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Every engagement lists its scope, timeline and fixed fee before checkout

Techluminate
Data & AI TL-DAT-105

Machine Learning Feasibility Study

A measured assessment of whether your data can support the model you have in mind, before you invest in building it.

Timeline
10 working days
Deliverables
7 listed
Practice
Data & AI
Fixed fee
₹18,999
01Overview

What this engagement delivers

Many machine learning projects stall once someone checks whether the data can support the idea, so this study does that check first. We take your proposed use case, examine the data you actually hold and train a baseline model to measure how much signal exists. The answer may be a well-tuned model, or it may be that a simple rules engine delivers most of the value at a fraction of the cost. Either way, you have evidence before you commit a budget.

02Deliverables 7 items

What you receive

  1. Data audit covering volume, quality, labelling and class balance
  2. Baseline model trained on your data, with measured performance
  3. Comparison against a simple rules-based alternative
  4. Feature analysis showing which signals carry the useful information
  5. Effort and cost estimate for a production build, with ranges
  6. Written recommendation, including a recommendation not to proceed if warranted
  7. Reproducible notebook and code you keep
03Process

How the work runs, in 4 stages

  1. Stage 1: Frame

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

  2. Stage 2: Audit

    Data is profiled for completeness, leakage and label quality

  3. Stage 3: Baseline

    A model is trained and measured against the rules alternative

  4. Stage 4: Report

    Findings, numbers and a build estimate presented on a call

04Fit & inputs

Who it suits, and what we need from you

Ideal for

  • 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

You provide these through the technical brief in your dashboard after checkout.

05Benefits

What changes for your team

  • 01

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

  • 02

    Data quality issues identified before they derail a build

  • 03

    Where appropriate, a simpler non-model alternative identified

06FAQ

Questions about this engagement

You receive that answer with the supporting evidence, which is the least expensive way to learn it. We would rather advise against a build than deliver one that cannot succeed.

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 a remote engineering engagement — nothing is shipped physically. Work is delivered into systems you control. See delivery & handover and refunds & cancellation for the full terms.

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Next step

Bring the problem. Leave with a defined plan.

Pick the engagement that matches what you need, or tell us about the system you are working on and we will point you to the right starting place.