Which AI tools do you work with?

Mostly Claude Code, GitHub Copilot and Cursor, and whatever you already pay for. We'd rather get value from your existing licences than sell you new ones.

Is this about cutting headcount?

No. The goal is more shipped work per engineer and less time lost waiting on reviews, QA and releases. What you do with the extra capacity is your call.

What about security and our code?

Guardrails are agreed with your security team in week one: which tools, which repos, what data may leave your environment. Every AI-generated change still goes through human review.

What do you need from us?

Read access to git and your issue tracker for the baseline, one or two pilot teams, and an engineering lead who owns the program after handover.

Do you work remotely?

Yes. We're based in Poland on CET, work with UK and European teams, and come on site for kick-off and key workshops when it helps.

Free calculator for CTOs, CFOs and founders

Is Claude Code enough? See what each stage of AI adoption does to delivery

Giving every engineer an AI coding assistant speeds up typing, not shipping. Enter a few numbers about your team and see how lead time and delivery capacity change as you add AI code review, a full AI workflow and AI-assisted refinement.

Your team

EngineersCost per engineer per hourSalary plus overheads, or your contractor rate€Lead time per ticket todayWorking days from starting a ticket to production

Where is your team today?Tick everything already in daily use

AI coding assistantClaude Code, Copilot or Cursor on engineers' machinesAI code reviewAn AI reviewer runs on every pull request in CIFull AI workflowAgentic workflows, shared context files, AI QA in the pipelineAI refinement and reasoningTickets refined into specs and plans with reasoning models

Your team today: +4% vs. no AI

AI coding assistant on its own

+4% delivery capacity

Code gets written about 40% faster, but coding is only part of a ticket's life. Bigger PRs wait longer for review, and more defects come back as rework. The time saved at the keyboard mostly moves to the review queue.

Refinement and clarification
Coding
Code review and waiting
Testing and QA
Rework and bug fixing

No AI8.0 days · baseline
AI coding assistant only7.7 days · +4%
+ AI code review6.3 days · +26%
+ Full AI workflow4.8 days · +67%
+ AI refinement and reasoning3.8 days · +112%
Your team today7.7 days · +4%

How the calculator works

A ticket spends its life in five phases: refinement and clarification, coding, code review and waiting, testing and QA, and rework. On a typical team without AI, coding is only about 30% of that time. The model starts from that split and applies an estimated speed-up or slow-down to each phase at each stage of AI adoption.

That is why an AI coding assistant on its own adds so little. It cuts coding time, but larger pull requests wait longer for review, and more defects come back as rework. Delivery only speeds up when review, QA and refinement change too, which is what an AI-native software development lifecycle (SDLC) does.

The factors are modelled estimates based on public research and our own client work. Your team will differ. The AI Engineering Snapshot replaces them with numbers measured from your own git history and issue tracker.

What the research says

Each 25% increase in AI adoption was associated with an estimated 1.5% drop in delivery throughput and a 7.2% drop in delivery stability.
DORA, Accelerate State of DevOps 2024
AI adoption now correlates with higher throughput, but still with lower delivery stability: more change failures and rework.
DORA, State of AI-assisted Software Development 2025
Teams with high AI adoption completed 21% more tasks and merged 98% more pull requests, while PR review time rose 91%.
Faros AI, The AI Productivity Paradox 2025