Agentic Solutions / Evaluating LLM performance
Confidently scale LLM in production
For businesses integrating generative AI, evaluating performance is key. Regularly assessing how well these models meet user needs and business objectives ensures that the AI continues to add value and drive innovation without regressions.
How we can help
The lack of evaluations has been a key challenge for deploying AI models to production. By treating model evaluations as unit tests for LLM and pairing prompting with evaluations, our experts can transform ambiguous dialogues into quantifiable experiments, making AI a more manageable software product.
Create human-graded evaluation suites
Our team develops comprehensive human-graded evaluation suites to assess the performance of your LLM in various scenarios, including bad output formatting, inaccurate responses/actions, going off the rails, bad tone and hallucinations, ensuring that it meets user needs and business objectives effectively.
Create model-graded evaluation suites
We also design model-graded evaluation suites, enabling you to automatically evaluate and monitor your LLM's performance at scale. By reducing human involvement in parts of the evaluation process that can be handled by LLMs, human testers can be more focused on addressing some of the complex edge cases that are needed for refining the evaluation methods.
Govern and upgrade models
To maintain high-quality performance and prevent regressions, we provide ongoing model governance and upgrades. Our team continuously refines evaluation suites, updates models with new data, and implements improvements based on evaluation results, ensuring your LLM remains a valuable asset that drives innovation.
Companies we've helped
Three weeks to launch a scalable AI-powered marketplace solution with secure governance
In just three weeks, we built and deployed a secure human-in-the-loop matchmaking system for a service marketplace platform. Using generative AI and an open-source AI governance platform, the solution minimizes operational costs, accelerates lead response times, and scales without increasing headcount, with oversight, traceability, and control at its core.
Case Study
Babbly
An AI-enabled demo from concept to functional model in just 2 weeks
Babbly's founder needed a machine learning algorithm in a few weeks to close a pre-seed investment round.
Case Study
Blue J Legal
Simplifying the work of tax professionals with a mobile app
Blue J Legal successfully launched its AI-powered research tool and acquired its first paying customers during their partnership with Rangle.
Case Study
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