AI made coding cheap. Building the right thing is now the hard part.

I help tech leaders adopt AI across the SDLC with a method: model the system first, then let AI build it. Hands-on workshops from an architect who ships, not a coach with slides.

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Fig. 1 · Functional flow · Customer purchase
ACTORS ACTIONS METRICS TECHNICAL COMPONENTS Customer Payment gateway Proceed to checkout Reserve items Collect payment Cart abandonment rate Shopping cart svc Order manager
Align · who Product and tech leaders, in the same room.
Build · outcome Functional tickets turned into reviewed, deployed code by AI agents with guardrails.
Author
Moisés Macero · Forward Deployed Architect · Tech Lead
Method
Creator of the MOSY method
Track record
20+ years shipping with teams · 4 books, 10,000+ readers
Sheet
1 of 3 · Align
the method
Your situation

Which of these sounds like your team?

Tap a note: the finer versions of that problem unfold next to it, and each one takes you to the part of this site that deals with it.

Six of eleven. See all of them on the board. start from your problem

AI didn't remove the bottleneck. It moved it to deciding what to build.

Writing code used to be slow. That slowness forced you to build a little, look at it and correct. AI took that pause away. The risk now isn't bad code; it's fast code nobody needed. The one checkpoint left is the model: a clear picture, agreed with the business, of what is worth building. With a good model, AI speeds up delivery. Without one, it speeds up waste.

See the MOSY method
With a model, AI speeds up delivery. Without one, it speeds up waste.Fig. 2 · The one checkpoint leftAn ideaThe checkpointA model, agreed withthe businessAI agents build fromitWhat was worthbuildingAI builds straightawayFast code nobodyneededmodel it firstfaster deliveryskip the modelfaster waste
who am I

My method, in three workshops

Model the system, then let AI build it.

For tech leaders who want AI in the whole development cycle, not only in the editor. Online and self-serve, with an invoice you can charge to your training budget.

  1. Who Product and tech leaders, in the same room.

    Align System Modeling

    You leave with a model of your system that product and engineering both trust: the input AI needs to build what the business asked for.

  2. Who Architects and developer reps.

    Design From Model to Architecture

    You leave with an architecture mapped straight from that model: components, boundaries, and the service decisions made, whether you start from scratch or refactor.

  3. Who Development teams.

    Build AI-Assisted Delivery

    You leave with a working delivery loop: functional tickets that your AI agents develop, you review and ship, with guardrails at each step.

Align first (its model feeds the rest), then Design and Build run in parallel.

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Two clients, before and after

Sympower, Energy Sector ~50 employees

How the team starts out

A startup entering the scale-up phase, but unable to scale its platform, and therefore its teams, because of a monolithic architecture.

  • The infrastructure was replicated per client, with no support for multi-tenancy.
  • Although there were many plans to evolve towards a more modular platform, they stayed stuck and never made it into the roadmap, because no resources were allocated to them.

The result after working with Mosy

I introduced a visual architecture workflow the team could act on. Communication with stakeholders improved and the teams were reorganized. Complex components moved behind APIs, which cut cognitive load and let the company grow past 100 developers.

Core challenge

Transforming a monolithic, client-replicated architecture into a scalable platform with reusable components.

Key solutions

Domain-Driven Design, pragmatic extraction plans, Anti-Corruption Layer, Strangler Fig Pattern, and Software Architecture Facilitation.

Dutch Fintech startup funded by ING ~70 employees

How the team starts out

Improvised software architecture with decisions pulling in opposite directions. A hard-to-maintain monolithic integration layer caused team-collaboration issues. Lack of a clear technical vision to split B2C and B2B business strategies.

The result after working with Mosy

The legacy monolith was decomposed using a pragmatic approach, enabling parallel work. I applied Domain-Driven Design to place the B2C application as a client of the B2B platform, which allowed scaling the business.

Core challenge

Lack of technical vision and alignment across teams.

Key solutions

Architecture Community, B2B technical strategy, Microservices and Event-Driven architectures.

Put AI to work on the right things.

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Let's talk about your team

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