Transform software delivery with AI.
Improve productivity, quality and speed across your software delivery lifecycle. We work alongside your teams on live projects, embedding AI from requirements to release and measuring the change.
- Typically 6–9 months
- On live projects
- Embedded engineers
- DORA-based dashboards
Five ways to improve the economics of software delivery.
Productivity, quality and speed translate into five changes your business can measure. We agree priorities and targets against your own baseline.
Engineering effort
Less engineering time per reliable change.
Cycle time
A shorter path from request to production.
Rework and defects
Fewer review loops, bugs and failed releases.
Run and support load
More capacity available for product development.
AI and tool cost
Lower model and tool spend for comparable work.
Targets and results are measured against your own baseline: more value delivered, lower cost per reliable change.
From individual AI use to AI-native delivery.
Each level changes a working habit. Together, they build a delivery system your team can run — and apply across further teams.
Scroll across to explore the diagram →
L0 AI coding: use AI individually, with a harness. L1 Baseline: measure current flow with connectors. L2 Context: add domain context through docs and memory.
L3 Work board: make work visible. L4 Measures: prove gains weekly with controls. L5 Adoption: adopt as a team, in the daily scrum.
L6 Automation: reduce coordination. L7 Agents: more per engineer. L8 Governance: scale with controls and guardrails.
The framework, dashboards and playbooks stay with you. Apply the same levels to the next team.
We put the levels into practice on live delivery, in order, and prove each one with your own measures before moving to the next.
We work inside your project. Your team owns the change.
Amotion engineers work alongside your team to put the L0–L8 framework into practice on live delivery.
We bring the implementation team
Three embedded engineers work full-time inside your project, backed by a principal expert who sets the design and leads reviews. Each team supports one dedicated project or two smaller projects side by side.
You direct and own the work
A named engineering lead and protected time keep the work moving. Your engineers bring project knowledge, review changes and build the capability to continue independently.
You see the progress
Weekly working reviews address blockers. Monthly reviews assess productivity, quality and speed against the agreed baseline. Your technical leaders sign off each phase.
Typically 6–9 months
Working on live projects, with internal ownership built throughout. Agree a baseline and targets for the project at the start.
What stays with your team
- Configured systems, project knowledge and shared playbooks.
- A DORA-based dashboard on your own delivery data, with the review rhythm to use it.
- Internal champions able to support adoption and onboard others.
- The L0–L8 framework, ready to apply across further teams.
What your organisation brings
- A named engineering lead and protected participation time from the team.
- Domain knowledge, review of proposed changes, and technical approval of each phase.
- A baseline and targets for the project, agreed at the start.
- Ownership of adoption after the engagement.
Scroll across to explore the diagram →
See progress in your delivery data.
As part of the transformation, we help your team build a dashboard from live delivery data. Establish a baseline, review the trends and use the findings to decide what changes next.
DORA measures show flow and stability; we agree further measures for productivity, quality and cost. Used in monthly reviews, with weekly decisions on blockers.
Explore DORA Metrics & Dashboards →See the foundation in practice.
Botree, a distribution software company, worked with Amotion to build an AI-ready knowledge base across its repositories. Engineers and AI agents use it to understand the code, business rules and earlier decisions.
- 01
Shared codebase knowledge
Reviewed documentation and module notes give engineers and AI agents a common source of context.
- 02
Context-backed engineering decisions
Plans, defect investigations and code reviews draw on recorded decisions and earlier fixes.
- 03
Documentation checks on every pull request
Automated checks flag documentation issues; engineers verify the content and approve changes.
Delivery measures are defined. Performance metrics are shared under NDA.
See what changed at Botree →Find the right starting point.
Start your engineering transformation with one live project.
Let’s identify where AI can improve productivity, quality and speed — and what a first engagement would involve for your team.
founders@amotion.ai