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Build-vs-Buy Decision Audit Service

A 2-3 week hands-on consulting engagement where a technical advisor interviews stakeholders, audits current workflows, stress-tests 3-5 candidate off-the-shelf solutions against actual business requirements, and delivers a written decision framework with ROI projections for build vs. buy scenarios specific to that company's constraints.

SERVICE

14 weeks • 70% confidence

Value Proposition

Replaces guesswork with data-driven clarity in 2-3 weeks instead of 3-6 months of internal debate; identifies hidden costs (integration, training, maintenance) that kill bad builds early; decision-makers walk away with a defensible recommendation they can present to leadership, reducing post-purchase regret

Target Audience

SMBs with 20-200 employees in operations, logistics, manufacturing, or professional services who are actively considering a software investment ($50k-$500k range) and have 2-3 decision-makers but no internal technical strategy function

Key Features

Structured stakeholder interview protocol (operations, finance, IT) to surface real pain points vs. stated ones
Hands-on trial setup of top 3-5 candidate SaaS tools with real data to test fit
Build-cost estimation framework (dev time, QA, deployment, ongoing maintenance) calibrated to the industry
And more, with full implementation detail...

Tech Stack

Google Sheets or Airtable for decision matrix and cost estimation templates Figma or Canva for visual report design Loom for video walkthroughs of candidate SaaS tools Simple CRM (HubSpot free or Notion) to track engagements and outcomes

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The build plan is behind the wall

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Original Problem

Businesses struggle to decide between building custom software vs. buying off-the-shelf solutions, wasting time and money on the wrong choice

Technical decision-makers and business leaders face paralyzing uncertainty when choosing between developing proprietary software and adopting ready-made SaaS platforms. They lack clear frameworks to evaluate trade-offs between customization, cost, time-to-market, and maintenance burden, resulting in either expensive failed builds or purchasing solutions that don't fit their needs.

Score: 21.4% • 2 demand signals