PraxisIQFuseIQby PraxisIQ

Financial technology · Anonymized

Global Payment Security Technology Provider

Engineering bottlenecks became an actionable AI-native roadmap.

This was an assessment and roadmap, not a completed transformation. PraxisIQ mapped delivery constraints and aligned engineering leadership on what to pilot first.

Customer name withheld for confidentiality.

BottlenecksAssessPilot onePilot twoMeasured baseline
2
priority engineering pilots defined and scoped.
85%
roadmap alignment across participating engineering leaders.
1
shared AI engineering maturity model established.

Productivity and quality improvements from the recommended pilots are projected outcomes, not measured results.

The problem

Delivery friction the existing metrics did not capture.

  1. 01Reported quality metrics had drifted from how teams actually worked.
  2. 02Testing depended heavily on people repeating manual checks.
  3. 03Lead time was dominated by waiting across numerous repositories.
01What PraxisIQ changed

From assessment to a phased plan.

Working sessions traced where delivery time is actually spent, tested AI-assisted practices against real code paths, and produced a sequence leadership could commit to and measure.

  1. 01

    Assess bottlenecks

    Where delivery time is actually spent today.

  2. 02

    Identify pilots

    Two priority concepts chosen and scoped.

  3. 03

    Define practices

    Testing, review, and pairing workflows recommended.

  4. 04

    Establish measurement

    DORA metrics set as the baseline.

  5. 05

    Sequence roadmap

    A staged path leadership aligned on.

Under the hood
Method
Workshops with engineering and quality leadership, bottleneck mapping, and AI-assisted workflow demonstrations against real code paths.
Recommended practices
AI-generated testing alongside test-driven development, AI pair-programming and code-review support, and documentation-as-code.
Operating model
A staged maturity path from AI-assisted development toward agentic engineering, with an AI Center of Excellence proposed.
Technology referenced
Cursor, GitLab Duo, OpenAI Codex, and Miro.
02What comes next
Delivered today
An assessment, a shared maturity model, architecture and documentation recommendations, and an agreed measurement approach.
Planned next
Running the two selected pilots against the defined DORA baseline.
Projected
Any productivity or quality change is expected to be measured against that baseline rather than assumed.

FuseIQ

Find the next workflow worth changing.

See how PraxisIQ combines FuseIQ, forward-deployed engineering, and governed AI delivery around measurable operating outcomes.

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