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Autoblocks 2.0: Enhance Your LLM Product Accuracy

Build superior AI products with Autoblocks 2.0, a collaborative platform that improves through user and expert feedback, optimizing testing, evaluation, and monitoring.

Pricing

Paid, $199/mo

Semrush Rank

380.2k

Tech Stack
React

Powerful Features

Discover what makes Autoblocks AI stand out from the competition

1

Collaborative Testing & Evaluation

A platform that enhances your LLM products through user feedback and expert reviews, ensuring better tests for better products.

85%
2

High-Quality Test Datasets

Curation tools to create precise test datasets, keeping an eye on production with observability tools.

90%
3

Flexible SDKs

Integrate seamlessly with any codebase or framework, providing the flexibility needed for modern AI development.

95%
4

Human-in-the-loop Feedback

Improve accuracy with feedback from both non-engineers and subject matter experts, aligning automated evaluation metrics with human preferences.

100%
5

Scale with Security

Engineered to meet rigorous privacy and security standards, ensuring safe scaling of your AI products.

100%

Real-World Applications

See how Autoblocks AI can transform your workflow and boost productivity

Local Testing & Experimentation

Turbocharge your local testing processes to consistently deliver high-quality AI product iterations.

Impact Level:
High ROI

Monitoring & Guardrails

Configure evaluations and guardrails online to maintain a safe and trusted user experience.

Impact Level:
High ROI

AI Product Analytics

Connect AI product state directly to user outcomes, providing insights for continuous improvement.

Impact Level:
High ROI

Prompt & Context Management

Enable prompt collaboration without breaking code, and optimize context pipelines for accurate outputs.

Impact Level:
High ROI

Final Thoughts

Autoblocks 2.0 empowers AI teams to enhance their products by integrating expert-driven feedback and advanced evaluation tools, achieving unmatched accuracy and reliability.

Try Autoblocks AI Now

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