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Every Einstein developer we place understands that a prediction is only as good as the data behind it, so they start with the data, not the model. With Einstein Prediction Builder they frame the right binary or numeric prediction question, select the object and example records, choose fields with genuine signal while excluding leakage and proxy fields, and review the model scorecard accuracy, the quality metrics, and the top predictors before deploying the prediction field. With Einstein Discovery (now in Data Cloud and CRM Analytics) they build story models that surface the drivers of an outcome, expose what-if and improve-on insights, and write predictions and recommendations back to records. They monitor model drift over time and retrain when accuracy degrades. The PM confirms your use case and data readiness so the model is set up to actually move a metric.

Yes. Next Best Action is where prediction turns into action, and our developers build it end to end. They define recommendations as records, then build strategies in Strategy Builder that load candidate recommendations, filter them by eligibility, branch on business rules, and prioritize them often using an Einstein prediction or Discovery model as the ranking input so the top recommendation is data-driven, not just rule-based. They surface the recommendations on Lightning record pages, in flows, in the service console for agents, or in communities for self-service, and they capture accept and reject feedback so the strategy improves. They handle the governor and performance considerations of evaluating strategies in real time. The PM confirms where you want recommendations to appear and what decisions they should drive before the strategy is built.

Our developers build full CRM Analytics solutions dataflows and recipes (Data Prep) to ingest and transform data from Salesforce objects and external sources, computed and security predicates for row-level sharing, and dashboards built in the dashboard designer with bindings, faceting, and SAQL or SQL queries for advanced logic. They design lenses and dashboards that actually drive decisions rather than vanity charts, embed analytics into Lightning pages and communities, and use Einstein Discovery within CRM Analytics to add predictions directly to dashboards. They tune performance for large datasets, schedule data syncs, and manage the analytics security model so users only see their data. They also integrate with Tableau where the broader Tableau platform is in play. The PM confirms your data volumes and audience so the solution scales.

Generative AI in Salesforce only works when it is grounded in trusted data, and our developers know how to do that safely. They configure prompt templates in Prompt Builder, ground them with merge fields, related lists, flows, and critically Data Cloud retrieval so the model reasons over your real customer data rather than hallucinating. They build and extend Einstein Copilot actions, wiring standard and custom actions (flows, Apex, prompt templates) so the assistant can take real action, and they apply the Einstein Trust Layer data masking, zero-retention, toxicity filtering, and audit so prompts and responses stay compliant. They understand how the Atlas reasoning engine plans and selects actions. The PM confirms your data governance and which generative use cases are in scope before any model touches customer data.

Deploying a model is the start, not the end, and our developers treat it that way. They establish a baseline before launch what the metric was without the model and run a holdout or A/B comparison so the lift is provable, not assumed. They monitor the model scorecard and prediction distribution for drift, watching for shifts in input data, seasonality, or business changes that erode accuracy, and they retrain on fresh data on a schedule. They check for bias and ensure sensitive fields are excluded, validate that predictions are actually being acted on (a great model nobody uses changes nothing), and instrument the downstream metric so leadership sees the business impact. They document the model assumptions so it stays maintainable after handover. The PM confirms your success metric up front so accuracy is measured against the thing that matters.