RESULTS
Real engagements. Real outcomes.
Not a promise of what you'll get - a record of what we've done. Every case follows the same pattern: a baseline set, capabilities built, results delivered, ownership handed to your teams.
OUR CONSULTANTS HAVE WORKED WITH TEAMS AT
Our track record
Since 2021, across manufacturing, automotive, financial and telecoms organizations.
10+
clients in 5 countries
50,000+
users working on platforms we optimized
300
teams on one streamlined, scalable tool suite
200+
integrations and automations delivered
1 month → 2 hours
to deploy a new environment (99.8% faster)
25%
lower operating expenses
30%
fewer delayed programs
24
contractors hired by our clients
Case studies
30%
fewer delayed programs
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​CLIENT
Global automotive manufacturer (top 10)
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SCOPE
10,000+ users across business units · 2 years
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DOMAIN
Ways of working
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SERVICE
Portfolio Management Accelerator
Enterprise portfolio management
A top-10 global car manufacturer whose delivery had outgrown its tools.
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The challenge
Siloed operations. Teams used tools in ways that didn't scale, overhead kept growing, and dependencies were overlooked until far too late, causing serious delays.
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What we did
Over two years we introduced a portfolio management solution that gave the organization a clear view of its investments and development work. We consolidated scattered backlogs into one shared space mapped to the product list, so leaders and teams could prioritize or drop work by importance, and gave all 10,000+ users a playbook for managing dependencies and risks.
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The outcome
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About 30% fewer delayed programs
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20 backlogs consolidated into 1
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One way of working, with better communication and traceability
25%
lower operating expenses
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​CLIENT
Automotive enterprise
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SCOPE
6 business units
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DOMAIN
Ways of working
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SERVICE
When misalignment became a cost line
Six business units, six ways of working, one delivery landscape.
The challenge
Every business unit ran its own playbook. Handovers stalled, the same work got done twice, and no two teams reported progress the same way. Good engineers left, taking delivery knowledge with them, and the cost showed up in the operating budget.
What we did
We evaluated several models and landed on hub-and-spoke. After leadership sign-off we rolled it out across all teams: the hub owns and defines the core practices; the spokes follow them but keep freedom over how they work day to day.
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The outcome
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About 25% lower operating expenses
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Governance and transparency across business units
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Less duplicated work, less admin overhead, lower turnover
Facts-based decisions
1 month → 2 hours
to stand up a new ALM environment
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​CLIENT
Large manufacturer
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SCOPE
Regulated industry
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DOMAIN
Connected factory platforms
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SERVICE
Platform & ALM Health Check → Connected Platforms & AI Implementation
When governance became the bottleneck
When teams move at AI speed, the governance stack can't take a month.
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The challenge
Every new ALM environment (the tooling that carries requirements, code and testing through to release) took up to a month to provision. The stack was expensive, overlapping, and had no tested disaster recovery.
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What we did
We mapped the stack and cut the overlap. What remained moved to public cloud, deployed with configuration-as-code so an environment is defined once and rebuilt on demand, and re-architected for scale and audit-ready security.
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The outcome
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New ALM instance in a couple of hours instead of a month
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About 30% lower administration and licensing costs
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Tool performance up about 50%
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Tested disaster recovery for every tool; security aligned to ISO 27001
Value-first
backlog prioritization driven by measurable OKRs
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​CLIENT
Industrial manufacturer
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SCOPE
Rolled out from one business unit
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DOMAIN
Strategy & transformation
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SERVICE
Measurable objectives: why before what
Building is no longer the bottleneck. Deciding what to build is.
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The challenge
Teams were measured on activity, not outcomes. Every board was full, and nobody could say which work moved the business. AI makes this harder: when teams can produce more of anything, the constraint shifts to judgement.
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What we did
We asked teams and leaders how they really decided what came next. We implemented OKRs in one business unit, proved them there, then rolled them out, connected to the work-management platform so every objective carries live delivery status.
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The outcome
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Prioritization moved from output-driven to value-driven
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Leaders see how decisions land against customer needs and revenue in real time
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Backlogs down by more than 50% for some teams
200+
integrations and automations delivered
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​CLIENT
Industrial organization
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SCOPE
Shop-floor and enterprise data
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DOMAIN
Connected factory platforms
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SERVICE
One source of truth across the enterprise
AI can only answer from what's written down. Most of this knowledge wasn't.
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The challenge
Tracking, documentation and know-how sat across many disconnected tools. Finding anything meant searching by hand or asking the one person who knew, and knowledge walked out with every departure.
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What we did
We consolidated onto a single Atlassian platform and built 200+ integrations and automations across the toolchain. Then we structured the knowledge base so information can be found by a person or a machine.
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The outcome
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Total cost of ownership down more than 50% by retiring tools and automating manual steps
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Teams find what they need instead of chasing it
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Knowledge captured in a form both people and AI tools can use
One trusted view
real-time insight behind every decision
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​CLIENT
Industrial manufacturer
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SCOPE
BI dashboards on agreed data
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DOMAIN
Strategy & transformation
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SERVICE
From gut feel to facts: a real-time overview
A manufacturer whose decisions were driven by activity, not value.
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The challenge
Every meeting started with an argument about whose numbers were right. Each team collected data its own way, so decisions defaulted to whoever was most confident in the room.
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What we did
We picked the metrics that carried business value, centralised the sources behind them and mapped them to newly defined OKRs. Then we built dashboards for leadership, teams and delivery, defined the data practices and trained the people entering the data.
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The outcome
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Live view of committed vs delivered, release roadmap, objective progress and dependencies
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No one maintaining reports by hand
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The arguments about numbers stopped
Feasibility-first
AI use cases costed, sequenced, then delivered
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​CLIENT
Manufacturing organization
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SCOPE
First pilot already shipped
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DOMAIN
Manufacturing AI
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SERVICE
AI Readiness & Opportunity Map
From AI ambition to AI that pays off
Picking the right use case is most of the work. Building it is the easy part.
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The challenge
Leadership was under pressure to "do AI" but couldn't tell real opportunity from noise. The risk was funding pilots that demo well and quietly die.
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What we did
We mapped data readiness, scored every use case on business value and feasibility, and sequenced a costed pilot plan the board could fund. Then we proved it on the first candidate: making the company's engineering knowledge searchable by AI.
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The outcome
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An open-ended ambition became a prioritised, fundable roadmap
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The first pilot already pays for itself
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Engineers find prior work in seconds instead of hours
FOR CONSULTANCIES & LICENSE RESELLERS
Outcomes we create together
When partners build with Agile Nest, shared clients scale faster, spend less and grow the partner ecosystem.
4×
licence growth in under 2 years
One client scaled from 2,000 to 8,000 licences by consolidating onto a single tools ecosystem.
~$20M
saved per year
A client's entire development pipeline re-engineered end to end, cutting annual operating costs by about $20M.
5
new partners onboarded
Five new partner companies brought into one client's ecosystem in a single year.