Accelerators and Experiments

Rapid prototypes and pilot projects to test decision science in your context

Guardrails & Control

Experiment Focus
Ensuring AI behavior remains aligned, bounded, and safe in decision-critical contexts.

Core Questions

How can AI outputs be constrained without losing decision usefulness?
Where do failure modes emerge under ambiguity or edge cases?
When should human intervention or override be triggered?

What This Enables

Safer deployment of AI in high-stakes decision environments
Clear rules for trust, escalation, and accountability
Reduced risk of unintended or non-compliant AI behavior

Prompt Optimization

Experiment Focus
Improving the consistency, clarity, and decision relevance of AI outputs.

Core Questions

How does prompt structure influence decision quality and reliability?
What reduces output variance while preserving reasoning depth?
How can AI responses remain explainable and repeatable over time?

What This Enables

More reliable and predictable AI-assisted decisions
Reduced hallucination and response drift
Higher confidence in AI outputs used for decision support

Configurable Retrieval (RAG)

Experiment Focus
Grounding AI reasoning in the right information for the right decision context.

Core Questions

What information should inform which decisions?
How does retrieval scope affect accuracy, relevance, and trust?
How can context be adjusted without rebuilding systems?

What This Enables

Decision-specific, context-aware AI reasoning
Better alignment between data, context, and outcomes
Reduced reliance on generic or stale information