploying with confidence in production is to systematically evaluate, improve and monitor GenAI systems for performance, safety, and reliability.
Get Trusted Insights
Use the trusted evaluation and benchmarking system for enterprise-grade GenAI.
Ensure Safety and Reliability
Avoid bias, hallucinations, poor accuracy, harmful responses, and malicious behavior.
Monitor for Peace of Mind
Keep track of latency and cost and get alerted for any issues or regressions.
How the Scale GenAI Platform Evaluates Applications
Trust in AI is earned through better data. Scale GenAI Platform combines automated evaluations with an expert workforce for human evaluations to build a “Trust Feedback Loop” of evaluation, improvement and monitoring.
Measure your AI
Automatically test your GenAI system against auto-generated evaluation datasets as well as against Scale’s industry leading proprietary benchmark datasets.
Set your own bar
Augment our industry best practice rubrics and datasets with custom metrics and datasets tailored for your domain and use case.
Verify with Human-in-the-Loop (HiTL)
Ensure quality control of auto-evaluation with industry-leading, efficient HiTL evaluation for the highest complexity test cases.
Iterate
Programmatically turn your evaluations into actions that improve your GenAI systems through RAG optimization and fine-tuning, then see your scores improve over time.
Deploy
Monitor production traffic to surface quality metrics, issues and alerts. Detect anomalies (e.g. prompts that are not covered by your evaluation datasets) to add them to your test suite.
