Machine Learning Solutions

Custom model development

Bespoke models trained on your data, delivered as running systems with pipelines, evals and documentation. You own everything.

Scope a feasibility study

Bespoke models, end to end

Off-the-shelf models solve generic problems. When your edge is your data, a pricing history no competitor has, a defect archive from your own line, a decade of claims, a custom model turns that edge into software. We build these models end to end: problem framing, data work, training, evaluation and deployment in one engagement.

The engagement starts with a baseline in the first days, because a simple model that works beats a complex one that might. From there, every increment of complexity has to pay for itself on the evaluation set. You see the metrics weekly, on your data, and you can stop at any point with something that already works.

What changes for you: instead of a notebook and a promise, you get a versioned training pipeline, a deployed model behind an API and the documentation to retrain without us. Weights, code and data pipelines are yours outright, and the handover includes working sessions with the engineers who will own the system.

Why build custom

Your data becomes the moat

A model trained on your history does things a generic API cannot, and competitors cannot copy it without your data.

Baseline first

We train the simple model first and publish its score. Complexity is added only when it beats the baseline on your metric.

Full ownership

Weights, training code and pipelines are handed over completely. No per-seat license, no rent on your own model.

Production from day one

Models are built to be deployed: containerized, versioned and tested for latency and load, not just accuracy.

Cost under control

A right-sized custom model at scale often runs at a fraction of per-token API pricing, and we show you that math before you build.

Machine Learning

How the work runs

Frame the problem

We define the prediction target, the metric and the baseline to beat, in business terms your CFO would sign off on.

Build the dataset

Audit, clean and label the data, with leakage checks and a frozen test set before any training starts.

Train and evaluate

Baselines first, then stronger architectures while the metric keeps moving. Every run is tracked and reproducible.

Deploy and hand over

The winning model goes live behind an API with monitoring, and your team gets the pipeline, docs and runbooks.

What you get

Trained model with weights and full ownership

Reproducible training and evaluation pipeline

Metrics report against an agreed baseline

Deployed inference API with monitoring

Model card, documentation and retraining runbook

Questions, answered

How much data do we need to train a custom model?

Less than you think for fine-tuning, more than you hope for training from scratch. A two-week feasibility study on your actual data replaces guessing: you get a baseline score and a clear read on whether more data would move it.

Custom model or a fine-tuned foundation model?

Fine-tuning wins when a pretrained model already roughly understands your domain; training from scratch wins on narrow, structured problems with plenty of labeled data. We benchmark both against your metric and recommend the cheaper path.

What does it cost to run after handover?

We size the model for your traffic and budget before training, and the metrics report includes cost per thousand predictions. Most custom models run on modest infrastructure, and the runbook covers scaling up and down.