Adding AI to Your Existing Product
Embedded ML engineers into an enterprise SaaS team to build AI features — from recommendation engines to predictive analytics — without disrupting existing development.
AI recommendation feature shipped
In user engagement with recommended content
Client team can maintain the system
Existing sprint velocity maintained
AI recommendation feature shipped
In user engagement with recommended content
Client team can maintain the system
Existing sprint velocity maintained
// The Challenge
What We Were Solving
An enterprise SaaS company wanted to add AI-powered recommendations to their platform but their engineering team had no ML expertise. Hiring AI engineers would take 6+ months and they needed results for an upcoming product launch.
// Our Approach
How We Built It
Embedded 3 ML engineers alongside the client's existing product team
Conducted a 2-week AI architecture sprint to design the recommendation system
Built the feature using the client's existing tech stack, adding only the ML-specific tooling
Trained 2 of the client's engineers on ML concepts for long-term maintenance
// Key Features
What We Delivered
- Dedicated ML engineers embedded in your team
- Architecture design aligned with your tech stack
- Knowledge transfer to your existing engineers
- Incremental delivery in your existing sprint cadence
- Full documentation and runbooks
- Post-delivery support and optimization
// Technology Stack
Built With
// Related Service
Team Augmentation
Team Augmentation
Embed senior engineers into your team within days — developers who bring AI expertise, clean code standards, and real production experience. No learning curve, no overhead, no commitment.
Learn More// Results
Measurable Impact
AI recommendation feature shipped
In user engagement with recommended content
Client team can maintain the system
Existing sprint velocity maintained
// Related Use Cases
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// Build Something Similar
Ready to Get Started?
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