Staff Augmentation
Deliverables
AI/ML Solutions
Solution Architecture
Product Strategy
Technologies
Kafka
Spark (Python)
FastAPI (Python)
Elasticsearch
AWS Redshift
The Customer
Appen is an Australian multinational company that provides high-quality data for machine learning and artificial intelligence applications. They offer various services such as data annotation, data collection, transcription, and translation to assist organizations in improving their AI systems and algorithms.
The Challenge
In the competitive landscape of data services for AI and ML, ensuring data integrity, client trust, and regulatory compliance is essential for maintaining a leadership position. Our challenge in this project involved refining the annotator registry process and implementing a robust anti-fraud measures to improve important business KPIs and reduce cloud infrastructure costs.
35millions
Records processed per hour
By integrating top-tier big data technology, we increased analyzed events and parameters while cutting processing time by 30%.
1million
In annual savings
Estimated right after the new feature’s release, solely from cloud infrastructure and engineering savings.
5+
Years partnership
Successful nearshore software development partnership spanning over 5 years with multiple projects delivered.
We collaborated with the client’s business areas to gain a better understanding of the data sources and information flow. We identified potential fraud events throughout the process and defined classification mechanisms.
“Understanding the business context and needs is crucial to propose the most suitable architecture. In this instance, the solution needed to incorporate real-time fraud detection and be capable of processing millions of records per hour.”
— Claudio Vasconcelos
Tech Lead @ Novatics
02. Co-creating alternative solutions and selecting the best strategy
We collaborated with the client to develop an architecture that could support the volume of data and meet other technical and functional requirements. Additionally, we researched, trained, tested, and selected the most suitable AI models for event classification.
03. Implementing the solution iteratively and incrementally
We delivered a machine learning-based qualification and prediction solution using Kafka and Spark that optimizes the anti-fraud process by delivering real-time alerts.
— Phoebe Liu
Data Scientist @ Appen
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Novatics is a consultancy firm specializing in software design and development. We support companies globally in delivering innovative projects on time and on budget.
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