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 teams in Seattle 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.
A local agency in Seattle
Same timezone and a face to meet, which genuinely matters for some teams. You are also paying Seattle teams valuing cloud-native systems and AI engineers. rates for the whole team including the layers that never touch your AI automation, and a local shortlist is a small shortlist.
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
Working with teams in Seattle
Seattle teams valuing cloud-native systems and AI engineers. We work with clients in Seattle remotely and keep overlapping hours in PST/PDT. We usually collaborate in English.
How we work with Seattle teams
Seattle is twelve to thirteen hours behind us, so the overlap sits at the ends of the day: your morning, our evening. We keep one fixed call and run the rest asynchronously.
What Seattle clients usually need
Seattle work is cloud-native by default and often technically demanding — data platforms, AI infrastructure, and engineering teams who want a partner able to read their existing codebase rather than start from scratch.
Contracts, data, and compliance
Expect architecture to be reviewed properly before the build starts. We invoice in USD under US contractor agreements with IP assignment, and default to AWS or Azure where your team already has the operational muscle.
