Einstein is the predictive layer (scoring, classification, language). Agentforce is the agentic layer (multi-step actions inside topics). They are 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 have done both. We will 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 is not the best option, we build the equivalent in the warehouse with dbt and Python. We will 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 will not ship without it.
Sometimes the Einstein feature is the right call. Sometimes a custom model in dbt + Python is better. We will 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, sufficient 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 is not 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.