Deepmark AI- LLM assessment tool for task-specific metrics on your data
VerifiedShow HN: Deepmark AI- LLM assessment tool for task-specific metrics on your data
Quick Facts
- Pricing
- Freemium
- Category
- Other
- Popularity Rank
- #19 of 40 · by views
- Added
- May 2026
Tool overview
Overview
Deepmark AI is a benchmarking tool from IngestAI Labs that assesses large language models on extrinsic, or task-specific, metrics using an organization's own data. The repository describes it as aimed at Generative AI builders who need to choose among LLMs so that their applications have predictable and reliable performance. The tool provides pre-built integration with leading Generative AI APIs such as GPT-4, Anthropic, GPT-3.5 Turbo, Cohere, and AI21. Developers can run multiple LLM models over hundreds or thousands of iterations on specific tasks and obtain assessment results in seconds. Deepmark AI reports on metrics including question answering accuracy, text classification accuracy, PII recognition accuracy, named entity recognition accuracy, summarization quality (relevance), sentiment analysis accuracy, cost analysis, failure rate, accuracy, and latency. Its
key features
cover reliability assessment, accuracy evaluation, cost analysis, relevance assessment, latency assessment, and failure rate assessment, each described in the repository as tools for developers to evaluate model behavior under various conditions and to track failure rates at various scales.
Features
- AI-powered workflow
- Productivity support
- Online tool access
Tags
Use Cases
Generative AI builders use Deepmark AI to compare large language models against task-specific metrics on their own data.
Developers use Deepmark AI to run multiple LLM models over hundreds or thousands of iterations of a task such as question-answering or sentiment analysis.
Teams use Deepmark AI to assess GenAI performance metrics such as question answering accuracy, text classification accuracy, PII recognition accuracy, named entity recognition (NER) accuracy, summarization quality (Relevance), and sentiment analysis accuracy.
Teams use Deepmark AI to estimate the cost of running their AI applications on different GenAI models.
Developers use Deepmark AI to evaluate latency and identify inefficiencies in generative AI APIs.
Teams use Deepmark AI to track failure rates across hundreds to thousands of requests.
Developers use Deepmark AI to compare generated outputs against desired criteria for relevance assessment.
Teams use Deepmark AI to evaluate model reliability under various conditions and capture potential failure points.
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