AI Outputs Are Unpredictable. Eval Framework Brings Clarity.
Request a DemoWhy AI Quality Monitoring Matters
Irrelevant Responses
AI may provide answers that don’t solve the user’s problem.
Fabricated Content
AI can generate convincing but false or misleading information.
Inconsistent References
Answers might be correct, but linked sources or references may not match.
Incorrect Tool Sequences
Agents must trigger tools in the correct order for reliable outputs.
High Latency
Slow response times reduce user satisfaction and retention.
Improper Use Cases
Using the solution outside its intended scope (e.g., customer support bot used for coding) can cause issues.
Bias Risk
Despite safeguards, biased or inappropriate content may still appear.
Sensitive Data Exposure
If connected to internal systems, AI could reveal restricted information to unauthorized users.
Our AI Evaluation Services
Pre-Production Evaluation
Comprehensive assessment of AI models to ensure accuracy, reliability, and safety. This includes bias detection, hallucination mitigation, and the development of tailored evaluation metrics to align AI behavior with expectations. Our approach ensures AI systems perform optimally before deployment.
Run-Time Evaluation
Design and integration of guardrails to enhance the safety, security, and compliance of AI solutions. This includes real-time monitoring, automated risk detection, ethical AI enforcement, and mitigation strategies for prompt injection, adversarial attacks, and unauthorized model behaviors.
Red Teaming
A structured approach to testing AI security and robustness by simulating adversarial threats. We analyze AI systems within their operational context, identifying vulnerabilities and weaknesses. Our evaluations help mitigate risks and improve AI resilience against real-world attacks.
Why Us
Comprehensive Evaluation & Guardrails Management
Our intuitive UI streamlines the evaluation process and guardrail configuration, ensuring AI systems meet compliance, security, and performance standards with ease. Easily configure, test, and adjust AI safety guardrails through an intuitive interface, ensuring compliance and risk mitigation with minimal effort.
Flexible Data Handling & Customization
Effortlessly upload datasets in XLS format or generate synthetic data from scratch or existing documents.
Select from predefined evaluation metrics—including conversational AI—or define your own for tailored assessments.
Seamless AI Integration & Red Teaming
Connect to any AI API endpoint, including ARGO and RAG, ensuring compatibility across diverse AI architectures. Identify vulnerabilities proactively with integrated Red Teaming capabilities.
Actionable Insights
Gain visibility into key AI metrics, such as token usage per request, latency, and evaluation cost.
Download detailed reports and visualized results directly from the UI for informed decision-making.
Our Evaluation Process
Define evaluation metrics
Based on the requirements, our team will prepare a variety of metrics for the AI solution
Dataset preparation
Leverage datasets created by AI QA Engineer and LLM that encompass specific domain knowledge, biases, and more to evaluate different facets of Gen AI solution.
Integrate AI Solution with Eval Framework
To facilitate evaluation, the prepared dataset will be integrated with our evaluation framework, allowing for the automatic calculation of metrics.
Analyse Deviations and Repeat Step 3
Based on the results, the AI QA engineer will analyze deviations from the metrics using AI responses and context to identify bottlenecks in the AI-based solution.
Available Metrics
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Answer Relevancy
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Faithfulness
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Contextual Relevancy
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Contextual Precision
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Contextual Recall
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Hallucination
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Toxicity
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Ragas
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G-Eval
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Bias
Also we can use:
Prompt Alignment
Measures whether your LLM application is able to generate actual outputs that aligns with any instructions specified in your prompt template.
Tool Correctness
Assesses your LLM agent’s function/tool calling ability.
Task Completion
Evaluates how effectively an LLM agent accomplishes a task as outlined in the input, based on tools called and the actual output of the agent.
Role Adherence
A conversational metric that determines whether your LLM chatbot is able to adhere to its given role throughout a conversation.
Knowledge Retention
A conversational metric that determines whether your LLM chatbot is able to retain factual information presented throughout a conversation.
Get Started Today
Don’t leave your AI solutions to chance. Ensure they meet the highest standards of quality and reliability.
Experience firsthand how Eval Framework can elevate your AI initiatives.