AI systems that ship in production
Hiring rounds often take weeks before anyone writes real code. What you usually need is one person who can own the architecture and the delivery from the first week.
You talk directly to the people writing the code, which keeps architecture decisions close to the implementation.
If that sounds like your situation, Arcode is set up for startups and product teams hiring AI engineering talent. 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. On AI engineering 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 Islamabad 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 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 Islamabad
Same timezone and a face to meet, which genuinely matters for some teams. You are also paying Islamabad tech community around SaaS and AI-enabled products. 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
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
Case Management Hub
Full-stack engineer · NestJS · Next.js
HIPAA-compliant healthcare SaaS for 150+ US organizations, with dual databases, realtime collaboration, billing, and secure client portals.
Enterprise case management platform for healthcare and social-work organizations with encryption, realtime messaging, Stripe billing, Zoom, and calendar sync.
- · HIPAA-ready architecture with encryption and audit logging
- · Realtime collaboration via Socket.io + Redis
- · Client portal and role-based access control
- · Stripe billing and document workflows
Next.js · NestJS · TypeScript · MongoDB · Redis · Socket.io
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
Working with teams in Islamabad
Islamabad tech community around SaaS and AI-enabled products. We work with clients in Islamabad remotely and keep overlapping hours in PKT. We usually collaborate in English/Urdu.
How we work with Islamabad teams
Arcode operates from Pakistan, so Islamabad clients get full working-day overlap and in-person meetings when a project benefits from them.
What Islamabad clients usually need
Islamabad briefs come from a mix of funded startups building for export markets and established businesses replacing manual processes with software that actually gets used.
Contracts, data, and compliance
We invoice in PKR locally. Where a product targets US or UK users, we build against those compliance and payment expectations from the first sprint rather than adapting afterwards.
