An AI recommendation engine guided B2B reps toward upsell, cross-sell, and long-tail SKUs in real time.
- Deployment
- Beta
- Long-tail utilization
- Targeted
- Upsell and cross-sell
- Targeted
What we shipped
A recommendation platform embedded in the sales workflow that evaluates client order history, co-purchase patterns, and strategic priorities.
B2B distributors and wholesalers manage thousands of SKUs across complex client portfolios. Sales reps rely on memorized product knowledge or habitual order patterns, which pushes a small set of familiar SKUs and leaves many strategically important products underrepresented.
Blueprint → AI Pilot → Production launch → Scale and operate.
We followed the Datablooz Delivery Model. See our process.
- Blueprint
Analyzed historical orders, catalog structure, client profiles, and corporate sales targets to define recommendation objectives.
- AI Pilot
Built a machine-learning recommendation engine on order history and co-purchase patterns, validated against rep judgment.
- Production launch
Integrated real-time recommendations into the sales workflow for rep-in-meeting use with target alignment logic.
- Scale and operate
Extended to demand forecasting, automated quoting, and customer lifetime value scoring across accounts.
Business, technical, and governance outcomes.
- In-meeting product suggestions aligned with targets.
- Upsell and cross-sell guidance from real behavior.
- Long-tail utilization raised above habitual patterns.
- Foundation for predictive commercial operations.
- Python
- Scikit-learn
- FastAPI
- PostgreSQL
- Airflow
- Docker
Recommendation rules reviewed by commercial leadership, with target weighting configurable per strategy cycle.
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Reference calls available under NDA after the second working session.