Turn the data you already have into decisions, not dashboards.

Custom models — predictive, classification, computer vision, or NLP — trained on your own data, deployed where your team actually works, not left in a notebook.

Why This Matters

Most ML Projects Die in the Data, Not the Model

The hard part of machine learning is almost never the algorithm — it's whether the data exists, where it lives, and how clean it is. We audit that before anyone talks about models.

Every model is built against a baseline — what a human or a spreadsheet formula already achieves — so you know exactly what the model is actually worth, not just that it "works."

When the Project Lands

Days 1–3: Kickoff on the decision the model needs to inform. We audit what data exists, where it lives, and how clean it is — because most ML projects die here, not in the modeling.

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How we get there

The Four Phases

Phase 1 — Discover

A data audit across your systems, the decision the model must support, and a baseline of what a human currently achieves without it. Deliverable: a feasibility report, the scope, and a fixed quote.

Phase 2 — Design

Feature engineering plan, model architecture choice, and the evaluation metric that actually matters for your business — not just accuracy. Signed off before training starts.

Phase 3 — Build

Model training and validation against held-out real data, iterated until the agreed metric holds. Weekly results reviewed against the baseline from Discover.

Phase 4 — Deploy & Monitor

The model is deployed into your actual workflow — API, dashboard, or embedded in an existing system — with monitoring set up to catch drift before it silently degrades results.

Outcomes you can measure

Every phase ends with a fixed deliverable, not a status update.

Phase 1 — DiscoverStage 01 · Weeks 1–2Phase 2 — DesignStage 02 · Week 3Phase 3 — BuildStage 03 · Weeks 4–8Phase 4 — Deploy & MonitorStage 04 · Weeks 9–10

Handover

You receive the trained model and its weights, the training pipeline and documentation, a monitoring dashboard for drift and performance, the deployment connected to your own cloud account, a training session for your team, and ongoing support.

What It Costs

Pricing depends on the data audit and model complexity identified in Discover. Contact us for a fixed quote.

Have data sitting unused?

Tell us where you are today. We'll help you figure out if Machine Learning is the right starting point — no pitch.

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