Multi-objective prompt optimisation for enterprise LLM deployments.
Automatically identify the optimal cost-accuracy trade-offs — without endless manual tuning.
Every LLM deployment involves a trade-off: you can optimise for cost, accuracy, latency, or all three — but they pull in different directions. EigenPrompt maps your full Pareto frontier automatically, so you choose the operating point that matches your business constraints.
Connect your deployment, define your evaluation criteria, and EigenPrompt's optimisation engine does the rest — running thousands of prompt variants, scoring outputs, and surfacing the configurations that beat your current baseline.
Automatically generates the complete cost-accuracy-latency trade-off surface for your deployment.
Re-runs automatically when model versions update or your usage patterns shift significantly.
SOC 2 Type II compliant. Full audit logging, RBAC, and private cloud deployment options.
Works across GPT-4, Claude 3.5+, Gemini, Llama 3, Mistral, and custom fine-tuned models.
Real-time token cost tracking, forecasting, and spend attribution across teams and projects.
Define your own success criteria — factual accuracy, tone, format compliance, or business KPIs.