Automate workflows with production AI
Newer tools change quickly, so it helps to work with someone who has already shipped the pattern you are buying.
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
- 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 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 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 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 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
Agile IT Blog
Frontend · Astro
Performance-minded blog maintained in Astro with Markdown, Tailwind, and modular content integrations.
Stellar Stack content site where I improved performance and scalability across modular Astro components and data integrations.
- · Astro static content pipeline
- · Markdown-driven posts
- · Modular component structure
- · Performance and scalability pass
Astro · Markdown · Tailwind · SCSS
Brain Blog
Frontend · Gatsby · Strapi
Custom Gatsby blog with Strapi CMS, built for fast content publishing and a clean reading experience.
Freelance content product pairing Gatsby performance with Tailwind styling and a Strapi-powered editorial workflow.
- · Gatsby static generation
- · Strapi blog integration
- · Tailwind-driven layout
- · Fast content browsing
Gatsby · Tailwind · Strapi
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.
