Klewr
Design AI experiences users trust and enjoy.
Engagement Increase (AI Feature)
Model Error Rate Reduction
We translate complex model outputs into intuitive narratives, confidence cues and ethical safeguards users trust.
Usability studies reveal where AI helps versus hinders, informing tuning and guardrails.
We run demographic-based usability and outcome analysis to spot and mitigate potential bias early.
We embed explainability patterns and privacy microcopy to meet emerging AI regulations and user expectations.
A transparent look at our end-to-end process—from kickoff to delivery—so you know exactly what to expect at every stage.
We run: AI feasibility workshop · ethics canvasWe clarify: value prop · data sources · user benefit
We audit: data quality · privacy limits · model latencyWe plan: human-in-loop or fallback flows
We craft: explain-oriented flows · trust cues · error recoveryWe package: prompt library snippets · annotation guidelines
We test: task success · trust metrics · hallucination impactWe iterate: prompt tweaks · UI microcopy
We deliver: validated flows · prompt library · metric dashboardWe suggest: next-gen model swap plan · data feedback loop
AI adds uncertainty—latency, hallucinations, trust issues—so we design explanations, fallback paths, and guardrails to keep users confident.
We advise on model fit (GPT-4o, Claude, Llama 3, etc.), but final vendor choice is yours. We focus on UX flows and prompt-engineering best practice.
Yes—under NDA we can run prototypes against anonymised datasets to validate latency and output quality before full integration.
