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Created a scalable platform for automated technical due diligence on software projects.
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Case File

CodeVerified

AI-powered code review platform that analyzes repositories and generates actionable engineering reports.

Client
Client: CodeVerified
Year
Year: 2026
Tag
Tag: AI / Developer Tools / Automation
Focus
Focus: Platform architecture / Automation pipeline / Engineering reports
Pipeline stages
5
Review mode
AI + Human
Delivery
Automated

The challenge

Businesses often inherit or purchase codebases but lack the technical expertise to evaluate quality, security, and maintainability with confidence.

The result

The result was a repeatable due-diligence platform that turns repository evaluation into a scalable, auditable service instead of a manual consulting bottleneck.

Solution

  • Ingest repositories into a structured review pipeline
  • Run automated analysis across security, maintainability, and architecture concerns
  • Generate structured engineering reports for business stakeholders
  • Combine AI analysis with human engineering verification before delivery

Technology

PythonFastAPI workern8n orchestrationDeepWiki ingestionDigitalOcean SpacesSendGrid automation

Architecture

  1. 1Repo ingestion
  2. 2Analysis pipeline
  3. 3AI review layer
  4. 4Report generation
  5. 5Automated delivery
Artifact gallery
CodeVerified screen 1
CodeVerified screen 2
CodeVerified screen 3

Project narrative

Overview

CodeVerified was built to help non-technical buyers and operators understand what they are inheriting before they commit to a software project or acquisition. The goal was to turn repository analysis into a repeatable service with credible reporting and fast turnaround.

Problem

Inherited and purchased codebases often come with hidden risk. Teams need to know whether the architecture is maintainable, whether security issues are already present, and whether the code can realistically support future product plans.

Solution

  • Designed a repository ingestion flow that normalizes incoming projects before analysis.
  • Built an automated pipeline that scans structure, dependencies, and implementation patterns.
  • Generated structured engineering reports for security, performance, and architecture findings.
  • Added a human verification layer so the final deliverable stayed defensible and useful.

Technology

  • Python and FastAPI workers for processing and orchestration
  • n8n for workflow coordination
  • DeepWiki ingestion for repository understanding
  • DigitalOcean Spaces for artifact storage
  • SendGrid automation for report delivery

Outcome

The platform created a scalable system for automated technical due diligence and made it easier to evaluate software projects without depending entirely on manual senior-engineer review time.

Outcome

The result was a repeatable due-diligence platform that turns repository evaluation into a scalable, auditable service instead of a manual consulting bottleneck.

Pipeline

Repository analysis with clear artifact boundaries

The platform was designed as a pipeline so ingestion, analysis, report assembly, and delivery could be monitored and improved independently.

Trust

AI findings verified by engineering review

Automated analysis was paired with human verification so the final report stayed actionable for technical due diligence and stakeholder decision-making.

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