AI & Machine Learning Services
Predictive models, computer vision, NLP pipelines, and automation built on your data and deployed in your infrastructure.
AI Service Lines
Seven AI service lines, each focused on a specific class of problems. All built with your data, deployed to your infrastructure.
How we work
No prototypes dropped in a Colab notebook. We build systems that run.
Process complete. Monitoring active.
Every engagement starts by auditing your data and workflows. Most teams have 3-5 AI use cases worth building. The ones worth pursuing are identified by impact, not novelty.
Clean data is 80% of the work. Ingestion pipelines, labeling, and structure need to be set up for training, not just exploration. That groundwork happens before any modeling.
Training runs on your actual data with the right model architecture. No off-the-shelf wrappers. Validation happens against your real production conditions.
Model serving, monitoring, drift detection, and retraining pipelines. The final deliverable isn't a notebook. It's an observable system running in your infrastructure.
Tech Stack
Industry-standard ML infrastructure. No proprietary lock-in. Your team can own it.
ML Framework
TensorFlow
Deep learning model training and serving
ML Framework
PyTorch
Research to production neural networks
ML Library
scikit-learn
Classical ML, preprocessing, pipelines
NLP / LLMs
Hugging Face
Transformers, embeddings, fine-tuning
LLM Orchestration
LangChain
RAG, chains, agent workflows
MLOps / Cloud
SageMaker
AWS training, deployment, endpoints
MLOps / Cloud
Vertex AI
GCP ML platform, AutoML, pipelines
Experiment Tracking
MLflow
Run tracking, model registry, serving
Building AI products is also why we know how to evaluate AI fluency in the engineers we place. It is not a checkbox for us.
See our free AI toolsFAQ
Let's build something real
We'll tell you what it takes, what data you need, and whether it's worth building.
