RESOURCES

Case Studies

Reference solution designs, not slideware.

These are worked solution designs for applying AI inside Salesforce — the data model, the architecture and the guardrails, not just the pitch. They're patterns we've designed and can adapt to your org, rather than results from a specific named client.

Sales Optimization

Lead Scoring & Prioritization

Challenge

Sales teams were spending time on every lead and opportunity equally, with no data-driven way to know which ones were actually worth prioritizing — hurting both conversion rates and rep productivity.

Solution

An automated scoring pipeline inside Salesforce: standard CRM fields (lead source, industry, company size, engagement recency, deal stage, forecast category and more) feed a predictive model — Einstein Discovery natively, or an external model via Apex callout — that scores every lead and opportunity from 0–100, engineering features like days since last activity and opportunity age along the way.

Outcome

Reps get a prioritized, dashboard-driven view with a filtered list of high-priority records (score > 70), turning a gut-feel triage process into a repeatable, data-driven one.

SalesforceEinstein DiscoveryPredictive ScoringApex
Sales Optimization

Opportunity Win Prediction

Challenge

Forecasting which deals would actually close was based on rep intuition and pipeline stage alone, with no systematic signal for where to focus effort to improve win rate.

Solution

A binary classification model trained on three years of closed-won/closed-lost opportunity history using Salesforce's native Einstein Prediction Builder — standard fields only, no custom data pipeline required. The model outputs an Opportunity Win Score (0–100) and surfaces the top predictive factors behind each score: engagement level, deal size, time-to-close and buying power.

Outcome

Sales leaders get an early, quantified signal on deal health, and reps get next-best-action guidance instead of a static probability field.

SalesforceEinstein Prediction BuilderMachine LearningSales Forecasting
Sales Optimization

Dynamic Pricing Recommendations

Challenge

Reps had no consistent way to know how much discount they could offer on a quote line item without either leaving margin on the table or over-discounting to close a deal.

Solution

A prediction service, callable in real time from the Quote Line Item, recommends a discount or target price using Einstein Discovery trained on historical closed deals — with custom guardrails (maximum discount per product family, minimum margin thresholds, competitor pricing rules) enforced alongside the model. Recommendations, confidence scores and reasoning surface directly in a Lightning Web Component, with reps able to override.

Outcome

Pricing guidance that protects margin while still giving reps room to close, with every recommendation explainable rather than a black box.

SalesforceEinstein DiscoveryLightning Web ComponentsPricing Strategy

Want a solution design like this for your org?

These patterns adapt to your data and your Salesforce instance. Let's talk about the highest-value place to start.

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