What We Build
Machine Learning Systems
Classification, regression, recommendation, and prediction systems built for production reliability.
LLM Applications
ChatGPT, Claude, and open-source model integration—RAG pipelines, agents, and custom applications.
Computer Vision
Image classification, object detection, and visual inspection systems for real-world use cases.
Natural Language Processing
Text classification, entity extraction, sentiment analysis, and document processing.
MLOps Infrastructure
Model training pipelines, experiment tracking, model serving, and monitoring in production.
Data Science Support
Helping data scientists get their models into production with proper engineering practices.
Technical Capabilities
Traditional ML
Scikit-learn, XGBoost, and classical algorithms that often outperform deep learning for structured data.
Deep Learning
PyTorch and TensorFlow for problems that genuinely require neural networks.
LLM Integration
OpenAI, Anthropic, open-source models—prompt engineering, fine-tuning, and RAG architectures.
MLOps Platforms
MLflow, Kubeflow, SageMaker, Vertex AI—infrastructure for the full model lifecycle.
How We Work
Problem Definition
Making sure ML is the right solution and defining success metrics that matter to the business.
Data Assessment
Evaluating data quality, availability, and what's needed to build something useful.
Experimentation
Rapid prototyping to validate approaches before investing in production infrastructure.
Production Engineering
Building the serving infrastructure, monitoring, and operational practices for reliable ML.
Iteration
Continuous improvement based on real-world performance and feedback loops.
Knowledge Transfer
Enabling your team to own and evolve the ML systems we build together.
When to Call Us
You have a data science team but struggle to get models into production
We'll build the MLOps infrastructure and practices that bridge the gap between notebooks and production.
You want to add AI capabilities to your product
We'll help you identify the right opportunities and build production-ready features—not science projects.
You're evaluating LLMs for your business
We'll help you cut through the hype, identify realistic use cases, and build applications that actually work.
Your ML systems are unreliable or hard to maintain
We'll stabilize operations, add monitoring, and implement practices that make ML manageable.
Frequently Asked Questions
Do we need AI for our problem?
+Maybe not. We’ll be honest if a simpler solution would work better. AI adds complexity and cost—it should only be used when the value justifies it. Sometimes a well-designed rule system or SQL query is the right answer.
What about hallucinations and reliability in LLM applications?
+These are real challenges. We design architectures that mitigate hallucination risks—RAG with verified sources, structured outputs, validation layers, and appropriate use cases. We won’t build something that fails in ways that damage your business.
Should we build or buy AI capabilities?
+Usually a combination. We’ll help you evaluate build vs. buy for each capability, considering your specific requirements, budget, and strategic importance. Pre-built APIs make sense for many use cases.
How do you handle model monitoring and drift?
+We build monitoring from the start—input distributions, prediction quality, business metrics. When models drift, you’ll know before it becomes a problem. Retraining pipelines are part of production ML.