Know what you have. Before you point AI at it.
A rapid technical audit of your codebase and cloud infrastructure — architecture, dependencies, dead code, coupling, security exposure, and cost. The document every incoming CTO wishes they had on day one. The prerequisite to letting AI agents anywhere near your code.
Two situations. Same blind spot.
- →New role, 30 to 90 days in
- →Inherited team, codebase, and a backlog that doesn't match what's in the code
- →Spending evenings reading files, tracing dependencies, and building a mental model
- →Need to make architectural calls before you've fully oriented
- →Want time-compression on three months of discovery work
- →"Known compromise" vs. "unknown risk" still indistinguishable
- →About to roll out or scale up Claude Code, Cursor, or Copilot across the org
- →Aware that agents amplify whatever shape the code is already in
- →Worried about modularity, dead code, and undocumented contracts
- →Need a clean baseline before agents start modifying the system
- →Want to know what to refactor before the agents make it worse
- →Eval and CI/CD safety net not yet in place
Either way, you need a read on the system that doesn't depend on what the previous team chose to write down. Ground Truth gives you that, generated from the system itself, not from the documentation.
Three outputs. One source of truth.
Every Ground Truth engagement produces three aligned deliverables. The Technical Deep Dive is the document you'll actually live in. The other two exist so the conversations you have with your CEO, CFO, and board are grounded in the same evidence you are.
Technical Deep Dive
- →Architecture diagrams generated from actual code, not whiteboard archaeology
- →Service coupling analysis showing where small changes cascade and why
- →Dead code, deprecated dependencies, and untested-path quantification
- →Security exposure map with committed credentials, attack surface, and dependency CVEs
- →Database schema documentation including stored procedures and triggers
- →Modularity analysis with specific refactor targets
- →Pre-agentic baseline: what to clean up before agents touch it
Executive Brief
- →What the business is actually running, in plain English
- →Where engineering time and cloud spend are going
- →What's slowing the team, with sourced reasons
- →30/60/90 action list aligned to product priorities
Cost & Risk Summary
- →Cloud spend by service, with waste quantified
- →Vendor and dependency concentration risk
- →Bus-factor exposure on critical systems
- →Compliance gap analysis across SOC 2, ISO 27001, and NIST AI RMF
Read-only access. Generated from the system itself.
We read directly from the artifacts that don't lie — code, infrastructure config, schema, CI/CD, and observability. Internal docs and architecture decks are useful as expected-state references, but they're inputs to validation, not sources of truth. When the docs say one thing and the code says another, we report what the code says.
Senior operators backed by 100+ concurrent read-only AI agents. Agents handle the broad crawl — architecture mapping, dependency analysis, dead code detection, security scanning, and modularity analysis. Senior engineers review every finding, validate against context, and produce the deep dive. The agents are the force multiplier. The judgment is human. We don't ship a finding we can't defend.
What we tend to find when we look.
These categories show up repeatedly when the analysis reads the actual system instead of taking documentation at its word. None of these are surprising in isolation; what's useful is having all of them on the same page, sourced and prioritized, by the end of the week.
Average cloud overspend identified. Unused resources, over-provisioned instances, and duplicated services accumulated over years. The savings often cover the engagement fee.
Live credentials, API keys, and secrets committed to production code. Found in most codebases. Creates breach liability and complicates compliance posture.
Tightly coupled services where a change in module A breaks module D. We surface specific coupling points, not just an aggregate score, with refactor recommendations along modularity boundaries.
Average portion of the codebase that is dead, deprecated, or untested. Pollutes both human onboarding context and AI agent context windows. Direct hit to velocity.
SOC 2, ISO 27001, NIST AI RMF, and HIPAA gap analysis mapped to specific code and infrastructure. Useful for audit prep, regulated-industry sales, and M&A diligence.
What breaks first under 10x load. Single points of failure surfaced from dependency graphs. Cascade risk traced through the actual call graph, not from a whiteboard sketch.
See the deliverable structure.
The sample technical deep dive shows the section structure: architecture map, service inventory, security findings, coupling analysis, dependency graph, dead code report, and prioritized refactor backlog. Written for engineers, sourced to the line.
Minimal access. Senior-led. Delivered in days.
Read-only credentials
GitHub read-only token, cloud Cost Explorer read access, database dump, and CI/CD config access. Your platform or DevEx lead can handle setup in under an hour.
100+ AI agents deployed
Purpose-built agents run concurrently across the codebase, cloud, and CI/CD: architecture mapping, dependency analysis, security scanning, dead code detection, coupling analysis, and schema documentation.
Senior engineering review
Senior engineers review every agent finding. We don't surface findings we can't defend. Each item in the deep dive has a source path and a reproducible analysis behind it.
Deep dive + working session
You receive the Technical Deep Dive, the Executive Brief, and the Cost & Risk Summary, plus a working session with you and your senior engineering leads to validate priorities and align on the refactor sequence.
“I've been wondering how this worked for two years since I got here. It diagrammed it better than I would have.”
Ground Truth is the baseline. Then we keep it clean.
Tech Debt Modernization
AI-accelerated refactoring against the priorities Ground Truth surfaces. Runs alongside your product roadmap, not instead of it, with sequencing built around modularity boundaries so changes don't ripple.
AI Readiness
For teams shipping AI features or rolling out AI-assisted development. Sandbox-first evaluation and regression checks that help keep the baseline clean once AI starts modifying the system.
See your system the way it actually runs.
A 30-minute briefing is enough to know whether Ground Truth is the right fit for your situation. We'll ask about your stack, your situation, and what you need to learn, then tell you honestly whether the engagement makes sense.