Client Details
A small but rapidly growing SaaS startup delivering analytics and productivity tools to mid-market enterprises. With strong initial traction, the company aimed to embed AI capabilities into its platform to differentiate from competitors.
Engagement Duration
6 months | 5 Engineers (2 AI/ML Engineers, 3 Data Engineers)
The Challenge
The startup lacked in-house expertise in AI/ML and cloud-scale data engineering. Their product roadmap required developing recommendation systems and predictive insights, but hiring top AI talent on short notice was proving expensive and slow. Moreover, they needed engineers who could also understand product nuances and user behaviors.
Our Approach
We assembled a compact, high-impact team:
- 2 AI/ML engineers with experience in predictive modeling, NLP, and recommendation systems
- 3 data engineers with expertise in Azure Data Lake, Databricks, and Python pipelines
Our team worked closely with the client’s product and engineering leads, translating user requirements into machine learning models and real-time pipelines.
Key Contributions
- Developed a personalized recommendation engine that improved feature adoption by 25%.
- Built data pipelines to process behavioural event data in near real-time using ADF and Databricks.
- Applied clustering and anomaly detection techniques to enhance user segmentation and reporting.
The Impact
- Accelerated AI/ML module delivery by 3 months, allowing the client to launch ahead of schedule.
- Boosted end-user engagement and NPS scores through intelligent insights.
- Improved internal team focus by offloading complex data science work to our experts.
