What our clients say about Twilix.
Don't just take our word for it - see what our customers and users have to say!
Companies of all sizes use Confident AI to benchmark, unit test, and red team LLM applications - may it be LLM Chatbots, RAG, or Agents.
Unit test LLM systems, compare test results, detect performance drift, optimize on prompt templates, and identify the root cause of regressions.
Evaluate on any criteria using research-backed LLM-as-a-judge metrics, proven to be as accurate and reliable as human evaluation.
Battle-tested with over 4 million evaluations ran.
For LLM safety, RAG, agents, or chatbots, etc.
Generate test cases that makes sense for your use case on the cloud to manage evaluation datasets on one centralized platform.
Custom to your style & data, using our expertise.
Automated annotation, dataset versioning, and so much more.
Discovery which combination of hyperparameters such as LLMs and prompt templates works best for your LLM app.
No more time wasted on finding breaking changes.
Users evaluate by writing and executing test cases in python.
Run evaluations and monitor LLMs on the cloud through simple APIs via DeepEval, Confident AI's open-source LLM evaluation framework.
Deploy LLM solutions with confidence, ensuring substantial benefits and address any weaknesses in your LLM implementation.
Supply ground truths as benchmarks to evaluate your LLM outputs. Evaluate performance against expected outputs to pinpoint areas for iterations.
From altering prompt templates to selecting the right knowledge bases – we guide you towards the optimal configurations for your specific use case.
Utilize out-of-the-box observability to identify and evaluate use cases that bring the most ROI for your enterprise.
Compare and choose the best LLM workflow to maximize your enterprise ROI.
Quantify and benchmark your LLM outputs against expected ground truths.
Discover recurring queries and responses to optimize for specific use cases.
Utilize report insights to trim LLM costs and latency over time.
Automatically generate expected queries and responses for evaluation.
Identify bottlenecks in your LLM workflows for targeted iteration and improvement.
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Don't just take our word for it - see what our customers and users have to say!
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