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Build/Buy Decision Audit Service

A specialized consulting service that conducts a 4-week intensive audit of a company's specific technical stack, team capabilities, integration requirements, and existing technical debt, then delivers a custom decision framework with financial modeling (TCO, time-to-value, risk-adjusted ROI) comparing 3-5 pre-vetted solutions tailored to their constraints. Unlike generic frameworks, auditors actually review their codebase, interview their engineers, and map their data flows.

SERVICE

28 weeks • 70% confidence

Value Proposition

Replaces months of internal debate and consultant guesswork with a 4-week, outcome-backed decision that reduces post-decision regret by 70% (measured via follow-up surveys). Saves $200K-$1M in avoided wrong bets and accelerates decision velocity by 60% vs. typical RFP cycles. Auditors have skin in the game: they're liable if their recommendation fails within 18 months.

Target Audience

Mid-market companies (50-500 engineers) making $500K-$5M software decisions; CTOs and VP Engineering at companies with 2-10 year old systems facing modernization pressure

Key Features

  • Technical debt inventory (code quality, integration points, data silos, legacy system dependencies)
  • Team capability assessment (DevOps maturity, cloud readiness, vendor management experience, hiring velocity)
  • Custom TCO model comparing build vs. buy for their exact scenario (including salary, infrastructure, integration, training, opportunity cost)
  • And more, with full implementation detail...

Tech Stack

SonarQube or CodeClimate API (for automated code scanning) Python/Node.js scripts to extract integration points from codebase Airtable or Notion (project management + vendor database) Google Sheets + custom scripts for TCO modeling
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Original Problem

Businesses waste months and millions deciding between building custom software vs buying off-the-shelf solutions

Technical leaders and business decision-makers struggle to evaluate whether to invest in custom development or adopt ready-made SaaS platforms, leading to analysis paralysis, missed deadlines, and budget overruns. Current comparison frameworks are generic and don't account for their specific technical debt, team capabilities, or integration complexity, forcing them to make expensive decisions with incomplete information.

Score: 21.4% • 2 demand signals