AI engineering for real business outcomes
Plenty of agencies are built around long retainers. What you actually want is a live site, an app in the stores, and users adopting it.
Design and engineering share one backlog, so the visuals never drift away from what can realistically ship on time.
If that sounds like your situation, Arcode is set up for businesses searching for an AI agency. Agents, RAG, MCP, and LLM features delivered as working systems rather than slide decks.
What you get
- AI strategy to implementation
- Agent and MCP builds
- Knowledge systems
- Automation that sticks
- One studio accountable for the result
- Design and engineering on the same schedule
- Support available after launch
How engagement works
As your AI agency, 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 development 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 North America 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 agency 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
- MCP
- the Model Context Protocol, so your tools plug into Claude, Cursor, and other agent clients without a custom adapter for each
- RAG
- retrieval over your own documents, so answers cite your data instead of the model's training set
- Next.js
- server rendering and static generation in one framework, so marketing pages stay fast and app routes stay dynamic
What the first weeks look like
- Week 1
Scope and architecture
Users, data, integrations, and constraints, ending in a written plan for the AI development that you approve before anyone opens an editor.
- Weeks 2-3
Design the critical path
The screens that carry the product, prototyped and clickable. Design and engineering review together, so nothing gets drawn that cannot ship.
- Weeks 4-8
Build in weekly slices
Every week ends with something on a real URL. ai strategy to implementation lands early, because the rest of the plan depends on it working.
- Launch
Ship, measure, hand over
Release, monitoring, analytics, and a documented handover, so your team can carry it without calling us.
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 development 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 North America
Same timezone and a face to meet, which genuinely matters for some teams. You are also paying US and Canadian startups shipping fast, plus established companies adding AI to products that already have users. rates for the whole team including the layers that never touch your AI development, and a local shortlist is a small shortlist.
Arcode
A small senior team. The people who scope the AI development 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
Brain CMS
Frontend · Firebase · Analytics
Multi-module enterprise CMS with documents, calendars, video, and advanced analytics.
Content and operations system spanning enterprise modules with Firebase and rich media tooling.
- · Multi-module business domains
- · PDF and document management
- · Advanced charting suites
- · Calendar and media workflows
React · Redux Saga · Firebase · Chart.js · Video.js
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 North America
US and Canadian startups shipping fast, plus established companies adding AI to products that already have users. Work is remote-first (ET to PT), with written updates you can read in your own time. We usually collaborate in English.
How we work with North America teams
North America spans ET to PT, roughly a three-hour band. We hold one standing call in the client's morning and cover everything else in writing, so the time difference costs at most a few hours on any decision.
What North America clients usually need
Most North American work is funded startups shipping an MVP against a deadline, plus companies bolting real AI features, agents, RAG, copilots, onto a product that already has paying users.
Contracts, data, and compliance
Contracts follow US or Canadian contractor norms with IP assignment. For regulated sectors, healthcare or finance, we scope the relevant compliance evidence, HIPAA or SOC 2, into the build plan from the first sprint rather than adding it before a raise.
