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Iria MonitorAI CODE OBSERVABILITY

See what your AI actually produces.

Measure authorship, cost, and durability of AI-generated code. Line by line.

April 2026 · Confidential

WHAT YOUR CLIENTS READ

Your board read the headlines. Now they want the numbers.

Every major consultancy is publishing the same message: AI will radically change software economics. Your clients’ boards are reading this and asking for proof.

McKinsey, 2025
“AI will automate 25% of software engineering tasks by 2027.”
Goldman Sachs
“3–4x developer productivity gains with AI coding tools.”
Gartner, 2025
“By 2028, 75% of enterprise software engineers will use AI assistants.”
Accenture
“+84% more builds per week in AI-assisted teams.”

The board translates this into one thing: fewer developers, lower cost. Prove it.

THE STATUS QUO

This is what most enterprises measure today.

Microsoft Copilot + Power BI. The default dashboard for 90% of large organizations. Three metrics, all about activity.

ID
Who has a Copilot license
%
Prompt acceptance rate
ORG
Team assignment

Activity metrics. Not business outcomes.

THE CLASH

Two questions. No shared answer.

THE BOARD ASKS
“How many developers can we reduce?”
THE CTO ANSWERS
“How many developers use Copilot?”

Measuring adoption is not measuring impact. The gap between these two questions is where decisions stall — and where budgets get frozen.

THE REAL PROBLEM

Proving AI productivity in large orgs is nearly impossible.

  • 01No baseline: you don’t know how long things took before AI
  • 02Activity metrics (prompts, acceptance rate) don’t correlate with real output
  • 03Bureaucracy (procurement, security reviews, change boards) masks any gain

DORA State of DevOps 2025: org-level delivery metrics stayed flat despite widespread AI adoption.

WHAT WE MEASURE

We measure what the AI actually writes. Line by line.

Not prompts. Not acceptance rates. The actual code that lands in production.

What code
Which lines the AI wrote, in which files, in which repos.
How much
Percentage of AI vs human code, by team and project.
At what cost
Actual token/API cost of each AI contribution, annotated in the commit.
What survives
Code rejected, modified by a human, or still in production after 30 days.
HOW IT WORKS

Deploys in your environment. Reads git. No code changes.

  1. 1
    Agent hook
    A lightweight hook captures what the AI agent edits. Works with Claude, Cursor, Codex, Windsurf.
  2. 2
    Git annotation
    Attribution is stored as git notes (refs/notes/ai). Open standard. Source code never leaves the machine.
  3. 3
    Dashboard
    Authorship, cost, and durability data visualized per repo, team, and developer.
  • Zero dependencies — runs in any agent process
  • Under 100ms per edit — invisible to the developer
  • Data stays in git — portable, auditable, yours
DEPLOYMENT

Your cloud or ours.

Two deployment models. Same product. Choose what fits your client’s compliance requirements.

Private cloud
  • Deploy in the client’s own infrastructure
  • Data never leaves their environment
  • Compliance-ready from day one
SaaS
  • Onboarding in minutes
  • Ideal for pilots and fast starts
  • SOC 2 / ISO 27001 on the roadmap
Iria MonitorNEXT STEP

Stop measuring adoption. Start measuring output.

We help your clients answer the only question their board cares about: is AI actually producing results?

Plexus Tech · 2026

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