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Data & AI Fixed scope

Machine Learning Feasibility Study

An honest answer on whether your data can support the model you want, before you spend on building it.

Delivered in

10 working days

Revisions

One round, included

Ownership

Yours from day one

Overview

What this engagement is

Plenty of machine learning projects fail during the first week of a build, once someone finally checks whether the data supports the idea. This does that check first. We take your proposed use case, examine the data you actually hold, and produce a baseline model to see what signal exists. Sometimes the answer is a well-tuned model. Sometimes it is that a rules engine gets 90 percent of the value for a tenth of the cost. Either way you get the number before you commit a budget.

Deliverables

What lands in your repositories

7 items
  • Data audit covering volume, quality, labelling and class balance
  • Baseline model trained on your data, with measured performance
  • Comparison against a simple rules-based alternative
  • Feature analysis showing which signals carry the useful information
  • Effort and cost estimate for a production build, with ranges
  • Written recommendation, including a recommendation not to proceed if warranted
  • Reproducible notebook and code you keep

Outcomes

What changes once it ships

A go or no-go decision backed by a measured baseline

Data quality problems surface before they derail a build

Sometimes a much cheaper non-model solution is found instead

Process

How the 4 stages run

  1. 01

    Frame

    we define the prediction, the decision it drives and what good looks like

  2. 02

    Audit

    data is profiled for completeness, leakage and label quality

  3. 03

    Baseline

    a model is trained and measured against the rules alternative

  4. 04

    Report

    findings, numbers and a build estimate presented on a call

Booked most often by

  • Teams under pressure to add AI without a clear use case
  • Companies with a specific prediction problem and unproven data
  • Boards asking for a business case before funding a data science hire

What we need from you

  • A data extract, with sensitive fields masked if necessary
  • A clear statement of the decision the model would improve
  • Access to whoever understands how the data is collected

These are collected in the technical brief that opens in your dashboard the moment payment clears. The clock starts when they arrive, not before.

Answers

Questions this engagement raises

You get that answer with the evidence, which is the cheapest possible outcome. We would rather lose a build than sell one that fails.

It depends on the problem, but a few thousand labelled examples is a reasonable starting point for most classification tasks.

Yes, before any data is shared. Extracts are deleted at the end of the engagement on request.

This is engineering work, not a physical product — nothing ships and there is no stock to run out of. Delivery is to the working days stated above, into systems you control, and the refund terms set out what happens if we miss the date.

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Capacity open this month

Read the scope. Know the price. Start on Monday.

No discovery calls to find out a number, no statements of work that take three weeks to sign. Pick the engagement that matches the problem and we assign the engineers.

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