Measured
Typical outcome: measurable productivity improvement
Accelerate software teams with practical AI.
AI ADOPTION FOR DEVELOPMENT TEAMS
We help technology leaders introduce AI coding tools with clear operating models, measurable productivity gains, and governance standards that satisfy risk, security, and compliance expectations.
- Improve engineering throughput with repeatable AI coding practices.
- Strengthen code quality through AI-assisted review, testing, and refactoring.
- Enable managers with clear capability frameworks and adoption playbooks.
- Implement governance for IP protection, data handling, and accountable usage.
Faster
Typical outcome: faster delivery of production-ready code
Confident
Typical outcome: stronger governance and leadership confidence
WHAT WE DELIVER
Consultancy for organizations that need AI adoption to be productive, governed, and scalable.
AI Adoption Assessment for Engineering Teams
Assess engineering workflows, AI maturity, capability gaps, and policy risk to define a practical adoption strategy.
Developer Workflow Design
Define standardized AI usage patterns for implementation, debugging, refactoring, documentation, and review across teams.
Governance and Leadership Enablement
Establish governance models, leadership enablement, and usage controls so AI scales without avoidable risk.
DELIVERY MODEL
From team assessment to organization-wide AI adoption in three phases.
Assess
Map coding practices, team capabilities, tool usage, and governance requirements.
Enable
Run hands-on AI enablement with selected teams and measure productivity and quality outcomes.
Scale
Standardize playbooks, governance controls, and manager reporting across the organization.
TEAM USE CASES
Interactive view of where AI helps developers and engineering managers day to day.
Plan: Better backlog and task shaping
Use AI to turn product requirements into clear technical tasks developers can execute faster.
- Convert product requests into implementation-ready stories and acceptance criteria.
- Draft technical options and tradeoffs to support architecture discussions.
- Highlight blockers, dependencies, and sizing risks earlier.
Code: Faster implementation with AI assistants
Use AI during development to generate code, explain unfamiliar areas, and accelerate refactoring without losing standards.
- Generate implementation drafts aligned to team conventions.
- Refactor legacy code with clearer structure and reduced complexity.
- Create technical documentation and inline explanations as code evolves.
Review: Stronger pull requests and quality checks
Use AI to support code review by identifying risks, suggesting improvements, and enforcing coding standards.
- Summarize pull request intent and highlight risky changes.
- Suggest maintainability, performance, and security improvements.
- Provide reviewer prompts for consistent engineering quality.
Test: More coverage with less manual effort
Generate and maintain tests with AI to improve confidence while reducing repetitive test authoring work.
- Create unit and integration test drafts from implementation code.
- Expand edge-case and negative-path test scenarios automatically.
- Identify weak coverage areas and suggest missing assertions.
Govern: Policy, leadership, and responsible adoption
Give managers and leadership clear governance models so AI usage scales safely across development teams.
- Define approved usage patterns, security boundaries, and data rules.
- Train engineering managers on capabilities, limits, and rollout expectations.
- Track adoption, productivity impact, and compliance signals over time.
WHY TEAMS ENGAGE
Practical transformation across coding, quality, and leadership capability.
THE TEAM
Experienced software leaders guiding practical AI adoption.
Simon Oddy
Co-Founder and AI Engineering Consultant
Simon has over 30 years in software engineering and holds an MSc in Artificial Intelligence at the University of Bath.
He has spent 20 years leading private businesses and delivering high-reliability software solutions, including mission-critical systems for emergency services, while guiding agile transformation across teams.
Israel Menis
Co-Founder and Solutions Architecture Lead
Israel has over 20 years leading engineering teams and designing high-performance software architectures across multiple sectors.
He focuses on aligning business outcomes with the right technology choices, helping teams improve programming quality, product development, cloud practices, and data architecture with measurable impact.
TEAM AND BUSINESS OUTCOMES
AI adoption that improves engineering performance and executive confidence.
We connect adoption to measurable productivity, software quality, and governance maturity so leadership can scale with control.
Higher Developer Throughput
Developers deliver more useful code by reducing drafting, debugging, and rework time.
Better Code Quality
AI-assisted review and testing patterns improve consistency and reduce defects.
Controlled, Governed Adoption
Management gains clear policy guardrails and visibility as AI usage expands.
START WITH YOUR ENGINEERING TEAM
Ready to modernize your engineering workflow with AI?
Tell us about your goals and we will shape a practical AI adoption plan together.