Delivery & Implementation
Turn strategy into
real outcomes.
We design and deliver modern product, platform and engineering capabilities, using agentic AI, secure by design and built for scale.
Made with AI. This image was generated with OpenAI’s image tools, made from our team’s own photos. We use AI in our daily work, and we say so when we have.
Our operators have run engineering at
- Ford
- Microsoft
- Disney
- FCA
- Meta
- Rivian
- 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
- JustAnswer
- Kugler Maag Cie
- Orange
- Ridemakerz
- United States Army
- Xbox
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 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.
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.”
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.”
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.”
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.”
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, with 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 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.
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
Read bioSteve Tengler
Chief Growth Officer
30+ years in automotive product development
Read bioFlorian Frischmuth
Senior Vice President, Digital Engineering
Executive Director, Vehicle Controls, Ford
Read bioSakis Kitsopanidis
Partner, CIO Practice
28 years at Ford and Ford Credit, including Interim CIO
Read bioMichael Dennis
Partner, Enterprise Architecture
30+ years in enterprise software — Disney, Microsoft, ExxonMobil; three patents
Read bioKeshav Puttaswamy
Senior Vice President, Product Management
25+ years — Microsoft, Atlassian, Meta
Read bioJ. Caldwell
Atlassian Solutions Architect and Practice Lead
20+ years in enterprise IT operations — Disney, Expedia, AT&T Wireless; Atlassian-accredited
Read bioJohn McCauley
Director, Portfolio Management
16+ years in portfolio management — Deloitte, UMT Consulting Group
Read bio
Proof
Engagements, with the numbers attached.
Across automotive, industrials, energy, government, consumer technology, and sports. Each one names what was actually wrong and what changed.
Automotive
$1.2BBuilt 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 organizationAutomotive
1,800 → 14Jira 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 manufacturerAutomotive
430From 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 OEMGovernment
800,000Smart Move
800,000 riders, one seamless pass
A national policy commitment to give 800,000 young people free bus travel via smart card, delivered through many partners, under parliamentary scrutiny, to a fixed date. The program had to satisfy a social mandate and a technical one simultaneously, and failure would be public.
A national government agencyIndustrials
−75%Building a Data-Driven Operating System
Reducing warranty claims by 75%
Warranty failure rates approached 100% during the coverage period. Key dealers were leaving, market confidence was eroding, and there was no instrumentation to explain any of it — no defined KPIs, no warranty analysis process, and therefore no way to tell which failure was worth fixing first.
A motorized equipment manufacturerIndustrials
12 weeksMobility Makeover
Clarity, control, and ROI
A Strategic Mobility platform launch was at risk, and with it a billion-dollar investment. The organization had no real demand management or product management capability, so priorities were misaligned, value streams were unclear, and resources were being allocated to work that was not moving the platform forward.
A global companyOriginal research
We publish our own data.
Original work, kept public and kept current. We publish it because an argument is more persuasive when the evidence behind it is checkable.
Since 1994 · 1,576 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 itSince 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 itA capability framework
The Five Levels of AI
Where your organization sits on the AI capability ladder, and why you cannot skip a layer to get to the next one.
Read itEvery 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 itWhere we work
The delivery problems are the same everywhere. Only the regulator changes.
Who we work with
Two hours to find out whether we recognize 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.
Training
The process discipline behind our engineering work, taught to your teams. Looking for intacs® certified Automotive SPICE® training rather than consulting?