AI systems that ship in production
Splitting work across several freelancers creates handoff problems. Keeping it with one accountable engineer or small pod protects both quality and pace.
Discovery stays short and specific: users, data, and risks. After that we build the critical path first and strengthen it from there.
We work best with startups and product teams hiring AI engineering talent who want a partner that ships. Agents, MCP servers, RAG pipelines, and LLM features wired into real software rather than demos.
What you get
- Production AI agents with tool calling
- MCP servers connected to your stack
- RAG over company knowledge
- OpenAI and Claude product integrations
- One studio accountable for the result
- Design and engineering on the same schedule
- Support available after launch
How engagement works
As your AI engineer, 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 engineering 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 Singapore 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 engineer 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
- LangChain
- orchestration for multi-step chains when the flow is genuinely complex
- Next.js
- server rendering and static generation in one framework, so marketing pages stay fast and app routes stay dynamic
- NestJS
- structure and dependency injection on the backend, which keeps a growing API from turning into a pile of route handlers
What the first weeks look like
- Days 1-3
Context and constraints
Codebase, deploy path, and what "done" means for the AI engineering. Short, because the useful version of this is specific.
- Week 1
First shipped slice
Something real goes out in the first week — production ai agents with tool calling rather than a setup ticket.
- Weeks 2-6
Build to the milestone
Pull requests, reviews, and weekly demos. You see the work as it happens rather than at a handover meeting.
- Handover
Documented and transferable
Runbooks, architecture notes, and a walkthrough, so the work does not depend on us still being here.
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 engineering 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 Singapore
Same timezone and a face to meet, which genuinely matters for some teams. You are also paying Singapore market for SaaS, fintech, and AI engineering. rates for the whole team including the layers that never touch your AI engineering, and a local shortlist is a small shortlist.
Arcode
A small senior team. The people who scope the AI engineering 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 Singapore
Singapore market for SaaS, fintech, and AI engineering. We work with clients in Singapore remotely and keep overlapping hours in SGT. We usually collaborate in English.
How we work with Singapore teams
Singapore is SGT, three hours ahead of us, so mornings overlap fully. Decisions raised at the start of your day are usually resolved before it ends.
What Singapore clients usually need
Singapore work concentrates in fintech, logistics, and regional SaaS, frequently products that need to serve several Southeast Asian markets from one codebase.
Contracts, data, and compliance
We invoice in SGD or USD. PDPA obligations and multi-market requirements, including currency, language, and payment methods per country, get scoped in the architecture rather than patched in later.
