Backend platform team
A hands-on workshop turns a legacy module into an AI-assisted refactoring playbook the team keeps using after we leave.
Service 02 · Engineering enablement
Raise your software development productivity with AI.
We help organizations adopt AI in their SDLC-s: assess readiness, train technical leadership and engineering teams hands-on, and set a realistic adoption strategy.
01
AI-powered SDLC
The goalRaise software development productivity through a clear AI adoption strategy, trained teams, and realistic expectations.
We assess how ready your existing development process is for AI, brief technical leadership on the opportunities and the risks, run in-depth seminars and hands-on workshops with your development teams, and deliver an adoption strategy tailored to your context — so the gains hold up after we leave.
02
A clear strategy is needed
The why- Coding assistants are in use unevenly across teams; the impact is anecdotal at best.
- 10x productivity increase claims are unrealistic.
- Leadership needs to know what's actually achievable, and how to get it.
- Concerns about code quality, security, or IP have stalled adoption.
- Developers are producing plenty of AI-generated code, but delivery isn't actually faster.
- Every developer improvises with their own tools and prompts; nothing is standardized.
03
What We Do
Key activities
01
AI readiness assessment
A deep dive into and review of your current processes and tooling, to determine where AI can help now, and what needs fixing first.
02
Leadership briefing
A focused working session for technical leadership: real capabilities, limitations, and risks, and how AI-accelerated development changes productivity, quality, and team dynamics.
03
Developer seminar
Tailored seminars for your engineers: the tooling landscape, workflows that hold up in production codebases, and the failure modes that create technical debt instead of speed.
04
Hands-on workshops
Practical sessions with each team, working on your actual projects and tasks — not toy examples — so the new practices stick.
05
AI adoption strategy
Concrete proposals for optimizing your development process, with metrics, priorities, and a rollout plan, sized to your organization.
06
Support & evaluation
Advisory support while teams apply the new practices, a follow-up evaluation of the impact, and clear recommendations for scaling what works.
04
Engagement Structure
Four weeksAssess
Readiness questionnaire, interviews with leadership, and a review of processes and tooling.
Educate
Leadership briefing, developer seminar, and hands-on workshops per team; the adoption strategy is drafted.
Apply
Teams use the new practices in their daily work, with our advisory support on tap.
Evaluate
Follow-up assessment with team leads, measured impact, and recommendations for next steps.
05
Expected Outcomes
What you get- Sustainable, real productivity gains across the whole development process.
- Code quality and engineering standards stay the same or improve.
- Deployment reliability stays the same or goes up.
- A standardized, sustainable approach to AI tooling across teams.
- Teams that are self-sufficient with AI and clear-eyed about its strengths and limits.
Next step
Talk to us about AI Enablement for Software Development
30 minutes to explore your unique situation and determine whether this is the right shape of work for you.