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    APEX by N-iX – GenAI Developer Productivity Enablement

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    Sold by: N-iX 
    Most GenAI programs train people and hope something sticks. APEX does the opposite; it is a 4-phase framework (Assess → Pilot → Expand → eXcel) that embeds GenAI into real engineering workflows across SDLC/STLC - for developers, QA, and DevOps/SRE teams. Rather than broad training, it starts with champion-led pilots, measures actual impact, and scales what works. The result: 40–60% net productivity gains backed by before/after data.

    Overview

    Why do most GenAI enablement programs fail?

    Because they start with training and hope adoption follows. We've seen this pattern across dozens of engineering organizations – and it doesn't work. APEX flips this. We start with real workflows, real engineers, and real metrics.

    How it works APEX has 4 phases. Each one has clear entry/exit criteria – no hand-waving.

    • Phase 1 – Assess (Weeks 1–2): We map your SDLC workflows, find 1–2 motivated champions per team, and establish baseline metrics. Not surveys — actual time-on-task data.

    Deliverables: workflow diagrams, champion profiles, prioritized pilot shortlist.

    • Phase 2 – Pilot (Weeks 3–6): This is where the real work happens. Champions go through a hands-on loop – Capture current workflow → Spot AI integration points → Co-Implement with our engineers → Demo results to team. We run 2–3 pilots per engagement, each producing a breakthrough story with before/after data.
    • Phase 3 – Expand (Weeks 7–12+): Proven workflows scale to full teams. Champion-led knowledge sharing, role-specific playbooks, prompt libraries.

    Target: 60%+ team adoption of validated workflows.

    • Phase 4 – eXcel (Ongoing): For mature teams only – AI-DLC, multi-agent workflows, agentic CI/CD pipelines, custom tooling. Not everyone gets here, and that's fine.

    Security that works for regulated environments

    Three modes depending on your compliance needs:

    1. Sandboxed (default – standard AI tools with access controls),
    2. Strict (Privacy Mode, .cursorignore exclusions, Amazon Bedrock for private LLM inference)
    3. Air-Gapped (fully isolated, zero external AI calls).

    AWS integration

    We use Amazon Bedrock for secure private model inference, Amazon Q Developer for IDE-level AI assistance, AWS CloudTrail for audit logging, and IAM cross-account roles with temporary credentials via AWS STS. Your code stays in your environment.

    What makes this different?

    We don't do workshops-only engagements. Every engagement must produce measurable workflow change – or we stop and reassess. Built-in feedback loops at baseline, mid-point, and close. Champions own the change, not us. We focus on making the difference and scale it.

    Coverage: Developers, Tech Leads, QA Engineers, DevOps/SRE, PMs, BAs, Architects, UI/UX. Typical engagement runs 8–11 weeks with 1–2 FTE and on-demand experts from N-iX - a 2,200+ engineer technology partner with 22+ years of delivery across North America and Europe.

    Highlights

    • 40–60% net productivity gains – measured, not estimated. Every number is grounded in before/after tracking across real workflows: coding, testing, and DevOps. Results are baseline-validated and our champion-confirmed. If we can't measure it, we don't claim it.
    • Champion-led adoption: 1–2 motivated engineers per team co-implement, demo wins, and pull peers in — driving 60%+ sustained adoption through real results, not mandates
    • Full ownership of everything built: workflow cards, playbooks, agentic use-cases, prompt libraries, and breakthrough stories — a repeatable system your teams extend on their own, not a one-time engagement that disappears.

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    Deployed on AWS
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    Please reach out to awspartnership@n-ix.com  with any questions.