Einstein services that solve real problems.
Einstein expertise that comes from the data side.
What clients ask before they engage
Often paired with Einstein Consulting
Ready to make Einstein predictions trustworthy?
Einstein Discovery model design
Prediction Builder for sales and service
Einstein Bots conversation design
Model evaluation and calibration
Einstein + warehouse comparison
Tableau CRM (Einstein Analytics) reporting
Evaluation is the bar
Honest about Einstein vs warehouse models
Tied to outcomes, not AUC
Senior delivery, not junior staffing
Start here when
Consider another path when
The person who owns this is not always the person who signs.
Einstein is the predictive layer (scoring, classification, language). Agentforce is the agentic layer (multi-step actions inside topics). They're complementary, not the same product. We help you decide which fits each use case.
Depends on the use case. Einstein is fast for predictions tied to standard Salesforce objects. Custom warehouse models win for cross-system features and where calibration matters. We've done both. We'll recommend the better fit.
Yes. Every Einstein model we deploy has a documented evaluation, a monitoring dashboard, and an alert when accuracy drifts.
A bounded model deployment with evaluation runs 8 to 12 weeks. A multi-model rollout with monitoring typically runs 14 to 20 weeks.
We design Einstein Discovery models with clean training data, evaluation splits, and calibrated outputs that are usable in business decisions.
We design bot conversations bounded to defined topics, with handoff rules and a regression suite. Note: for full agentic behavior, see our Agentforce service.
We build the evaluation harness so every Einstein model in production has a documented accuracy, calibration, and drift profile.
When the Einstein model isn't the best option, we build the equivalent in the warehouse with dbt and Python. We'll tell you which is better.
We build CRM Analytics dashboards on top of Einstein outputs so the predictions show up where the user is making the decision.
Most Einstein deployments lack evaluation. Without evaluation, the models are vibes. We won't ship without it.
Sometimes the Einstein feature is the right call. Sometimes a custom model in dbt + Python is better. We'll tell you which, with the test results.
We measure model business impact, not just statistical accuracy.
Agentic layer for multi-step actions, complementary to Einstein predictions.
Assess whether your Salesforce data is ready for predictive AI.
Einstein consulting: Einstein is the predictive AI layer Salesforce ships across products. Most teams...
A black-box AI pitch. Every model is documented, evaluated, and reviewable.
Einstein predictions are only as good as the data behind them. These are the factors that move the effort.
Einstein models need clean, enough data; gaps need fixing first.
Tracking model quality and access is part of doing it right.
You run Salesforce and want Einstein predictions on trustworthy data.
Predictions underperform because the underlying data is thin or messy.
You want AI agents that act, not just predict: see Salesforce Agentforce Consulting.
Your data isn't unified: see Salesforce Data Cloud Consulting.
You want a decision model outside Salesforce: see Decision Support Systems.
Einstein is the predictive AI layer Salesforce ships across products. Most teams turn it on, see numbers that look plausible, and never evaluate whether the predictions are right. We change that. Einstein gets evaluated, calibrated, and tied to outcomes.
Salesforce Einstein consulting makes Einstein's predictions hold up to evaluation. Most teams turn Einstein on, see plausible-looking numbers, and never check whether the predictions are right. Thinklytics evaluates, calibrates, and ties Einstein output to real outcomes, so the predictive AI layer earns trust instead of quietly misleading the sales and service teams that rely on it.
Start with an Analytics Truth Audit. We review the Einstein features in use, evaluate them against a holdout, and give you a prioritized fix and rebuild list.
100%: Einstein models with documented evaluation.
0: Models in production without monitoring.
1: Calibrated score per use case.
The people who normally own this problem are CIO, IT director, COO. They are the ones who start the search, and they are not always the person who signs for the work.
A buyer usually describes the situation this way: Someone re-keys the same record into two systems, and approvals move by email.
The spend is normally gated by the CFO or executive sponsor, whose question is: Is the improvement worth the investment? What that person needs to approve it is a business case with stated assumptions and measurable outcomes.