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Quality & Operational Performance at Scale

“We’re shipping defects our own data should have caught.”

Connecting design, manufacturing and supply chain data into decisions — eliminating defects, modernizing the systems that hide them, and building engineering discipline that holds.

Almost every organization that ships a physical product already holds the data that would have predicted its worst defect. It is in warranty claims, test results, supplier quality records and field telemetry. What it is not in is one place, at one time, in front of the person who could still have done something about it.

So quality becomes a department that inspects rather than a property of how the work is done, and the same failure modes recur program after program — because nothing connects the evidence from the last one to the design of the next.

We connect those sources and then change what happens when they disagree with the plan. The measure of this work is not a dashboard. It is a defect that does not reach a customer, and a warranty line that comes down and stays down.

Where AI fits here

Includes the AI that genuinely pays here — pattern detection across warranty, test and field data — and the data quality it depends on, which is the layer most programs skip and then blame the model for.

The five levels of AI capability

What the engagement contains

The actual work.

Manufacturing Quality

Quality operating systems with real KPIs: warranty analysis binned by impact, DFMEA and PFMEA run against actual failure data rather than an assumed severity, and a feedback path that turns field failures into design changes instead of into reports.

Supply Chain

Supplier quality and performance where it is measurable — early warning on supplier software quality, recovery of what has been overpaid, and connecting supplier data to the design decisions that depend on it.

Systems Engineering

Requirements you can follow through to the test that proves them, across mechanical, electrical and software. This is the discipline ASPICE and ISO 26262 audit for, and the reason regulatory work is either tractable or a permanent tax.

Who does the work

The people who have already done this.

Envorso staffs engagements with operators who have held the job you are hiring for. Not analysts who have studied it, and not a junior team with a template.

  • Stuart Taylor

    Chief Executive Officer

    Deputy CEO for electronics and software at VinFast; 17 years at Ford

  • Steve Tengler

    Chief Growth Officer

    30+ years in automotive product development

  • Florian Frischmuth

    Senior Vice President, Digital Engineering

    Executive Director, Vehicle Controls, Ford

  • Sakis Kitsopanidis

    Partner, CIO Practice

    28 years at Ford and Ford Credit, including Interim CIO

  • Michael Dennis

    Partner, Enterprise Architecture

    30+ years in enterprise software — Disney, Microsoft, ExxonMobil; three patents

  • Keshav Puttaswamy

    Senior Vice President, Product Management

    25+ years — Microsoft, Atlassian, Meta

  • J. Caldwell

    Atlassian Solutions Architect and Practice Lead

    20+ years in enterprise IT operations — Disney, Expedia, AT&T Wireless; Atlassian-accredited

  • John McCauley

    Director, Portfolio Management

    16+ years in portfolio management — Deloitte, UMT Consulting Group

How it starts

Evaluate → Improve → Confirm. We drive.

Three steps, in order, every time — and an operator of ours driving all three. The first is deliberately small and deliberately purchasable, because nobody should commit to a transformation program before anyone has established what is actually wrong.

The other three

Two hours to find out whether we recognise your problem.

No deck, no obligation. We listen, we tell you whether we have seen this before, and we say what we think it would take. If a Jump Start is the right next step we will say so — and if it is not, we will say that too.