Select and secure the best business-model fit
Seamlessly customize your model analysis and criteria – benchmarked by professionals – to pick the best model in the business, for your business.
Customizable model criteria
Make your AI model evaluation meet your needs. Tune your generic model parameters, or optimize further with HPO, through establishing temperature, tokens, JSON, and more criteria. Authenticate your results by cross-validating output definition with tailored and hyper-targeted AI-powered prompts.
Automation and human-led LLM evaluation
Our team sets up and runs evaluations using standardized metrics and helps with interpreting the results while taking into consideration various dynamics.
- Accuracy
- Reasoning Traceability
- Relevance
- Coherence
- Factuality
- Performance
- Efficiency
Insights-backed decision making
Let analyses drive your decisions. Access sharper comparisons between multiple multimodal, contextually-sound datasets. Our detailed breakdowns of these case-specific evaluation designs help you select the model best fitting to your performance needs, tailored to your goals and overarching strategy.
Big picture evaluation, granular information
Detailed multi-model comparisons and report on strengths, weaknesses, and areas for improvement
Big picture evaluation, granular information
Detailed multi-model comparisons and report on strengths, weaknesses, and areas for improvement
Frequently Asked Questions
Find out the essentials of the model selection process and how to make the best of your comparisons.
Why is model fitting important?
What is the model selection process?
What factors should I consider while selecting a LLM?
The primary factors you should account for when finding a model is to select one that:
- Aligns with business objectives
- Retains accuracy and quality with result benchmarking
- Ensures ease of use through tools and APIs and hardware requirements
Why compare using a multi-model approach?
What other factors could affect my model selection?
- Data complexity: While complex datasets require complex models, this could lead to overfitting
- Data quality: Inaccurate, irrelevant, or missing data could affect some models more than others, but these can be reduced with synthetic data and other measures.
- Interpretability: A "black box" model will reduce explainability in its decision-making workflow, which could subvert AI use guidelines.
- Efficiency: Practical limitations, from financial to computing availability, might exist—depending on your requirements and use cases.
Maximize business impact with the right model
Pick the model that empowers your business objectives – for now and the long term.
