We transform technology delivery — and prove it

We help IT, Platform & Engineering leaders turn complex organizations into faster, more predictable delivery engines. We work alongside your teams to identify what is holding delivery back, make the changes that matter, and prove the impact in speed, quality, cost, and predictability.

Led by people who have built and run technology and engineering organizations at Ford, Meta, Microsoft, Rivian and many others.

Start with a two-hour discovery Or a Jump Start →

$1.2B: contract won after a delivery turnaround
1,800 → 14: Jira projects consolidated into standard templates
$20M: supplier overpayments recovered in 18 months
430: engineers unified across 8 sites post-acquisition

Our operators have run engineering at

Ford
Microsoft
Disney
FCA
Meta
Rivian
Xbox
Seattle Seawolves
empwr.ai
Tesla
SpaceX
Sony
ExxonMobil
Aston Martin
Land Rover
Lockheed Martin
HSBC
NBC
Expedia
Honeywell
AT&T Wireless
Atlassian
Lucid Motors
Lincoln
Ford Credit
Magic Leap
OnStar
Toyo Tires
Daewoo
VinFast
Canoo
Faraday Future
Webasto
Autel Energy
Autonomic.ai
Altia
Pcubed
UMT Consulting Group
BlackBerry
Chariot
Continental Automotive
Deloitte
Funko
Harman
IonQ
JustAnswer
Kugler Maag Cie
Ridemakerz
United States Army

Agentic AI

Most AI programs don’t fail on the technology.

They fail because the organization underneath them cannot support what was promised — the data is not good enough, the roles are unclear, nobody owns the outcome, and there is no honest measure of whether it worked. We assess which of the five levels of AI capability you are actually operating at, work out which level the business goals you are chasing actually require, build the layer you skipped, and measure the return against targets set before the work starts.

You cannot skip the layers. Most organizations are stuck on level 1 or 2.

Find your level And the AI company we built

The five levels

1
Machine Learning
Analyzes and predicts
2
Neural Networks and Deep Learning
Recognizes patterns at scale
3
Generative AI
Creates content and code
4
AI Agents
Executes multi-step tasks
5
Agentic AI
Orchestrates entire processes

What we fix

Four problems. Said the way you would say them.

If one of these is the sentence you have used in a leadership meeting this quarter, that is the page to read.

“We can’t see what we’re spending, or whether it’s the right work.”

AI capabilityLevel 1Level 5

Includes AI budgeting and eval-driven measurement, so AI spend competes for portfolio funding on the same evidence as everything else.

Strategic Portfolio Management

Aligning investment with enterprise strategy — shifting from project-based funding to outcome-driven portfolio governance, and connecting financial efficiency, resource allocation and lean governance across the enterprise.

“Our products are late and the quality isn’t there.”

AI capabilityLevel 2Level 3Level 4

Includes AI in the development toolchain and in the product itself, and the agentic-first way of working that the teams above are built for.

Product & Technology Delivery

End-to-end delivery from concept to launch — the product operating model, cross-functional teams, CI/CD, Agile enablement, and a toolchain configured to carry it.

“I’ve inherited an IT estate I can’t explain to the board.”

AI capabilityLevel 1Level 5

Includes where AI belongs in the estate and where it does not: which applications are worth modernizing, which are worth retiring first, and what has to be true before an AI program is worth funding at all.

CIO & IT Advisory

Strategy, operating model and application portfolio for technology leaders — so the estate can be explained, defended, and changed.

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

AI capabilityLevel 1Level 2Level 3

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.

Quality & Operational Performance at Scale

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

How we work

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.

01Scoped in a conversation
Evaluate
We start from what the business is trying to achieve, then assess your team’s talent, ways of working, tools, org structure, portfolio strategy, and where you genuinely sit on the five levels of AI capability — and tell you which of those is actually standing between you and the goal, whether or not you hire us to fix it.
02As long as the work takes
Improve
Our operators embed and do the work. Not a slide deck with recommendations — architecture, governance, hiring, migration, coaching, whatever the assessment says is required.
03Agreed up front
Confirm
On time, on budget, on content. We agree the measures before the work starts and report against them, so the improvement is a number rather than a feeling.

Start with a two-hour discovery Or a Jump Start →

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.

All 31 operators and advisors

Proof

Engagements, with the numbers attached.

26 case studies across automotive, industrials, finance, energy, government, consumer technology, and sports. Each one names what was actually wrong and what changed.

Automotive

$1.2B

Built to Deliver, Proven to Transform

Unlocking $1.2B in new business

Delivery dates were missed often enough that customer confidence had become a commercial problem rather than an engineering one. Defects were surfacing in late-stage testing, contractual penalties were being paid annually, and the organization was competing for a major new contract it was not, on its record, credible to win.

A major automotive engineering organization

Automotive

1,800 → 14

Jira Reinvented

From chaos to clarity

Eighteen hundred custom Jira projects had accumulated over years, each configured by whoever needed it at the time. No two behaved the same way, no change could be made without an unknown blast radius, and assembling a portfolio report took six weeks of manual work — by which point it described a company that no longer existed.

An electric vehicle manufacturer

Automotive

$20M

Code to Component

Powering profits with digital precision

Digital parts content ran on a legacy system that was inefficient, error-prone, and — critically — could not produce an audit trail. That made compliance expensive, scaling impossible, and supplier billing effectively unverifiable.

A global OEM

Automotive

7×

Test. Commit. Win.

A factory of software excellence

Software quality depended on manual testing performed late, by people who were already the constraint on everything else. There was no engineering services function, no shared toolchain, and no way to coordinate release trains across teams that were nominally agile and practically sequential.

A global automotive company

Automotive

430

From Chaos to Code

Unifying 430 engineers across 8 sites

The acquisition of 430 embedded development and hardware engineers from BlackBerry, spread across eight North American sites, delivered talent but not capability. Processes were inconsistent site to site, integration was more complex than modeled, and the acquired teams were not aligned to the strategic objectives that had justified buying them.

A major OEM

Consumer Technology

100+

Solve It Once

How a recurring client problem became empwr.ai

Across engagements with automotive OEMs, Tier 1s, industrial manufacturers, consumer goods companies and silicon providers, we kept finding the same failure. The knowledge needed to run a large program existed, but only in fragments — in meetings nobody captured, in tools that did not talk to each other, and in the heads of whoever happened to be in the room.

Envorso — internal innovation, since spun out

All 26 case studies

Original research

We publish our own data.

Four pieces of original work, kept public and kept current. We publish them because an argument is more persuasive when the evidence behind it is checkable.

Since 1994 · 1,565 recalls · 115.5M vehicles

Vehicle Software Recall Statistics

A proprietary dataset of every software-related vehicle recall since 1994, updated as new recalls are issued.

Read it

Since 2003 · 4,239 recalls · 1 in 12

Medical Device Software Recalls

The same question asked of the FDA device recall database — how much of it is software, and where in the lifecycle the defect was introduced.

Read it

A capability framework

The Five Levels of AI

Where your organization actually sits on the AI capability ladder, and why you cannot skip a layer to get to the next one.

Read it

Every source labeled by who paid for it

Atlassian ROI Research

What the evidence actually shows about the return on Atlassian at enterprise scale — including the research that cuts against the case.

Read it

Where we work

The delivery problems are the same everywhere. Only the regulator changes.

Finance: No case study published yet. We reach finance through the operators rather than a filed engagement — a board director at HSBC, and a former Interim CIO of Ford Credit. The bench

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.

Start with a two-hour discovery Or a Jump Start →

Training

Looking for intacs® certified Automotive SPICE® training rather than consulting?

See the course catalog →