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.
Specialist features sit behind clear interfaces, so your core product stays stable while new capability gets added.
We work best with companies searching for AI automation who want a partner that ships. 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
- Documented handover and runbooks
- Help getting it deployed
How engagement works
During the consult we turn your idea into a first milestone, often a clickable prototype or one working slice of the product.
- 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
Happy Kids Dental
Frontend · CMS
Pediatric dental site with services, patient resources, and appointment-focused UX.
Code Band clinic product using Next.js, Tailwind, Redux Toolkit, Video.js, and Strapi for content and scheduling flows.
- · Pediatric service pages
- · Patient resource content
- · Appointment scheduling UX
- · Strapi-managed content
Next.js · Tailwind · Redux Toolkit · Strapi · Video.js
Vigorant
Frontend · Accessibility
Accessible web app with reusable components, Redux Toolkit state, and Strapi integration.
Code Band product built with React, Next.js, Tailwind, and Redux Toolkit, focused on accessibility and maintainable UI patterns.
- · Accessibility-first UI
- · Reusable component system
- · Efficient Redux Toolkit logic
- · Strapi-backed content
Next.js · React · Tailwind · Redux Toolkit · Strapi
Where we work
International clients across US, UK, EU, Middle East, and Asia. We work remote-first, so the time zone matters less than keeping updates clear and regular. We usually collaborate in English.
