Automate workflows with production AI
Specialist work falls apart when it never leaves the demo stage. Anything in production still needs auth, data handling, monitoring, and an interface people can use.
We document the prompts, tools, schemas, and day-to-day runbooks so your team can run the system after we hand it over.
Most people who reach this page are companies searching for AI automation. Business process automation with agents, APIs, and LLM workflows that save time and reduce manual work.
What you get
- Workflow automation
- Internal AI copilots
- Document and data pipelines
- Measurable time savings
- One studio accountable for the result
- Design and engineering on the same schedule
- Support available after launch
How engagement works
As your AI automation, we run a shared backlog, design reviews, and engineering sprints, and you keep one point of contact throughout.
- 01
Discover
Goals, constraints, data sources, and success metrics, all settled before we touch architecture or prompts. You see progress on the critical path every week.
- 02
Design
System design, AI/tool boundaries, interface direction, and interaction prototypes. Nothing here waits on a handover between separate teams.
- 03
Build
Modern stack, clean architecture, evaluated AI behavior, and iterative demos you can react to. On AI automation projects we favour practical architecture over trendy defaults.
- 04
Launch
Ship, monitor, harden, and keep improving models, tools, and product after go-live. The work for remote and worldwide teams stays tied to the outcome you asked for.
How we build it, and why
The stack is chosen per project, not applied from a template. For AI automation work these are the defaults, and the reason each one is on the list.
- OpenAI
- a second model provider, so a single vendor's outage or price change is not your problem
- Claude
- long-context reasoning and reliable tool calling for agent work
- n8n
- workflow automation your team can edit later without opening a code editor
- MCP
- the Model Context Protocol, so your tools plug into Claude, Cursor, and other agent clients without a custom adapter for each
- Node.js
- one language across the stack, so context does not get lost at the boundary
What the first weeks look like
- Week 1
Spike the risky part
We build the thinnest possible version of workflow automation against your real data. If it is going to be a problem, it is better to know in week one than week six.
- Weeks 2-3
Harden the integration
Auth, rate limits, error paths, and the data contract. This is the work that separates a demo from something you can leave running.
- Weeks 4-6
Wire it into the product
The AI automation stops being a standalone service and becomes a feature people use, with the interface and permissions that implies.
- Ongoing
Evaluate and tune
Behaviour gets measured against cases you care about, and we tune from that rather than from impressions.
Weighing up your options
There are three realistic ways to get this built. Each is the right answer for someone.
A solo freelancer
Cheapest per hour and fine for a contained task. The risk on AI automation is breadth — one person covering design, backend, infrastructure, and launch usually means one of them is weak, and there is no cover when they are unavailable.
An in-house hire
A permanent engineer is the right call once the AI automation is continuous rather than a project. Before that point you are paying a salary, recruiting for months, and carrying the risk that the first hire is the wrong shape for the problem.
Arcode
A small senior team. The people who scope the AI automation write the code, you get weekly demos instead of status decks, and the engagement ends when the thing is live and handed over.
Work we have shipped
Note Assist
AI product · Next.js · Speech
AI meeting assistant with live speech-to-text, ChatGPT chat, and Firebase auth for transcription workflows.
Freelance AI product built with Next.js, Firebase, AssemblyAI, and ChatGPT for live meeting transcription and realtime chat around the transcript.
- · Live speech-to-text transcription
- · Realtime chat over meeting notes
- · Firebase authentication
- · AI-assisted follow-up on transcripts
Next.js · Firebase · AssemblyAI · ChatGPT · TypeScript
Agile IT Blog
Frontend · Astro
Performance-minded blog maintained in Astro with Markdown, Tailwind, and modular content integrations.
Stellar Stack content site where I improved performance and scalability across modular Astro components and data integrations.
- · Astro static content pipeline
- · Markdown-driven posts
- · Modular component structure
- · Performance and scalability pass
Astro · Markdown · Tailwind · SCSS
Brain Blog
Frontend · Gatsby · Strapi
Custom Gatsby blog with Strapi CMS, built for fast content publishing and a clean reading experience.
Freelance content product pairing Gatsby performance with Tailwind styling and a Strapi-powered editorial workflow.
- · Gatsby static generation
- · Strapi blog integration
- · Tailwind-driven layout
- · Fast content browsing
Gatsby · Tailwind · Strapi
Where we work
Fully remote engagements across time zones. We work remote-first, so the time zone matters less than keeping updates clear and regular. We usually collaborate in English.
How we work with Remote worldwide teams
Remote-first engagements run on written updates, a shared backlog, and a weekly demo call. We keep at least four hours of overlap with whichever time zone you work in, and everything else happens asynchronously so nobody waits on a meeting to unblock work.
What Remote worldwide clients usually need
Most remote briefs arrive as a rough product idea with a deadline attached. The first job is separating the slice that has to ship from the rest of the roadmap.
Contracts, data, and compliance
Contracts are milestone-based, invoiced against agreed deliverables. Code lives in your repository from the first commit, so there is no handover cliff if you take the work in-house later.
