AI Engineering
Moving AI from notebooks to production systems that run reliably at scale.
The gap between prototype and production is where most AI initiatives die. A working Jupyter notebook is not a production system. We build production-grade AI systems that run reliably at scale, handle edge cases, monitor performance, and integrate into your existing infrastructure. Our engineering approach prioritizes operational excellence, maintainability, and measurable business impact over cutting-edge experimentation.
AI Products
Production-grade AI, not demos
We build end-to-end AI products that deliver real business value in production. Not proof-of-concepts or demos—fully productionized systems with proper error handling, monitoring, testing, and operational processes. Whether it's a recommendation engine, forecasting system, or intelligent automation, we deliver systems that work reliably at scale.
Our Approach
We start by deeply understanding the business problem and success metrics. Then we design the system architecture considering data pipelines, model training/serving infrastructure, monitoring, and integration points. We build iteratively, getting working systems into production quickly, then improving based on real-world performance data. Every system includes comprehensive testing, monitoring, and documentation.
What You Get
A fully deployed AI system running in production with performance monitoring dashboards, comprehensive documentation including architecture, operations runbooks, and API specs, automated testing and CI/CD pipelines, and a trained operations team ready to maintain the system. You'll have a production AI system that delivers measurable business value from day one.
Timeline
8-16 weeks depending on complexity and scope
Data Modernization
Build the foundation for AI
AI is only as good as the data it's built on. We modernize data infrastructure to create the foundation for successful AI initiatives. This means consolidating fragmented data sources, establishing data quality processes, building scalable data pipelines, and creating governed data platforms that enable both AI development and broader analytics use cases.
Our Approach
We assess your current data landscape to understand sources, quality issues, and access patterns. Then we design a modern data architecture that consolidates key data sources, establishes quality controls, and enables both batch and real-time processing. We implement incrementally, prioritizing data sources that enable the highest-value AI use cases while establishing patterns and processes that scale to your full data estate.
What You Get
A unified data platform providing clean, accessible data for AI and analytics, automated data pipelines with quality monitoring and alerting, data governance framework including cataloging, lineage tracking, and access controls, and self-service data access for data scientists and analysts. You'll have reliable, high-quality data that enables AI development and broader data-driven decision making.
Timeline
8-20 weeks depending on data complexity and scope
MLOps
Keep AI running reliably
Getting a model into production is just the beginning. Models degrade, data drifts, systems fail. MLOps is about building the infrastructure and processes to keep AI systems running reliably—continuous monitoring, automated retraining, version control, testing, and incident response. We implement MLOps platforms and practices that ensure your AI systems stay healthy and deliver consistent value.
Our Approach
We design MLOps infrastructure tailored to your technology stack and operational maturity. This includes model registry and versioning, automated training and deployment pipelines, comprehensive monitoring (data drift, model performance, system health), and incident response processes. We implement incrementally, starting with critical systems and establishing patterns that scale across your AI portfolio.
What You Get
A production ML platform with model registry, experiment tracking, and automated deployment pipelines. Monitoring dashboards tracking model performance, data quality, and system health with automated alerting. CI/CD for ML including automated testing, validation, and deployment processes. Operational runbooks and incident response procedures. You'll have the infrastructure and processes to operate AI systems reliably at scale.
Timeline
6-12 weeks for platform setup, then ongoing operational support