Consulting, delivery, and fractional leadership for businesses of every size
Your strategyisn't shippingitself.
A plan with nothing shipped is a document, not evidence, and it invites cost overruns and missed revenue. We start with a business question and build the first working piece.
AI is the new norm. The problems worth solving are as old as business: maximize revenue, minimize cost, improve quality, move faster, and differentiate.
01Approach
Every project ends with something running.
Many consulting projects end with a recommendation and a page count. Ours end with demonstrable deliverables and, where it fits, viable code running against your data.
We start with a business question, then size its value and the data it needs. The strongest case for investing is a working deliverable, built for your situation.
02Impact
What we have built.
Three recent projects, and what each one changed
Program options, simulated before the spend
A healthcare delivery client was choosing between several ways to run a proposed program change, with millions of dollars in cost savings resting on which one they picked. We modeled multiple simulations, varying the cost drivers and the acceptance rates the savings actually turned on. They funded the one that held up, and they knew before starting what it would cost and which assumption would sink it.
Manual routines turned into running software
One pattern repeats: a manual routine becomes a small application the whole organization then uses, and AI tooling now gets it built in a fraction of the time. Tooling we wrote ourselves has cut provisioning and profile verification by ninety-five percent, content compliance checking across thousands of pages by eighty-five percent, and deployment time by half. A first project is often this small, and the below-the-line cost savings accrue quickly.
Agentic assisted delivery
A client came to us with an application build quoted above three hundred thousand dollars and a schedule measured in months. We ran the delivery with agentic assisted engineering and it was production ready in twelve weeks at a small fraction of the budget. The agents did the volume work. We brought the judgment about what to build, and senior delivery experience kept it production quality and ready to scale.
03Complexity
Your business problems are never only technical.
Decades of delivery have taught us that what’s blocking progress is rarely what the status report names. We work through all of it, in the order that matters.
Technology
What is actually running, what still depends on it, and what it costs to keep alive.
Culture
What gets rewarded, what gets quietly ignored, and what nobody will say in the meeting.
Habits
The workaround that became permanent, invisible now to everyone who built it.
Process
Steps that survive because of a problem somebody solved years ago and nobody has revisited.
Strategy
Whether the plan on paper still matches what the business actually needs this year.
Market
What customers will pay for, and what competitors have already made table stakes.
Delivery context
What your teams are already committed to, and what that realistically leaves for anything new.
04Questions
Which of these is yours?
If one sounds familiar, it is worth a conversation.
01
You built something other companies would pay for. Is platformizing it worth the cost?
02
Your teams are fully committed. Who is preparing the thing that is six months out?
03
You have an AI or modernization strategy. How much of it has actually shipped?
04
Are you caught in a cycle of stockouts one quarter and overstock the next?
05
Is a deliverable off track, carrying unresolved issues, and not ready for production or scale?
06
Are you an AI strategist or architect, or about to hire one, without being crystal clear on what the role should deliver?
07
You are sitting on years of data. Do you know which part of it could grow revenue or cut cost?
05Work
What we take on.
01
First working piece
A rough idea becomes a deliverable quickly, tested against your data.
02
Accelerator adoption
We specialize in getting published accelerators into production fast, adapted to your cloud, your data, and the part of the system only you have.
03
Platformization assessment
We help you assess, plan, and budget what it takes to bring internal software to market.
04
Make or buy
Build it, buy it, or leave it alone. We surface the integration and scaling costs that usually decide it, not the features.
05
Data put to work
Modeling, optimization, and measurement wired in before the spend, so decisions rest on real numbers, not another dashboard.
06
Duplicative inventory reduction
Systems you replaced but never switched off. We take them off the bill.
07
Back on track
A project that stalled short of production. We get it to where your team can carry it.
08
Fractional leadership
Senior product, technology, or AI leadership, part time, until the role is ready for a full hire.
DomainsProduct development · Agentic workflows · Supply chain and inventory · Systems integration and reliability
06Principles
How we work.
01
Go simple. It'll complicate itself soon enough.
Start with the plainest thing that could work. Once it is running, most of the real solution reveals itself on its own.
02
We'd rather bet on your people than on a tool.
Tools get replaced every few years. The leaders and engineers who understand your system do not. We do not build dependency: we teach as we go and hand over clear documentation and an honest assessment of what we built. The knowledge stays after we leave.
03
Plan the work, work the plan.
A plan sets the baseline. Executing a piece of it shows you what is true. Every stage ends with something documented or something working.
04
Be prepared. Always scout.
Know the terrain before you commit the budget: dependencies, owners, data quality. We lay out clear options and the risks of each, and an early prototype shows you which one holds up before you decide.
05
Risk is good. Manage the mitigation.
Avoiding risk is how organizations stop moving. Name the risk, size it, plan the way back, then take it deliberately rather than by accident.
06
Demonstrate it.
Design and ideas hold little value until someone can use them. Show the working thing. Every argument gets shorter once it is on screen.
07AI
AI is an enabler. It doesn't replace people.
Our team was part of early cloud development and is part of early AI adoption now. The arguments and obstacles of a decade ago are surfacing again. Having seen how they played out, we can make progress through the noise, churn, and uncertainty.
Impact
AI should make your people more effective at the work only they can do. If it is not doing that, it is only another subscription.
Responsible use
Know what the system was trained on, what it is allowed to touch, and who reviews the output before it reaches a customer. All of it has to meet your ethical standards, your internal governance, and your industry’s compliance rules.
Security and vulnerability
New capability is new exposure. Data handling, model access, and prompt paths belong in the threat model from the beginning.
08Tech for good
Some projects matter more than the invoice.
Getting AI and technology to the people who need it most is a personal commitment for us. If your nonprofit or business serves people who are underserved or disadvantaged, get in touch! We are convinced we can help, and we will find a way to make the project work.
09Fit
Different scale, same discipline.
Global enterprises, startups, and public sector. What matters is the problem, not the headcount.
The common denominator across all of them: define the business problem, lay out the viable solution options, and build the right one.
10Martin
Martin Haase
Founding Partner
Over thirty years of building the thing rather than describing it.
Martin spent sixteen years at Microsoft, first building products and most recently as Senior Director, working with hundreds of customers through modernization. Before Microsoft, Martin worked across a range of industries in data science, policy analysis, product development, and consulting, co-founding two companies and building a product that was acquired.
Practice areas
- Delivery and modernization
- Data science, decision systems
- Product strategy and make-or-buy
- Platform and go-to-market readiness
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11David
David Nguyen
Partner
Fifteen years building enterprise applications end to end.
He works the whole stack rather than a slice of it. That is what makes a small practice viable: one person who can define the schema, write the service, build the screen, and own the pipeline that ships it.
Practice areas
- Full stack application development
- APIs, services, and integration
- Database design and performance
- Test automation and quality engineering
- Deployment pipelines and release
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