(nai™ — 01)
Our Approach
A distributed bench of senior engineers, building
the systems that run day-to-day operations.
Who
We Are (™)
(nai™ — 02)
Built for Real Work
How
We Think
The method is the product here: how a build
runs is decided before anything is built.
We map how the operation actually runs, then automate only the parts that pay for themselves in production.
The audit names what the work costs and where it slows,
the design keeps a person over the big calls,
and the build ships only what we can watch live.
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WHAT WE DO
Four lines of work that share
one way of building.
Automation, software, commerce and mobile —
built by the same people, to the same standard.
Practice 01
AI automation
The repetitive half of an operation, handed to systems that do not tire.
Practice 02
Custom software
Internal tools, APIs and platforms built around how the business runs.
Practice 03
Shopify and Hydrogen
Headless commerce, where the speed of the page is the conversion rate.
Practice 04
Mobile apps
iOS and Android, wired to the systems the rest of the business runs on.
A studio this young has no track record to trade on, so we publish the prices, the method and the commitments instead.
Nukes AI
How we ask to be judged
7+ yrs
minimum, every engineer
Facts
Service lines
Years per engineer
Audit to production
Reply to enquiries
(nai™ — 04)
Commitments
Four things we commit to in writing
before an engagement starts
Not a track record — we do not have one yet.
These are the terms instead.
001.
Commitment · 01
Senior only
Every engineer on your build has shipped
production systems for five years or more.
002.
Commitment · 02
No layers
No account manager relaying questions.
You talk to the people writing the code.
003.
Commitment · 03
Priced in the open
A published starting price and a written
scope, agreed before any work begins.
004.
Commitment · 04
We stay on
Launch is the start. We monitor what the
system decides and retune it as work moves.
What
Drives Us (*)
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Inside The Work
What shapes how we think, build and
run systems inside real operations.
Talking through the flow
Where the work starts and stops
Explaining the system
Showing how the automation decides
Working through the edges
Deciding what the system must not do
Our work is shaped by how
real operations actually behave
We do not build AI to look impressive. We
work on systems where execution matters —
where decisions carry weight and workflows
have to hold up in production.
Designing the logic
Breaking a workflow into steps that hold
Agreeing on execution
Coordinating the rollout across teams
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Our bench
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Insights & Research
Recent articles
Notes on automation, commerce and the software that has to carry both.
Aug 28, 2026
in /
Automation
Why operational automation stalls six months after launch
Aug 14, 2026
in /
Commerce
Moving a Shopify store to Hydrogen, and what actually changes
Aug 1, 2026
in /
AI systems
An assistant is only as good as what it is allowed to read
Jul 22, 2026
in /
Automation
The exceptions are the work, not the edge case
Jul 9, 2026
in /
Architecture
Nobody wants to pay for data work, and everybody pays for it
Jun 26, 2026
in /
Commerce
On a storefront, performance is not a technical concern
Jun 17, 2026
in /
Architecture
Build the field app for the worst signal it will ever see
Jun 5, 2026
in /
Strategy
When the spreadsheet is load-bearing, buying will not help
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Our Newsletters
Occasional updates. No noise, and no sharing.
No hype. Just systems
Clarity beats automation
Decisions over demos
Designed for messy reality
Systems that hold under pressure
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Frequently Asked Questions
Questions
that count
A plain set of answers on how we build,
ship and run software and automation.
Both, and usually in the same engagement. Nukes AI runs four practices — AI automation, custom web and backend software, Shopify and Hydrogen commerce, and mobile apps — because an automation that has nowhere to write its result is not finished, and a storefront that nobody can operate is not either. The four are staffed by the same people to the same standard.
No. The model is one layer of a system that is mostly ours: retrieval that reads your own records, a policy layer that decides what an agent may act on, and an execution path that writes back into the tools you already run. Answers are grounded in retrieved records and checked before they are used, and where confidence is low the system escalates instead of guessing. Swap the model out and the system keeps working, because the reasoning about your business does not live in the prompt.
Yes. We integrate through the APIs, databases and internal services you already have, and we adapt the architecture to your stack rather than asking you to adopt ours. Nothing here expects a migration, and nothing here expects your team to work outside the tools they know.
Security is an architectural choice made at the start, not a review at the end. Access is scoped per agent, storage is encrypted in transit and at rest, and processing runs in isolated environments under your own retention rules. We work to India’s Digital Personal Data Protection Act 2023 and will sign whatever your own regime adds on top of it. Your records stay yours, and every read an agent makes is logged.
Discovery and strategy starts at $2,500 and takes two to three weeks; system design starts at $6,000; a full deployment and integration starts at $12,000 and typically runs four to eight weeks. The prices are on the site because the alternative is a discovery call whose real purpose is working out what you can afford. Scope is agreed in writing before anything starts.