Measure customer satisfaction and loyalty with easy-to-use NPS widgets. Tailored for ai & machine learning companies to address industry-specific challenges.
AI and ML platforms provide model training, data pipeline management, experiment tracking, and deployment tools. Users range from data scientists who think in code to business analysts who need no-code ML capabilities, requiring layered onboarding approaches.
ML platform complexity creates a steep learning curve even for experienced data scientists
Business users cannot leverage no-code ML features because the interface assumes technical knowledge
Experiment tracking and model versioning tools are underutilized, reducing reproducibility
Model deployment and monitoring dashboards are disconnected from the training workflow
Gather feedback directly within your app to understand how likely users are to recommend you.
High Response Rates
Automated Scheduling
Closed-Loop Feedback
Guide data scientists through connecting data sources, launching their first experiment, and deploying a model with the platform's managed infrastructure. Reduce the weeks-long setup phase so data scientists spend time on modeling, not infrastructure configuration.
Walk business users through data upload, automated feature engineering, model selection, and prediction interpretation without requiring any coding knowledge. Democratize ML by making the no-code builder genuinely accessible to non-technical users.
Help teams adopt experiment tracking, model versioning, A/B testing, and production monitoring as part of their standard workflow. Transform ad-hoc ML development into a repeatable, auditable process through guided MLOps practice adoption.
Time to first model deployment
Experiment tracking adoption rate
No-code ML feature utilization
Model monitoring dashboard engagement
Guided quickstart tours that walk data scientists through end-to-end model development, from data connection to deployment, using the platform's managed services eliminate the infrastructure setup phase. Scientists can deploy their first model in hours instead of weeks when the platform handles the DevOps complexity.
Yes, but only if the tours genuinely simplify the experience rather than explaining technical concepts. No-code ML tours should use business language (predictions, patterns, accuracy) instead of technical jargon (hyperparameters, cross-validation, feature importance). The tour must translate ML concepts into business decisions.
MLOps adoption requires changing habits, not just teaching features. Contextual prompts that suggest experiment logging when a scientist starts a new run, or model versioning when they modify a model, integrate MLOps practices into the existing workflow rather than requiring a separate process.
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