What We Build With It
We engineer robust, scalable ML systems that drive real business value, not just impressive demos.
Real-Time Fraud Detection Engines
Systems that score millions of transactions per second with sub-millisecond latency, minimizing financial risk.
Personalized Recommendation Systems
For e-commerce and media platforms, dynamically updated and retrained for maximum engagement.
Automated Demand Forecasting Platforms
Systems that automatically ingest new data, retrain models, and provide accurate, up-to-date forecasts for optimal planning.
Computer Vision QA Systems
For manufacturing and logistics, processing video streams in real-time on the factory floor to identify defects and anomalies.
NLP-Powered Document Processing Pipelines
Systems that classify, extract information, and automate workflows for millions of unstructured documents.
Anomaly Detection for Industrial IoT
Monitoring sensor streams from complex machinery to identify early warning signs of failure and optimize maintenance cycles.
Why Our Approach Works
We apply rigorous engineering discipline to machine learning, ensuring your investments deliver measurable, sustained value.
ML Engineering Discipline
We apply rigorous software engineering principles: automated testing, CI/CD for models, Infrastructure as Code, and a relentless focus on production stability.
Resilience by Design
Models fail, data pipelines break. We build in robust error handling, automated recovery, and deep monitoring so failures are managed incidents, not catastrophes.
Full Reproducibility & Auditability
We version everything—code, data, features, and models—ensuring every prediction made in production is 100% reproducible and auditable.
Our Go-To Stack for ML Systems
We build production-grade ML systems using a modern MLOps stack that emphasizes automation, reproducibility, and continuous monitoring.
Core Frameworks
Scikit-learn, PyTorch, TensorFlow, XGBoost for diverse modeling needs.
Data & Feature Management
dbt (Data Build Tool) for transformations, Feast (Feature Store) for consistent feature serving.
Experiment Tracking
MLflow, Weights & Biases for managing and comparing model experiments.
ML Orchestration
Kubeflow, Prefect, Dagster for automating complex ML workflows.
Model Deployment & Serving
FastAPI, KServe, Seldon Core, NVIDIA Triton for scalable, low-latency model inference.
Monitoring & Observability
Prometheus, Grafana for infrastructure monitoring; Evidently AI for model performance and data drift.
Frequently Asked Questions
What is MLOps and why is it important?
+MLOps applies DevOps principles to ML systems, crucial because ML requires managing code, data, and models. It automates this complex lifecycle, making ML systems reliable and scalable in production.
Our data science team already builds models. How do you help?
+We partner with data science teams to productionize their work. Your team focuses on modeling and research, while we build the robust engineering platform for testing, deploying, monitoring, and scaling models in production.
How do you monitor a model in production?
+We monitor four key areas: 1) Infrastructure (CPU/memory), 2) Operational performance (latency, throughput), 3) Input data properties (for drift), and 4) Model’s predictive accuracy over time. Automated alerts trigger when performance degrades.
What is a Model Registry and do we need one?
+A Model Registry is a central hub for managing the lifecycle of ML models. It tracks versions, stages (dev, staging, prod), and associated metadata, ensuring that you always know exactly which model is running where and why.
How do you optimize models for GPU performance?
+We use techniques like model quantization, pruning, and optimized kernels (using TensorRT or ONNX) to ensure your models run with maximum efficiency and minimum latency on specialized hardware.
How do your engineering teams work with our data scientists?
+We bridge the gap. We help data scientists move from messy notebooks to modular, version-controlled code, providing them with the automated tools and pipelines they need to see their work actually reach production safely.