You move fast in Cursor or Claude Code. Your team doesn't.The speed is real — it just lives in your head, not in a process anyone else can run.
For funded startups and mid-market SaaS teams
Deliver like a bigger team, without hiring one
Your AI moves fast. Your team still doesn't. That speed lives in your head — not in a process your team can run without you.
AI on Rails is an embedded AI-native delivery team running an open, git-native method — Business Analyst, Architect, Developer, and QA as one pipeline, with a person on every call that can't be undone.
Priced on what gets delivered, not hours worked.
Talk about your project
Tell us where you're stuck — we reply within one business day.
Read the open method
No form, no call. Install Praxis and run it against your own codebase.
Does this sound like you
This is probably why you're here
Your last AI prototype got you most of the way there, then stalled.Turning it into something you'd trust in production is a different job, and nobody on your team owns it.
You just closed a round, and growth is outrunning your delivery discipline.More scope, more pressure, more people to coordinate — and the same process gaps you had before the round closed.
A competitor on the same AI tools is already delivering faster than you.Same models, same market, same starting point — the only difference left is how fast each of you delivers.
Two products run through AI on Rails end to end — plus the open-source tool behind them. Measured, not projected.
Digital Wineryour own product, in production
−25%
production cost
+30%
operational efficiency
6 months
first requirement to MVP
Luxury Watcha client engagement, 9-person team over 5 months
2,000+
users
200–300
daily active
4,000+
member community migrated from
Praxisthe open-source tool behind it, applied to itself
Open source
Apache-2.0 — read every line
BA + Dev
the harnesses live today
4 roles
one pipeline, BA to QA
HOW IT WORKS
One pipeline, four roles, zero guesswork
Every request moves through the same lifecycle – the same file trail every time, so nothing depends on memory or a chat thread.
RAW REQUEST
BUSINESS ANALYST
ARCHITECT
DEVELOPER
QA
PRODUCTION
4
ROLES
1
PIPELINE
0
GUESSWORK
FIXED
PRICED ON DELIVERED OUTCOMES
Delivery without a method
handoffs + waiting
AI on Rails
scoped → priced → built → reviewed
See this pipeline run on your codebase
Tell us where you're stuck, and we'll show you what it looks like against your own stack — or install the plugins and run it yourself first.
What you get
What changes when AI on Rails runs your delivery
Four things hold on every request, no matter what you're building:
01
A finished feature, not a stack of half-built pieces
Every request runs through the whole pipeline, Business Analyst to QA, before it counts as done — what reaches you is something you can put in front of a user, not code you still have to finish yourself.
02
A file trail, not a person you have to track down
Every hand-off — scope, decisions, code — lives in your repository, so nothing depends on a Slack thread, an unwritten meeting, or a contractor who's already moved on.
03
A change that's already been checked, not a promise that it works
QA reviews everything before it reaches production, and that review becomes part of the file trail you keep, not a verbal sign-off.
04
The pace of a bigger team, priced like a project
Four roles run in parallel on your request, and what you pay is tied to what's delivered — not to how many hours it took to get there.
HOW WE’RE DIFFERENT
Beyond tools and headcount
AI tools help people work faster. Our method connects that work from the first request to a checked result.
01A team with a method
Business Analyst, Architect, Developer, and QA follow one shared process. You get a team that knows how to work together.
02Tools connected to delivery
AI helps write the code. Praxis connects it to clear requirements, agreed decisions, and checks before production.
03Results you pay for
Pricing is tied to what gets delivered. You agree on the outcome, rather than buying hours and hoping they add up.
04Knowledge you keep
Requirements, decisions, and checks stay in your repository. Your team can follow the work and carry it forward.
Cases
Measured in production, not projected
Two real products, run through the same pipeline. The numbers below are measured in production, not modeled or estimated.
Digital Winery
Paper logs → problems caught before they cost money.
By the numbers — measured in production, our own product
−25%
production cost
+30%
operational efficiency
6 months
first requirement to MVP
Challenge
Vineyard and cellar ran on spreadsheets and paper logs that never talked to each other — problems surfaced only after they'd already happened.
Solution
Business Analyst, Architect, Developer, and QA ran the whole build through AI on Rails, end to end.
Result
A working product in 6 months, full batch-level traceability, and a method proven on the team's own product before it was sold to anyone else's.
Luxury Watch
A trading community, finally built to handle real money.
By the numbers — a client engagement, 9-person team, 5 months
2,000+
users
200–300
daily active
~1,700
migrated from a 4,000+ member community
Challenge
A luxury-watch trading community lived inside a Facebook group never built for trading — no auctions, no real-time chat, no safe way to move money.
Solution
The same pipeline built web, iOS, and Android clients with real-time chat and verified dealer badges — on someone else's codebase, not our own.
Result
Delivered in 5 months, roughly 1,700–1,900 members migrated, and every hand-off left behind a file, not a memory.
Go deeper
Want the full technical detail?
This page sells the outcome. The method behind it is fully open — read the mechanics of every role, the verification rules, and every plugin on the technical site.
Already looked at the embedded-team offer here? Install the plugins and run them against a real task on your own codebase before you talk to us.
The team that delivers faster isn't smarter — it's just not waiting
Somewhere, a founder with your same AI tools already turned them into a process, not just a habit — and that gap compounds every week it stays open: a prototype that stalls before production, a hand-off that only lives in one person's head, a competitor reaching the market first from the same starting point you had.
You don't have to guess whether this works on your codebase. Tell us what you're building, and we'll show you what the pipeline looks like running against it.
Talk about your project
We reply within one business day.
FAQ
Questions worth asking
Why not just hire our own team?
Hiring and building delivery discipline from scratch takes months most funded startups don't have. A team built without a shared method reproduces the exact problem you're trying to escape — headcount growing faster than the discipline holding it together.
We already use Cursor, Claude Code, or Copilot — why not figure it out ourselves?
The model isn't the gap — everyone has it. The gap is the process around it: which requirement you're building, what "done" means, how it fits the rest of your system. AI on Rails is that process, running as open plugins on the assistant you already picked. Install it and try it before deciding anything.
How is this different from a dev agency or staff-augmentation?
Hours buy you time, not a result. When the engagement ends, the knowledge leaves with the people who did the work — not in files you own. We work embedded, priced on the outcome, and every hand-off is a file in your repository you can open and check.
When is this actually relevant for us?
Usually one of four moments: your team has hit the ceiling of what one person with one AI tool can produce; a fast AI prototype isn't making it through production; a fresh funding round means growing faster than your delivery discipline; or a competitor is already delivering faster with an AI-native process.
How much does this cost?
Outcome-based fixed price for a defined project, or a dedicated embedded team for ongoing work — priced on what's delivered, not hours logged. Tell us about your project and we'll size it.
