Specialized Technical Expertise
Mastery in productionizing and scaling LLMs to turn AI from experiment into business value.
FlavorHawk Case
85% More Launch Accuracy: The FlavorHawk Case
How Zallpy revolutionized personalization at a high-growth Food Tech company through cloud data engineering and predictive artificial intelligence
Predictive Intelligence for a Growing Food Tech
FlavorHawk is a high-growth Food Tech company that processes millions of interactions and complex behavioral data every day, competing in a market where anticipating consumption trends defines who leads.
Turning this massive flow of unstructured data into real-time commercial intelligence required a modern predictive architecture, capable of sustaining critical decisions without compromising performance or corporate security — that was the starting point of the partnership with Zallpy.
AI Engineering in Production
Building the technical and data foundation needed to sustain agile product launches at global scale.
Cloud Infrastructure & Resilience
Ecosystem built on AWS Lambda, VPC and Terraform to ensure uninterrupted scalability.
Unified Data Management
Centralized integration between Snowflake and AWS Kinesis for the continuous flow of internal and external data.
Generative & Predictive Intelligence
Applying Llama3, Stable Diffusion and Amazon SageMaker to decode consumption preferences with high accuracy.
Strategic Visualization
Intuitive dashboards that empower leadership with real-time AI insights.
The Challenge: Real-Time Intelligence for Critical Decisions
As a high-growth Food Tech company processing millions of interactions and complex behavioral data daily, FlavorHawk faced the critical challenge of turning massive flows of unstructured data into real-time commercial intelligence to lead the market.
The lack of a modern predictive architecture created operational vulnerabilities and limited the ability to anticipate global consumption trends without compromising performance or corporate security.
Strategic Focus Areas
A solid infrastructure for agile data.
Highly flexible cloud-native architecture based on AWS and Terraform.
Results
Operational Agility 40% faster new product launches thanks to automated trend detection.
Market Precision Over 85% accuracy in forecasting consumer preferences and behavior.
Processing Efficiency Drastic reduction of data loss from 20% to 1% with LLM-optimized flows.
Reliable Infrastructure Total stability during critical peaks of global demand.
Development With Far More Involvement
Global Synergy & Agility
Operating in a US-compatible time zone for seamless real-time collaboration.
Proactive Delivery Culture
A direct extension of the internal team with absolute focus on cultural alignment and technical excellence.
Related Cases
Case Bunge: +60% freight forecast accuracy with Machine Learning. Learn more
Case BMW: Managing 12M+ active users across dozens of countries. Learn more
Case Bunge: +60% freight forecast accuracy with Machine Learning. Learn more
Case BMW: Managing 12M+ active users across dozens of countries. Learn more
