01
Custom evaluation frameworks
Translate domain requirements, edge cases, human preferences, latency constraints, and failure severity into reproducible evaluation harnesses.
Choose AI workflows with evidence.
StickFlux Labs builds rigorous, decision-ready evaluations for organizations comparing models, agents, and AI-enabled workflows under real operational constraints.
Start an evaluationSee the methodDomain-specificMetrics reflect your actual work rather than generic leaderboard tasks.
Statistically defensibleUncertainty, variance, and sensitivity stay visible in every recommendation.
Decision-orientedResults map directly to workflow choices, deployment risk, and operating cost.
Evaluation built around the decision.
Instead of forcing a business problem into a standard benchmark, StickFlux starts with the operational decision, identifies failure costs and success criteria, then constructs an evaluation that can discriminate among alternatives.
02
Statistical modeling
Quantify uncertainty, compare alternatives with appropriate tests, evaluate sensitivity, and distinguish robust performance gains from sampling noise.
03
Workflow optimization
Determine where language models, classical ML, deterministic software, retrieval, and human review belong to improve reliability without wasting compute or labor.
From question to evidence.
Every engagement follows a compact scientific loop, keeping assumptions explicit and preserving a trace from business requirement to measured result.
Define the consequential decision.
Specify users, workflows, costs of error, candidate systems, and the minimum evidence needed to choose.
Build the evaluation surface.
Create representative tasks, scoring methods, controls, baselines, and statistical comparisons tied to the real operating environment.
Turn results into an operating choice.
Identify the winning configuration, uncertainty bounds, failure modes, and the next experiment required before scale-up.
Make the next AI decision testable.
Bring a workflow, a shortlist of systems, or simply a decision that currently depends too much on demos and intuition.
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