Automate workflows with production AI
Deep skills without product context tend to produce fragile integrations. The specialist work has to connect to real user flows.
We document the prompts, tools, schemas, and day-to-day runbooks so your team can run the system after we hand it over.
If that sounds like your situation, Arcode is set up for 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. On AI automation projects we favour practical architecture over trendy defaults.
- 02
Design
System design, AI/tool boundaries, interface direction, and interaction prototypes. The work for teams in Chicago stays tied to the outcome you asked for.
- 03
Build
Modern stack, clean architecture, evaluated AI behavior, and iterative demos you can react to. You see progress on the critical path every week.
- 04
Launch
Ship, monitor, harden, and keep improving models, tools, and product after go-live. Nothing here waits on a handover between separate teams.
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 Chicago
Same timezone and a face to meet, which genuinely matters for some teams. You are also paying Chicago enterprises hiring for dependable full-stack delivery. 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
Quickee UI Library
Design system · Tailwind
Tailwind component library for React and Next.js with reusable UI patterns inspired by Tailwind UI.
Code Band component kit providing a variety of production-ready Tailwind components with Redux-friendly patterns for React and Next.js apps.
- · Reusable Tailwind components
- · React and Next.js ready
- · Consistent interaction patterns
- · Faster UI assembly across projects
React · Next.js · Tailwind · Redux
CRM Dashboard
Frontend · Analytics
Customer relationship dashboard with management workflows and data visualization on Next.js.
Freelance CRM surface for managing relationships and visualizing key metrics, deployed on Vercel.
- · CRM management views
- · Data visualization
- · Responsive dashboard layout
- · Vercel deployment
Next.js · React · Tailwind · Vercel
Working with teams in Chicago
Chicago enterprises hiring for dependable full-stack delivery. We work with clients in Chicago remotely and keep overlapping hours in CST/CDT. We usually collaborate in English.
How we work with Chicago teams
Chicago is ten to eleven hours behind us, so our late afternoon is your morning. That is enough for a daily sync window, with the rest handled in writing so nobody waits on a call to get unblocked.
What Chicago clients usually need
Chicago work leans towards established companies rather than early-stage startups: logistics, insurance, trading-adjacent tooling, and internal platforms that need rebuilding without disrupting the business running on them.
Contracts, data, and compliance
Migrations here usually need a parallel-running period and a rollback plan rather than a cutover date. We invoice in USD under US contractor agreements with IP assignment, and scope SOC 2 evidence in where procurement asks for it.
