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Make your product engineering team
deliver 3× faster

Everyone builds products with AI. The difference is how. We redesign your SDLC around AI to unlock more engineering capacity and accelerate software delivery.

Book a 30-min call Take the free assessment

We work with the stack you already pay for:

  • GitHub Copilot
  • Cursor
  • Claude Code
  • OpenCode
  • MCP
  • Amazon Bedrock
  • Azure OpenAI
  • Jira

Adoption is “high”. Measured impact is not.

80% → 39%

80% of companies make efficiency the goal of their AI initiatives; only 39% see EBIT impact at enterprise level.

McKinsey, The State of AI 2025

31%

of engineering leaders measure the productivity impact of their AI tools.

LeadDev, AI Impact Report 2026

~10%

Tools alone (L1–L2): median productivity gain in teams using AI coding tools. In one enterprise, rework cancelled it out.

Stanford software engineering productivity research, 120k+ developers

With agents on a redesigned workflow (L3+): 3× faster coding, 3× throughput.

Deliver more, faster – with the team you have.

01

Ship more

More stories and features out of the same team.

Tracked: throughput

02

Ship faster

A shorter path from approved spec to deployable code.

Tracked: feature lead time

03

Spend less per feature

The same budget buys more delivery.

Tracked: cost per story point

04

Keep quality

Speed never hides rework.

Tracked: rework and fix rate

What faster coding is worth

40
AI coding boost

Engineering capacity unlocked

≈ $1.2M

a year of engineering capacity

≈ 8 engineers’ worth of output, without hiring

capacity = engineers × loaded cost × 30% × (1 − 1 ÷ boost)

Coding is ~30% of idea-to-production, so this counts coding alone. Redesigning spec, review and QA gives more. An estimate, not a promise – we replace it with your baseline.

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You bought AI tools. Delivery didn’t speed up.

01

Coding is ~30% of idea-to-production.

3× faster coding alone adds ~25% to delivery. It stalls in review and QA if they can’t keep up.

02

A few champions, most on autocomplete.

Licences 6–12 months old, usage flat. Personal productivity, invisible in delivery.

03

Bigger PRs, slower reviews.

Review becomes the bottleneck.

04

No baseline.

“Faster” is a feeling, not a fact.

“The board expects a 30% efficiency boost by next year. There is no adoption plan in place.”

“We bought the licences. I cannot tell you our adoption level or what changed.”

Everyone codes with AI. The difference is how.

L1–L2 is personal productivity: it rarely shows up in delivery. 3× comes from L3–L4 – agents run real tasks, people review the evidence.

  1. L0

    You drive; no assist

    No AI in engineering work.

  2. L1

    You drive; the dashboard helps

    Prompts and chat. People write the code.

    ChatGPT · Copilot chat

    Where teams are: L1–L2, ~90% of teams today

  3. L2

    One hand on the wheel

    Autocomplete in the IDE, plus the basic skills the tools load on their own.

    GitHub Copilot · Cursor

    Where teams are: L1–L2, ~90% of teams today

  4. L3

    The car drives; you supervise

    Coding agents run real tasks; engineers review.

    Claude Code · MCP

    Where teams are: Technology ready, few teams

  5. L4

    The car drives the route; you monitor by exception

    Specs in; orchestrated agents deliver with evidence; checks are async.

    Orchestration · evidence packs

    Where teams are: Leading teams, selected workflows

  6. L5

    Self-driving: value delivery only

    People set the value and accept outcomes; agents deliver the rest.

    Where teams are: Not production-ready yet

We score every SDLC stage. On pilot streams, development and code review go to L3 – that’s where 3× comes from. Every other stage moves up one level per wave.

Levels adapted from Dan Shapiro’s five levels.

Where’s your team? Find your level, L0–L5, in 3 minutes.

Take the free assessment

Questions we get

How much faster will we deliver?

Delivery: our UK InsurTech client reached 3× throughput and −63% cost per story point. Coding alone gets 3× faster on pilot workflows (−67% implementation time), but on its own that adds only ~25% to delivery – the rest comes from redesigning spec, review and QA. We measure it against your baseline.

We already have Copilot, Cursor or Claude Code. What do you add?

Tools give capability. Delivery speeds up when spec, review and QA change around agents – and someone measures it. We do both, with the licences you already pay for. We’re not tied to any tool vendor.

How do you measure the ROI of AI?

Baseline first, then six metrics on similar-scope work: tasks accelerated by AI, rework rate, time saved, cost per story point, feature lead time and developer pulse. ROI = capacity unlocked plus quality and time-to-market savings.

How long does it take, and what does it cost?

The assessment takes 1–2 weeks, depending on team size and complexity. An implementation wave is ~7–8 weeks – about two months from kick-off to handover. For pricing, get in touch: we quote a fixed price for your scope.

Who owns the IP? How do you access our code?

You own everything we build for you: code, skills, agents, evals and playbooks. We work inside your environment, with access you grant per repo and can revoke. NDA and DPA come first.

Will AI-written code reach production unchecked?

No. Human approval gates, evals in CI and full traceability are part of every rollout.

Is this about cutting headcount?

It’s about capacity: more output from the team you have. What you do with that capacity is your call.

Start with a 30-minute call.

Free. No slides. Three questions: what you’re doing with AI today, what hurts, what’s next.

Book a 30-min call Take the free assessment