AI Based Recommendation
We build recommendation engines that learn from real user behavior — not just static rules — to surface the right product, article, or next action for each user, integrated directly into your existing app or site.
0%
Avg. Engagement Lift
5-9 wks
Typical Engagement
Built-in
Cold-Start Handling
Yes
Real-Time Personalization
What's Included
Recommendation engine integrated into your product
A/B test results measuring engagement lift
Cold-start strategy for new users/items
How We Work
01
Behavior Data Audit
We review your existing user interaction data to assess what signals are available for personalization.
02
Model Selection
We choose a collaborative filtering, content-based, or hybrid approach matched to your data volume.
03
Integration
We integrate the recommendation engine directly into your product or site.
04
A/B Testing
We measure lift against your current experience before full rollout.
What You'll Receive
- Recommendation engine integrated into your product
- A/B test results measuring engagement lift
- Cold-start strategy for new users/items
- Real-time personalization pipeline
- Monitoring dashboard for recommendation performance
How Engagements Typically Work
Every project starts with scoping — here's the shape most engagements for this service take.
Pilot Build
A 3-4 week build tested via A/B experiment against your current experience.
Production Rollout
Full integration and rollout across your product — typically 5-9 weeks.
Ongoing Optimization
Monthly retainer to retrain and tune as user behavior and catalog evolve.
Technologies We Use
The tools and platforms our AI Based Recommendation team works in day to day.
Modeling
Infra
Frequently Asked Questions
We use a cold-start strategy — content-based or rule-informed recommendations — until enough behavioral data accumulates to personalize fully.
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