From start to finish, we help you build trustworthy AI
Evaluation Platform
Datumo Eval
Ideal for anyone looking to validate and monitor custom workflows with automation.
Auto-generate evaluation data with powerful AI agents
Auto-generate evaluation data with powerful AI agents
We generate realistic, high-quality evaluation questions using your policy and product documents. Questions are tailored for reliability, factual accuracy, and other key LLM benchmarks.
Generate practical, field-driven data with smart automation
Generate practical, field-driven data with smart automation
We generate realistic evaluation questions grounded in real-world business scenarios and practical use cases.
Thorough evaluation based on tailored metrics
Thorough evaluation based on tailored metrics
Evaluate with built-in or fully customized metrics—complete with reasoning for every response.
Dashboard-driven validation insights
Dashboard-driven validation insights
See metric-level scores, model comparisons, and key results at a glance.
AI Red Teaming, Automated and Visualized
AI Red Teaming, Automated and Visualized
No waiting. Launch targeted AI red teaming anytime, with results visualized for fast vulnerability detection.
Basic
- Single-chunk–based question generation
Safety Evaluation Data
- Prebuilt set of 1,000 safety prompts
Singleton Auto-Eval
-
RAG Quality Evaluation via Text Decomposition (w/o Reference answer)
- Safety Evaluation
- Custom Prompts
Scoring Dashboard
- Model & metric-level comparison
- Heatmap analysis by metadata attributes
Standard
All Basic Features
- Single-chunk–based question generation
- Safety Evaluation Data
- Singleton Auto-Eval
- Scoring Dashboard
Multi-Chunk–Based Eval Question
* In Development
- Chunk Selection for Gen
- Multi-Chunk Question Generation
Singleton Auto-Eval
-
RAG Quality Evaluation via Text Decomposition
(w/ Reference answer)
Add-on
Red Teaming
Human Red Teaming
- Red Teaming Playbook
- Manual Red Teaming
Automated Safety Red Teaming
- Seed data upload
- Red Team Prompt Generation
- Outcome Evaluation
- Prompt re-generation
Basic
- Single-chunk based question generation
Safety Evaluation Data
- Prebuilt set of 1,000 safety prompts
Singleton Auto-Eval
- RAG quality evaluation via text decomposition (w/o Reference answer)
- Safety evaluation
- Custom prompts
Scoring Dashboard
- Model & metric-level comparison
- Heatmap analysis by metadata attributes
Standard
All Basic Features
- Single-chunk based question generation
- Safety evaluation data
- Singleton auto-eval
- Scoring dashboard
Multi-Chunk–Based Eval Question
* In Development
- Chunk selection for generation
- Multi-chunk question generation
Singleton Auto-Eval
- RAG quality evaluation via text decomposition (w/ Reference answer)
Add-on
Red Teaming
Human Red Teaming
- Red Teaming Playbook
- Manual Red Teaming
Automated Safety Red Teaming
- Seed data upload
- Red Team Prompt Generation
- Outcome Evaluation
- Prompt re-generation
L Co.
• Chatbot Evaluation Setup for Real-World Customer Scenarios
• Evaluation Results: Score Comparison, Human Agreement, and Actionable Insights
K Co.
• Tailored Metrics for Assessing Customer-Facing RAG Systems
• Metric-Based Eval Dataset Creation with Peer Model Benchmarking Report
L Co.
• Safety Criteria for Customer LLM Chatbots in Q&A and Everyday Dialogues
• Custom-Metric-Based Evaluation and Benchmarking Against Similar Models
K Co.
• Developing Harmlessness Evaluation Sets Focused on Category Fit and Content Risk
S Co.
• Task-Specific Evaluation and Red Teaming Pipeline for Internal LLMs
• Custom Evaluation & Reliability Testing with Client Data
LLM Safety & Reliability Benchmark
• First-Ever Trustworthiness Criteria for Korean LLMs
• Under the AI Training Data Support Initiative, model performance is quantitatively evaluated using the 3H (Helpfulness, Honesty, Harmlessness) framework.
* 3H: A framework for developing AI systems that are Helpful, Honest, and Harmless.
LLM Alignment Benchmark for Korean Social Values and Common Knowledge
First Korean-Centric LLM Evaluation Dataset
• Korean Social Values & Common Sense Benchmark for LLMs
• Developed from a combination of large-scale public opinion data and authoritative Korean educational content.
L Co.
• Chatbot Evaluation Setup for Real-World Customer Scenarios
• Evaluation Results: Score Comparison, Human Agreement, and Actionable Insights
K Co.
• Tailored Metrics for Assessing Customer-Facing RAG Systems
• Metric-Based Eval Dataset Creation with Peer Model Benchmarking Report
L Co.
• Safety Criteria for Customer LLM Chatbots in Q&A and Everyday Dialogues
• Custom-Metric-Based Evaluation and Benchmarking Against Similar Models
K Co.
• Developing Harmlessness Evaluation Sets Focused on Category Fit and Content Risk
S Co.
• Task-Specific Evaluation and Red Teaming Pipeline for Internal LLMs
• Custom Evaluation & Reliability Testing with Client Data
LLM Safety & Reliability Benchmark
• First-Ever Trustworthiness Criteria for Korean LLMs
• Under the AI Training Data Support Initiative, model performance is quantitatively evaluated using the 3H (Helpfulness, Honesty, Harmlessness) framework.
*3H: A framework for developing AI systems that are Helpful, Honest, and Harmless.
First Korean-Centric LLM Evaluation Dataset
LLM Alignment Benchmark for Korean Social Values and Common Knowledge
• Korean Social Values & Common Sense Benchmark for LLMs
• Developed from a combination of large-scale public opinion data and authoritative Korean educational content.
LLM Evaluation
Enhance the performance of your LLM-based services with Datumo Eval. Create questions tailored to your industry and intent, and systematically analyze model performance using custom metrics.