Growth Transformation Case Studies

How we help growth companies scale their data and AI capabilities through Series A/B and beyond.

Public E-Commerce Company

4X User Engagement

E-commerce

Personalization

Real-time ML

Business Challenge

Nasdaq-listed company: User engagement for Customer Brand Products limited by poor matching of listings to brand certified information - limiting revenue growth.

Our Solution

Developed a real-time ML platform on Snowflake & Kubernetes, using Claude via Bedrock for advanced recommendations and LanceDB for semantic search, replacing a slow Redshift-based batch system.

Key Technologies

Snowflake data warehouse
DBT for transformations
Fivetran for data import
Real-time ML platform on K8s (AWS)

Timeline:

6 months (2 months architecture, 4 months implementation)

Innovation:

First implementation of Claude-powered recommendation system in their vertical.

Results & Impact

4x increase in user engagement metrics
Real-time recommendations serving ~1M requests/day
Platform scales to support 5x more users without infrastructure changes

Series A Healthcare Startup

75% Reduced Cost per Pilot

Healthcare

Value-Based Care

Data Platform

Business Challenge

New client acquisition in value-based care space requires advanced data prototypes.

Our Solution

Deployed Snowflake ecosystem with AWS integration for rapid data ingestion and Streamlit processing for accurate dynamic reports.

Key Technologies

Snowflake/AWS/Terraform
Streamlit with SQL and Python
Transparent and Modular ML Workflows
Repeatable deployment with Terraform

Timeline:

3 months (1 month scoping, 1 month deployment, 1 month extensibility engineering)

Innovation:

Modular ML workflow design for healthcare data

Results & Impact

New client acquisition critical to next fundraising
75% reduced cost per pilot for future clients
Internal team upskilling for future data work

Private Pharmaceutical Company

60% Ops Cost Reduction

Pharma

Operational Scaling

Team Building

Business Challenge

Small internal team was a bottleneck preventing customer growth and operational scaling.

Our Solution

Built a modern cloud-native architecture (Vercel/Supabase) with automated CI/CD and a customer self-service portal. Established a 4-person offshore development team in Uruguay.

Key Technologies

Vercel/Supabase cloud architecture
Automated CI/CD pipelines
Customer self-service web app
Automated data processing

Timeline:

9 months (1 month team building, 8 months development)

Success:

ZaroData continues to partner providing long term solutions and staffing

Results & Impact

60% reduction in manual operational work
Customer self-service portal reducing support requests by 40%
Internal team freed for growth initiatives
Platform supports >2x customers/vendors

Non-Profit (rapid scaling research organization)

51% Error Reduction

Non-Profit

AI Research

RAG

Business Challenge

Research scientists and internal AI agents couldn't efficiently access knowledge from millions of scientific documents.

Our Solution

Implemented an Agentic RAG system with advanced LLM integration for automated Q&A over millions of documents, deployed on GCP with a global researcher interface.

Key Technologies

Agentic RAG system
Advanced LLM integration (Q&A)
Automated document processing pipeline
One-line deployment (GCP, containers)

Timeline:

8 months (2 months research/planning, 6 months implementation)

Recognition:

System featured in leading AI research conferences.

Results & Impact

51% error reduction vs. SOTA systems
Query millions of documents in seconds
Automated Q&A accelerating discovery
Global researcher access improving collaboration

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