Full-Stack Developer AI Integration London, UK

I build AI-powered SaaS apps that ship to production.

Production-grade RAG systems, embeddable chatbots, and workflow automation for founders, ops teams, and SaaS companies that need more than a prototype.

0+ Products shipped
0% Support reduction
0h Typical response
Specialism
AI + MERN
Based in
London, UK
Status
Open for work

I'm a full-stack developer based in London, building production AI applications for founders and small teams. Over the past two years I've shipped four AI tools running live: a multi-tenant document Q&A platform, an embeddable customer-support widget with analytics, an email-to-Slack automation system, and an EdTech study app.

My focus is the gap between working in a notebook and working under real users. Authentication, billing, multi-tenancy, deployment, and observability are part of the deliverable, not afterthoughts.

I work across UK and US timezones, take on a small number of projects at a time, and communicate in plain English about scope and trade-offs.

Frontend

  • React
  • Next.js
  • TypeScript
  • Tailwind

UI Libraries

  • shadcn/ui
  • Ant Design
  • Radix UI
  • MUI
  • Chakra UI
  • Framer Motion

Backend

  • Node.js
  • Express
  • Python
  • FastAPI
  • MongoDB
  • PostgreSQL

AI / RAG

  • OpenAI
  • Claude
  • LangChain
  • ChromaDB
  • Vector search

Automation

  • n8n
  • Workflow orchestration
  • Webhooks

Infra

  • AWS
  • Docker
  • CI/CD Pipelines
  • GitHub Actions
  • Stripe
  • OAuth

Proven delivery for teams that needed speed, quality, and real outcomes.

Byte Bricks — Engagement

Full Stack Developer — Byte Bricks

Jan 2025 – Aug 2025

  • Shipped 3 production MERN applications serving live users end-to-end.
  • Containerised backend services with Docker on AWS Fargate, cutting environment-related bugs by 40%.
  • Built CI/CD pipelines from scratch, reducing deployment time by 30% and increasing feature velocity.
  • Optimised 15+ REST endpoints; average response time improved 25% through query and cache work.

Previously: MERN engineering at HATTRICK Solutions (2024).

Four production tools, each solving a real workflow problem.

Every project below is live and demoable. Click through to see the full workflow and interaction quality.

DashBot analytics dashboard
Project / 01

DashBot

Embeddable AI customer-support chatbot with a real analytics dashboard. Tracks unanswered questions, routing breakdown, and per-bot usage.

Problem
Small SaaS teams burn hours on repetitive support tickets and have no visibility into what users actually ask.
Approach
RAG over uploaded docs, one-line embed script, full dashboard for analytics and unanswered intents.
Outcome
Reduces routine support volume by ~40% based on internal testing; usable in any website with a single script tag.
Stack
ReactViteTypeScriptFastAPIMongoDBQdrantGroqDockerRailwayTailwind CSSshadcn/ui
View live
RAG Workspace application
Project / 02

RAG Workspace

Multi-tenant internal knowledge base for companies. Admins upload documents, users chat with them, and each workspace stays fully isolated.

Problem
HR, ops, and compliance teams need grounded answers from internal docs — not generic AI responses.
Approach
FastAPI backend with ChromaDB vector store, React frontend, role-based admin console, JWT auth.
Outcome
Per-workspace document isolation, per-user chat history, admin console for managing teams and upload permissions.
Stack
FastAPIReactChromaDBMongoDB
View live
InboxAI automation dashboard
Project / 03

InboxAI

Email automation that connects Gmail to Slack, Notion, and ClickUp. AI classifies urgency and routes work without manual triage.

Problem
Ops teams and founders drown in inbox triage; important client emails get lost, low-priority noise gets too much attention.
Approach
Gmail OAuth, AI classification of each new email, conditional routing to Slack alerts, Notion tasks, or ClickUp tickets.
Outcome
Preview mode for safe testing, configurable auto-run intervals, dashboard for workflow monitoring and health.
Stack
Next.jsReactTypeScriptMongoDBJWTGoogle OAuthSlack APINotion APIClickUp APITailwind CSSRailway
View live
Lecture Study learning interface
Project / 04

Lecture Study

EdTech platform that turns any YouTube lecture into a structured study session with summaries, quizzes, and a per-lecture Q&A chat.

Problem
Students and self-learners want study materials from YouTube lectures, not just raw transcripts.
Approach
Pulls public captions, generates AI study guides and MCQs, layers a knowledge-check chat scoped to each lecture.
Outcome
Per-lecture saved sessions, study guides and key ideas on demand, quizzes with explanations, light/dark UI.
Stack
ReactNode.jsOpenAIYouTube APIMongoDB
View live

A clear process from scope to production.

01

Discovery

We align on goals, constraints, and success metrics. I scope the MVP and flag risks early — no surprises mid-build.

02

Architecture

Stack selection, data model, and deployment plan. You get a written spec before a single line of code is written.

03

Build & Ship

Iterative delivery with staging previews. Auth, billing, and observability built in from the start — not bolted on later.

04

Handover

Deployed, documented, and yours to own. I include runbooks, env setup, and a walkthrough so your team can maintain it.

What I build.

  • Custom AI chatbots & support widgets

    RAG-powered chatbots trained on your docs, embeddable on any website with a single script tag, with an analytics dashboard tracking what users actually ask.

  • Internal knowledge bases & document Q&A

    Multi-tenant RAG systems for HR, compliance, ops, and consulting teams. Role-based access, per-user threads, source citations.

  • Email & workflow automation

    Connect Gmail, Slack, Notion, and ClickUp into a single AI-orchestrated pipeline. Classification, routing, alerts, observable runs.

  • Full-stack AI SaaS MVPs

    End-to-end SaaS builds with authentication, Stripe billing, admin dashboards, and AI features. Deployed and observable from day one.