Key challenges
As organizations adopt multiple LLMs across diverse use cases, selecting the most suitable model becomes increasingly difficult. Traditional benchmark-based evaluations often lack business relevance, fail to predict performance on new tasks, and ignore operational considerations such as cost and latency. Evaluating every model for every use case is expensive, slow, and difficult to scale.
Costly and time-consuming model evaluations
Benchmark results biased and context-blind
Managing growing LLM portfolios at scale
Balancing quality, cost, and latency
No predictive power on unseen tasks
The solution
Cognitive demand mapping
Assess task demands
Predict model fit
Estimate success rates
Profile and rank LLMs
Multi-criteria decision optimization
Rank candidate LLMs
Weighted scoring
Balance quality, cost and latency
Generate optimized recommendations
Preprocessing layer
ADeLe-16K+ queries
LLM-as-a-judge scoring
Per-model assessors
Profiles and metrics stored
Profile and rank LLMs
Service layer
19-dim demand vector
Success probability
PROMETHEE II ranking
Ranked list and insights
Generate optimized recommendations
Cost efficiency
Lower testing effort
Minimal marginal cost
Better resource utilization
Reduced evaluation expense
Decision speed
Minutes versus days
Faster recommendations
Accelerated deployment
Improved responsiveness
Continuously improve predictions using real-world usage data
Expand cognitive dimensions to enhance modeling accuracy
Enable automated routing across multiple LLMs
Improve recommendation precision and explainability
