When Clients Call Us Urgently
If one of these sounds familiar, this is where we step in.
We start with a review of your data, goals, and current model, then give you a written plan.
The Model Worked in a Notebook but Fails in Production
Data drift, missing features, and slow inference break models after launch. We compare training and live data, fix the pipeline, and add monitoring. A model you cannot trust in production is an expense, not an asset.
Accuracy Is Not Good Enough to Ship
Weak labels, too little data, and the wrong metric are common causes. We audit the data, set a clear success metric with you, and try simpler baselines first. That saves months spent tuning the wrong thing.
The Model Is Too Slow or Too Big for Your App
Large models cost money and time on every request. We compress and tune the model, and use LiteRT (formerly TensorFlow Lite) for mobile or edge where it fits. Slow predictions frustrate users and inflate the cloud bill.
Nobody Can Reproduce the Results
Untracked data, code, and settings make results impossible to repeat. We add versioned data, tracked experiments, and a repeatable training pipeline. Your team stops depending on one person’s laptop.
