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What our clients say about Twilix.
Don't just take our word for it - see what our customers and users have to say!
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Y Combinator
The DeepEval LLM Evaluation Platform.
Built by the creators of DeepEval, companies of all sizes use Confident AI to benchmark, safeguard, and improve LLM applications, with best-in-class metrics and guardrails.
LLM Evaluation, Done Right.
Confident AI's core features ensures you do LLM evaluations the proper way to achieve the best possible LLM testing results required for iteration.
Curate Dataset
Annotate datasets on Confident AI and pull it from the cloud for evaluation.
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Run Evaluations
Benchmark LLM systems to experiment with different implementations.
Improve Dataset
Keep your dataset up to date with the latest realistic, production data.
Align Evaluation Metrics
Tailor your LLM metric results to your specific use case/criteria.
Curate Datasets On One, Centralized Platform.
Dump Google Sheets, Notion, or whatever your domain experts are currently using to curate evaluation datasets, and let Confident AI unify your LLM evaluation workflow.
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from deepeval.datasets import EvaluationDataset
from deepeval.metrics import AnswerRelevancy
dataset = EvaluationDataset()
dataset.pull(alias="QA Dataset")
dataset.evaluate(metrics=[AnswerRelevancy()])
Move Fast, Don't Break Things.
Our Pytest integration enables you to unit test LLM systems in CI/CD, compare test results, detect performance drift, without changing the way you work.
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from deepeval import assert_test, LLMTestCase, EvaluationDataset
from deepeval.metrics import AnswerRelevancy
dataset = EvaluationDataset()
dataset.pull(alias="QA Dataset")
@pytest.mark.parametrize("test_case", dataset)
def test_llm_app(test_case: LLMTestCase):
assert_test(test_case, metrics=[AnswerRelevancy()])
> pip install -U deepeval
> deepeval test run test_llm.py
✓ Test Run Completed.
✓ 1/1 test case(s) passing.
Monitor & Trace, To Improve Evaluation Data.
Confident AI will automatically evaluate monitored LLM outputs, and let you decide on which real-world data to include in your dataset for subsequent testing.
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import deepeval
# after your LLM has finished generating
deepeval.monitor(
input="Whatever input your LLM is getting",
response="Whatever your LLM has generated",
model="gpt-4o",
*kwarg
)
Annotate Once, to Align Metrics With Expectations.
Everyone has their own opinions, and Confident AI is here to make sure your evaluation metrics are as aligned with your company's values as possible.
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if needs_improvement(your_llm_app):
you_need_confident = True
else:
you_need_confident = False
Our Evaluation Way Is Proudly Open-Source
I mean, how else could we possibly deliver you the best evaluation results?
300,000+
Daily evaluations
200+
Github stars
100,000+
Monthly downloads
WARNING: You'll Hate Confident AI If...
I mean, how else can we possibly deliver you the best evaluation results?
300,000+
Daily evaluations
200+
Github stars
100,000+
Monthly downloads
Evaluation metrics powered by DeepEval for all use cases.
Evaluate any criteria using research-backed LLM-as-a-judge metrics, proven to be as accurate and reliable as human evaluation. These metrics cover all types of LLM systems - ranging from RAG, agents, to chatbots.
Advanced LLM observability, with best-in-class evaluations.
Easily A/B test different hyper-parameters such as prompt templates, models, and enable best-in-class online LLM evaluations to get real-time feedback on how your LLM system is performing under different configurations. Tracing and user-feedback collection included.
Tailored synthetic dataset generation for every customer.
Generate datasets that makes sense for your LLM evaluation use case. These generations are grounded in your knowledge base, and can be customized for any output formats. You'll also be able to annotate, edit, and version datasets on the cloud.
Automated LLM red teaming to detect safety risks.
Discovery which combination of hyperparameters such as LLMs and prompt templates works best for your LLM app.
2.4x
Less time to production
No more time wasted on finding breaking changes.
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Evaluations completed
Users evaluate by writing and executing test cases in python.
Powerful features to productionize LLMs with confidence.
A/B testing
Compare and choose the best LLM workflow to maximize your enterprise ROI.
Evaluation
Quantify and benchmark your LLM outputs against expected ground truths.
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Output classification
Discover recurring queries and responses to optimize for specific use cases.
Reporting dashboard
Utilize report insights to trim LLM costs and latency over time.
Dataset generation
Automatically generate expected queries and responses for evaluation.
Detailed monitoring
Identify bottlenecks in your LLM workflows for targeted iteration and improvement.
Book a demo today.
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1. Create account
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2. Integrate with your tools
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3. Close more sales
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Rebeca Miller
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The future of evaluation depends on you.
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Sales teams
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Marketing teams
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Support team
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