Data & AI Fixed scope

Machine Learning Feasibility Study

A clear, evidence-based answer on whether your data can support the model you have in mind, before you invest in building it.

Timeline: 10 working days
Price: ₹18,999 incl. GST
Reference: NP-DAT-105
Ownership: Delivered into accounts you own

Overview

What this engagement is

Many machine learning initiatives stall early in the build, at the point someone finally checks whether the data can support the idea. This study carries out that check up front. We take your proposed use case, examine the data you actually hold and train a baseline model to reveal how much signal exists. Sometimes the right answer is a carefully tuned model; sometimes a simple rules engine delivers most of the value at a fraction of the cost. Either way, you have the evidence before committing a budget.

Deliverables

What you receive

7 items

Data audit covering volume, quality, labelling and class balance
Baseline model trained on your data, with measured performance
Comparison against a straightforward rules-based alternative
Feature analysis showing which signals carry useful information
Effort and cost estimate for a production build, with ranges
Written recommendation, including advice not to proceed where warranted
Reproducible notebook and code that remain yours

Outcomes

What changes once it ships

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

Data quality issues surface before they can derail a build

A far cheaper non-model solution is sometimes identified instead

Process

How the 4 stages run

Frame

We define the prediction, the decision it informs and what success looks like
Audit

Data is profiled for completeness, leakage and label quality
Baseline

A model is trained and benchmarked against the rules-based option
Report

Findings, measurements and a build estimate presented on a call

A strong fit for

Teams under pressure to adopt AI without a defined use case
Organisations with a specific prediction problem and untested data
Boards seeking a business case before funding a data science hire

What we need from you

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

You share these through the brief that opens in your client dashboard once payment is confirmed. The timeline starts when they arrive.

Answers

Questions about this engagement

You receive that conclusion with the supporting evidence, which is the least expensive outcome available. We would rather advise against a build than deliver one destined to fail.

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

Yes, before any data is shared. On request, extracts are deleted at the close of the engagement.

This is a digital service — nothing is shipped. Work is delivered to the timeline above, into systems you control, and our refund terms explain what happens if a date is missed.

Often paired with

Engagements that work well alongside.

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