(nai™ — 01)
AI AUTOMATION AND SOFTWARE
We take the cost out of how work runs — with automation, custom software and Shopify builds that hold up once real volume lands on them.
Automation that cuts cost
Custom web, backend, mobile
Shopify Hydrogen storefronts
Senior engineers, no juniors
Built for real operations
Shipped, measured, maintained
100%
senior engineers only
(nai™ — 02)
WHY OUR SYSTEMS HOLD
Most AI collapses the first time it meets genuine operational complexity. We build systems that keep deciding while workflows, data and people shift underneath.
Lead Engineer
Nukes AI delivery team
(nai™ — 03)
WHAT WE BUILD
Four lines of work — from automation to the software that carries it.
001.
AI AUTOMATION BUILD
We hand the repetitive half of an operation to systems built to run it the same way at 3am and at noon.
001.
Agents that run workflows
002.
Document and ticket triage
003.
Data cleanup and enrichment
004.
Escalation paths to a human
002.
CUSTOM SOFTWARE BUILD
We design and build the software your operation actually needs, not the one a platform happens to sell you.
001.
Internal tools and portals
002.
Dashboards and reporting
003.
APIs and service backends
004.
Multi-tenant SaaS platforms
003.
SHOPIFY AND HYDROGEN
We build and migrate online storefronts on Shopify and Hydrogen, because there, page speed is what sets the conversion rate.
001.
Headless Hydrogen builds
002.
Theme and checkout work
003.
Catalogue and data migration
004.
Core Web Vitals at scale
004.
MOBILE APPLICATIONS
We ship iOS and Android from one codebase, wired straight into the same systems the rest of the business runs.
001.
React Native and native
002.
Offline and sync handling
003.
Push, auth and payments
004.
Store release and updates
(nai™ — 04)
REFERENCE BUILDS
Seven builds we are set up to take on,
and what each one is measured against.
10 wks
The back-office loop where paperwork arrives, gets read by a person, and turns into an entry in a system nobody enjoys updating.
Best fit
Operations-heavy businesses
Service line
Operations Mapping, AI Automation Build
Problem shape
Volume grows, so headcount has to grow with it
Technology Stacks
(nai™ — 05)
MORE BUILDS
Inside Nukes AI
0:40 sec film
Engagement
Audit to running
Typical audit-to-run path
Predictable beats heroic.
Systems outlast intuition, every time.
AI
Automation
Custom
Software
Shopify
Hydrogen
Mobile
Apps
4 practices
Bring us the expensive one
Bench
Senior Only
Of the engineers on a build
No demos. Just systems
Clarity before automation
Decisions over dashboards
Built for messy reality
Systems that hold under load
(nai™ — 06)
Process
A repeatable path we use to design, deploy and hold AI systems inside live operational work.
001.
Audit
We trace how the operation runs across workflows, data and decisions, naming the constraints and the openings before anything is automated.
001.
Core operational workflows and handoffs
002.
Data sources, ownership and consistency
003.
Manual decision points and exceptions
004.
Existing tools, links and constraints
Technology Stacks
002.
AUTOMATION
We design the agents that carry the repetitive half of the work, turning the audit into an architecture whose behaviour stays bounded.
001.
Automation-ready workflows and clusters
002.
Decision trees, escalation and guardrails
003.
System APIs and the integration surface
004.
Human-in-the-loop control mechanisms
Technology Stacks
003.
ARCHITECTURE
We choose the architecture with you and hold it against business priorities, so every build lands inside one system rather than beside it.
001.
Where automation pays back first
002.
What your data can support today
003.
Build, buy and hybrid trade-offs
004.
Risk, compliance and guardrails
Technology Stacks
004.
INFRASTRUCTURE
We build the structured data layer a reliable system needs, turning fragmented sources into consistent, model-ready infrastructure.
001.
Data sources and ingestion pipelines
002.
Data quality, gaps and normalization
003.
Storage and retrieval performance
004.
Security, privacy and access control
Technology Stacks
How We Operate
(™)
(nai™ — 07)
SYSTEM OUTPUT
Everything above produces the same four things —
a system that runs, and the evidence that it does.
© ‒ 001.
A named inventory
Every workflow, source and decision point written down once, in the language the team already uses for them.
© ‒ 002.
An executable plan
The audit turned into an architecture you can run, review and argue with, rather than a slide about one.
© ‒ 003.
Agents with edges
Bounded behaviour, logged decisions, and a person who owns each one of them.
© ‒ 004.
Work that holds
The part nobody sells: the system still doing its job in month nine, under load, with the data it actually gets.
© ‒ 005.
Evidence, not claims
Numbers you can check, against a baseline we measured together before anything changed.
(nai™ — 08)
FIRST 90 DAYS
The studio is new and says so. The people
in it have shipped this work before.
Days 1 — 10
Audit and map
We trace how the work moves before anything gets automated.
Days 10 — 25
Design the system
Architecture, data contracts, and where a person stays in the loop.
Days 25 — 60
Build and integrate
Shipped in tight loops against your real data and real edge cases.
Days 60 — 90
Run and retune
Live, monitored, and tuned against what the system actually meets.
We will tell you what can be automated, what should not be, and what it takes.
No hype. Just systems
Clarity beats automation
Decisions over demos
Built for messy reality
Systems that hold under load
(nai™ — 09)
Our bench
(nai™ — 10)
ENGAGEMENT MODELS
Three ways to start, priced so
you know before you enquire.
We publish the starting price because the alternative is a discovery call whose real purpose is finding out what you can afford.
Nukes AI
Why the prices are public
© ‒ 001.
Discovery & Strategy
We analyze your systems, workflows and data to find where AI pays off fastest.
from
$
/project
Timeframe:
Usually delivered in 2–3 weeks
What’s included:
Existing process and workflow audit
High-impact AI opportunity mapping
Data structure and integration review
AI system architecture definition
Data pipeline and model design
© ‒ 002.
System Design
The architecture, data pipelines and foundation a production build needs.
from
$
/project
Timeframe:
Usually delivered in 3–5 weeks
What’s included:
Existing process and workflow audit
High-impact AI opportunity mapping
Data structure and integration review
AI system architecture definition
Data pipeline and model design
© ‒ 003.
Deployment & Integration
Building and deploying AI into the tools and workflows your teams use.
from
$
/project
Timeframe:
Usually delivered in 4–8 weeks
What’s included:
Existing process and workflow audit
High-impact AI opportunity mapping
Data structure and integration review
AI system architecture definition
Data pipeline and model design
(nai™ — 11)
Insights & Research
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
(nai™ — 12)
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
(nai™ — 13)
Frequently Asked Questions
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 at home, to the GDPR in the EEA, the UK GDPR and the CCPA/CPRA in California where your business brings them into scope, and will sign whatever else your own regime requires. 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.
(nai™ — 14)
Our contact
Bring us a workflow, a storefront or an idea.
We will say what it takes to build it.
No hype. Just systems
Clarity beats automation
Decisions over demos
Designed for messy reality
Systems that hold under pressure
(nai™ — 15)
Our principles
The rules we hold ourselves to on every system we put into production.
We believe automation should carry all the dull work, never the judgement. If a build can’t be watched, questioned and put right — it doesn’t ship.
We don’t automate everything we could. We automate what pays, and keep every system legible to the people who answer for it.
