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Specification DAT·105

DAT · Data & AI Defined scope Fixed fee

Machine Learning Feasibility Study

A data audit and baseline model that show whether your data supports the model you want, before you commit budget to building it.

Fee · GST incl.
₹18,999
Duration
10 working days
Deliverables
7

01 Overview

A lot of machine learning builds collapse in their first week, the moment someone looks closely at whether the data can support the idea. This study does that looking before any build begins. We take the use case you have in mind, examine the data you really hold and train a baseline model to see how much signal is there. The result might justify a model, or show that a simple rules engine delivers most of the value for far less; in either case you have measured evidence before setting a budget.

02 Deliverables

7 items

What you receive at handover

  1. D01 Audit of data volume, quality, labelling and class balance
  2. D02 A baseline model built from your data, with measured performance
  3. D03 Comparison with a simple rules-based alternative
  4. D04 Feature analysis identifying which signals carry useful information
  5. D05 Ranged estimate of the effort and cost to build it for production
  6. D06 Written recommendation, which may be a recommendation not to proceed
  7. D07 Reproducible notebook and code for you to keep

03 Outcomes

What should be true once it ships

  • A go or no-go decision supported by a measured baseline
  • Data quality issues come to light before they derail a build
  • A cheaper solution that needs no model is identified where one exists

04 Process

How the 4 stages run

  1. Frame The prediction, the decision it informs and the measure of success are defined
  2. Audit We profile the data, checking completeness, leakage and how reliable the labels are
  3. Baseline A model is trained and compared with the rules-based option
  4. Report Findings, figures and a build estimate are presented on a call

05 Fit & inputs

A good fit for

  • Teams expected to add AI without a defined use case
  • Companies with a clear prediction problem but data nobody has tested
  • Boards that want a business case before approving a data science hire

What we need from you

  • An extract of the relevant data, masking any sensitive fields
  • A plain description of which decision the model is meant to improve
  • Access to the person who understands how the data is collected

These inputs are collected through the technical brief in your dashboard after payment. The duration is counted from when they arrive.

06 Answers

Questions about this engagement

You receive that conclusion with the evidence behind it, which is the least expensive outcome available. We would rather decline a build than sell one likely to fail.

That depends on the problem. For most classification tasks, a few thousand labelled examples is a sensible place to begin.

Confidentiality terms are put in place before you send anything, and on request we delete the extracts when the engagement ends.

This is technology work delivered digitally — nothing is shipped. Work is delivered into systems you control within the duration stated above, and the delivery and refund policies set out what happens if a date is missed.

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