The Leap Method
Agents write the code. Engineers decide what ships.
How every Hyperleap project moves from a plain-English brief to software running in production, with each change traced back to what you asked for. It's the same process we use to build and run our own products.
Illustrative walkthrough of a client project.
From plain language to production.
- 01
Brief
We sit with the people who own the problem and write down what done looks like: the outcome, the edge cases, and acceptance checks in plain English. You sign it off before anything is built.
You get
- A written brief
- Acceptance checks
- A fixed price for the first sprint
- 02
Blueprint
Every part of the design points back to a line in the brief. We decide which model handles which task, what it costs to run, where data lives, and the rules for handing off to a person.
You get
- Architecture and data plan
- Model and running-cost plan
- Handoff rules
- 03
Build
Changes stay small enough for a person to actually read. Each one is traced to the acceptance check it satisfies, tested, reviewed by an AI reviewer and approved by an engineer before it merges.
You get
- Code in your repository
- A test for every acceptance check
- A change log traced to the brief
- 04
Run
We run what we ship the way we run our own products: monitored, evaluated against real conversations, and upgraded when a better model lands. What we learn becomes the next brief.
You get
- Monitoring and evals
- A runbook
- Model upgrades as they land
The rules our agents work by.
Speed is only safe when the process is written down. These come straight from the operating manuals in our own repositories.
One small change at a time
Each change touches a handful of files and stays under 200 lines, so a person can actually read it.
Two reviews before anything merges
An AI reviewer checks every change first. An engineer approves it against the brief.
Written operating manuals
Every repository carries the rules our agents work by, so quality belongs to the process, not to whoever is on shift.
Claims get tested too
Customer-facing copy runs through a check that blocks claims we can't support.
Tests that match production
Where two systems must behave the same, both are tested against the same golden outputs.
Agent-ready from the start
We've shipped MCP servers since May 2025. New products expose MCP from the first release.
Model-agnostic, tool-agnostic.
We pick the model for each job and switch when a better one lands. The work lives in the tools and infrastructure you already use.
- Anthropic
- Claude Opus, Sonnet and Haiku
- OpenAI
- GPT models, including bring-your-own-key
- Gemini models, including bring-your-own-key
Where it connects
Your repository
Every change lands as a reviewed pull request in a repository you own.
Your cloud
We deploy to Vercel, Cloudflare or Azure, or to the infrastructure you already run.
Messaging channels
WhatsApp, Instagram and Messenger through Meta's official platform, plus web chat.
MCP
We expose your software to AI assistants over MCP, with sign-in and permissions.
Agent tooling
Coding agents such as Claude Code and Codex do the drafting, under the rules above.
Three ways to work with us.
Sprint
One workflow, fixed scope, fixed price. Two weeks, and you see exactly how we work.
Scope a sprintProduct
A whole product, shipped in milestones and scoped with you, from brief to first paying customer.
Talk about a productRun
We host, monitor and improve what we built, by the month, and keep it current as models change.
Talk about running itBring us a comparable quote and we'll match or beat it.
Questions we get asked
How is this different from an AI agency?
Most agencies hand over a demo. We ship to production and stay on to run it, the same way we run our own products.
How long does it take?
Work runs in two-week sprints, and a first release usually takes a few of them. Bigger products are scoped with you.
How is it priced?
A fixed price per sprint, or by the month. Bring us a comparable quote and we'll match or beat it.
Who owns the code?
You do. It lives in your repository from the first commit.
Which models do you use?
Whichever fits the job, usually Claude, GPT or Gemini, and we switch when a better one lands.
Can it run in our cloud?
Yes.
What do we get at the end?
Working software, the brief it was built from, the tests that prove it, and a runbook for operating it.