Most "AI-first" providers sell the speed of a demo. MAE sells speed that survives to production — no rewrites, no hidden technical debt, no budget spent on code nobody understands six months later.
Where MAE Fits in Your Journey
We diagnose and steer; the decisions and the outcome stay yours. This is where Discovery sits — a fixed-scope, fixed-price stage where we close the key risks and unknowns together and agree exactly what will be built, before development starts.
What This Means for You
Architecture is approved before code is written, not after. Expensive rework is caught at the plan stage instead of weeks into development.
Every project is led by a senior full-stack engineer who makes the architectural decisions — not a prompt operator.
A classic two-week sprint compresses to a few days, because the agent takes over execution, not judgement.
Architecture notes and docs are updated in the same pull request as the code, so they are current by definition.
Every key step passes an engineer's review before anything moves forward.
How It Works
A Pod, Not a Set of Roles
Product management
0.5 FTE Product Owner
Development
1.0 FTE Senior Full-Stack Engineer
QA
AI QA agents (0.5 FTE) — two agents cover the cycle: one generates test cases from the specification, the other runs them before the engineer sees the result.
Tooling / skills
Included under the hood
Total
2 FTE
Product management
0.5 FTE PM + 0.5 FTE Tech Lead
Development
1.0 FTE Frontend + 1.0 FTE Backend
QA
1 FTE QA Engineer
Tooling / skills
—
Total
4 FTE
in calendar time, vs a classic 4 FTE team of the same scale
per feature, vs a classic team of the same scale
Why Discovery Closes the Risk
The most expensive mistake in an AI project is not a bug. It is a decision made silently: the agent filled in an API contract because nobody defined it, or assumed a scope that later had to be reworked in live code.
Discovery exists so those decisions are made by people, deliberately, before development starts.
Why It Works — and Why It Is Not About the AI
MAETON: the Methodology Packed Into a Starting Skeleton
MAE is the methodology. MAETON (MAE + skeleton) is how it starts on day one: a ready technical foundation on OTAKOYI's canonical stack, with auth, multi-tenancy, billing, queues, notifications, file storage and a basic admin panel already built in.
A new project therefore starts roughly 70% complete at the foundation level. You pay to build what makes your product different, not to reassemble authentication and billing that have been built and proven already.
MAE Is Not Another Specification Format
Tools exist that help structure technical specifications for AI agents. They solve a narrower problem: how to write requirements so an agent understands them better.
MAE is not a document format. It is the infrastructure that format lives in:
The specification is one artefact inside that system; without the rest, it drifts from reality like any other document.
When Agentic Development Delivers, and When It Does Not
Definition of success
The task has a measurable success criterion agreed before the start
Shape of the work
The work splits into small self-contained modules, each verifiable on its own
Your involvement
You are ready to invest time in Discovery and to sit the review checkpoints with us
The stack
The project can run on a managed stack with an existing pattern library, such as MAETON
Definition of success
The task is research by nature and success cannot be stated as a threshold in advance
Shape of the work
The decision needs an architectural trade-off nobody has made before — that calls for human judgement, not execution speed
Your involvement
You expect full autonomy with no human in the review loop. Production quality needs a human in the cycle; that is a deliberate MAE choice, not a temporary limit
The stack
The stack sits far from a managed pattern library — most of the speed advantage goes into learning the codebase instead of building the feature
Explore how we turned our clients' ideas into successful, market-ready products.
The agent writes code; the engineer decides what gets written and verifies each module against the specification before it merges. Nothing reaches production without an engineer's approval at a checkpoint.
The key to a successful project is a strong business idea backed by real market need, a solid tech solution, and a clear go-to-market plan.
