91%
AI adoption at scale
+27%
Engineering velocity, measured before/after on production code
>95%
Time savings on piloted tasks
73% of enterprise GenAI programs never reach measurable ROI
APEX Fixes Four Critical AI Engineering Challenges
Untracked license waste
Missing ROI visibility
Marginal productivity gains
Security and governance gaps
APEX methodology
APEX runs in four phases, from one team to the whole engineering organisation. Each phase has to prove the results before the next begins, so you expand only what already works.
1-2 weeks
- Deep-dive audit of tools, workflows, and team maturity
- Capture baseline DORA and DX metrics
- Identify and develop internal AI Champions
- Prioritized roadmap with ROI projections per workflow
4 to 6 weeks
- 2-week sprints targeting 2-3 high-impact workflows
- N-iX engineers co-implement on your active production codebase
- Before/after measurement against your baseline metrics
- Results documented
Weeks 4–13
- Roll proven workflows across additional teams and departments
- Customized internal playbooks for self-service adoption
- Scale tooling and infrastructure for enterprise-wide use
- Monthly performance optimization sprints with leadership reviews
Ongoing
- Advanced agentic and multi-agent workflows
- AI-native SDLC: spec-driven development, AI-assisted development, AI DevOps
- Continuous experimentation with emerging tools
- Strategic consulting to keep your AI program ahead of competitors
Why engineering leaders choose APEX
| Capability |
|---|
| Pre-engagement baseline metrics |
| Co-implementation on production code |
| Proof tied to your baseline |
| Internal capability transfer |
| Enterprise security & governance |
| Scale of engineering talent |
| Published client results |
| What actually changes |
| Typical AI consulting |
|---|
| Rarely, if ever |
| Workshop-based only |
| Estimated projections |
| Ongoing dependency |
| Client-managed |
| Variable |
| Success stories without numbers to support them |
| Tools deployed, workshops run |
| N-iX APEX |
|---|
DORA + DX baseline in Weeks 1-2
|
|
1-on-1 with engineers, 3-10 weeks
|
|
Before/after dashboard, exportable
|
|
Champions + playbooks + templates stay after we leave
|
|
Fully vetted, private-cloud only
|
|
2,400+ engineers across 10 delivery centers, all AI-enabled
|
|
Real case studies with specific metrics
|
|
The workflow itself, on live code
|
Typical APEX productivity gains by workflow
Measured on production work during APEX engagements: before-and-after on the same teams, same codebase, against the baseline captured in the Assess phase.
Case studies from 3 engineering teams Case studies
Yaroslav Kisylychka
Head of GenAI Value Lab
We started the way most teams do, deploying tools and running workshops, and it produced no measurable change. So we rebuilt how we work. If we can't prove it with numbers, we don't claim it.
Yaroslav Kisylychka
Head of GenAI Value Lab
Keep Your Code Safe While Using Our Vetted Tools
N-iX manages procurement, security compliance, and setup for every tool in your stack, so your engineers get a vetted toolchain without the governance overhead.
See where your AI stands
Tell us where your team is and we'll set up the 45-minute discovery: where you sit, the workflows worth changing first, and whether the numbers justify going further.
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