ML Engineering
From training pipeline to production endpoint.
We build the model, the infrastructure around it, and the monitoring that keeps it honest. You get a running system, not a notebook.
Free, no commitment. Takes about 30 seconds.
What we build
Full-stack ML engineering
Data pipelines
Ingestion, transformation, validation, and feature engineering. Clean data flowing to training on a schedule.
Model training
Supervised, unsupervised, and reinforcement learning. Architecture selection, hyperparameter tuning, and cross-validation.
Model serving
REST and gRPC endpoints, batch inference, streaming predictions. Low-latency serving on your cloud.
MLOps pipelines
CI/CD for models. Automated retraining, A/B testing, shadow mode, and gradual rollouts.
Monitoring and drift
Prediction distribution monitoring, feature drift detection, alerting, and automated retraining triggers.
Model explainability
SHAP values, feature importance, and prediction explanations. Required for regulated industries.
Stack
What we build with
Process
How we run ML engineering projects
step 01
Data audit and baseline
We assess your data, set a performance baseline, and define success metrics before writing a line of model code.
step 02
Pipeline build
Data ingestion, transformation, and feature engineering built as reproducible, versioned pipelines.
step 03
Model development
Iterative training with tracked experiments. You see model performance at every stage.
step 04
Deploy and monitor
Production deployment with endpoint APIs, monitoring dashboards, and retraining automation.
Engineers behind these numbers
Project outcomes
What clients have shipped
6 wks
Median time from data audit to production endpoint
40%
Avg reduction in manual review after automation
3x
Typical engineering velocity vs. in-house first attempt
Before you decide
The doubts everyone has before hiring us.
And what we do about each one.
AI projects never make it past the prototype.
Your data is assessed in the first week, and you get an honest answer on whether there is enough signal to build something useful.
Data assessment in week oneThis will just be a chatbot on top of an API.
Language models are used when they fit, like text and language tasks.
The right tool for the problemOur data is too sensitive to leave our infrastructure.
Model training can run entirely in your cloud, so your data never leaves your infrastructure.
We'll end up locked in to you.
Trained weights, training code, data pipelines and all documentation transfer to you on delivery.
Models and code are yours
FAQ
Common questions
Ready to build
Ship your ML model to production.
Tell us what you want to predict. We'll take it from there.
What happens next
- 1
You tell us what you need. It takes about 2 minutes.
- 2
A real person replies within 4 business hours.
- 3
A 30-minute call to understand the project and agree on next steps.