Akkio

Start with structured tabular data - clean errors, standardize categories, and remove leakage columns before uploading

Focus on core strengths: lead scoring, churn prediction, sales forecasting, and classification problems

Explicitly choose target column and whether it is classification or regression - define what success means in business terms

Hold out data for out-of-time validation (e.g., last quarter) for forecasting models to catch overfitting

Take advantage of free training time by building many model variations with different feature sets and time windows

Deploy via native integrations (Salesforce, HubSpot, Snowflake) to embed predictions directly where teams work

Assign an AI owner per use case responsible for monitoring drift, retraining cadence, and documentation