Do you ship real AI features or just chat wrappers?

Real ones. Streaming responses, prompt orchestration, tool use, vector search, and eval pipelines. DreamyBot's AI stack handles 100,000 users with prompts versioned in the repo and one eval suite per prompt. Monetiza uses Claude in production for transaction categorization.

How do you add AI to an existing mobile app?

I start with one feature that earns its keep, build the backend endpoint and streaming layer, wire it into your existing screens, and put an eval suite around the prompt before launch. You get a feature that ships, not a six-month rewrite.

How do you make AI features feel fast on a phone?

I stream the model's response token by token using server-sent events from FastAPI into a Flutter StreamBuilder. I don't reach for websockets unless the app needs bidirectional. The DreamyBot AI-stack post in /writing covers it with code samples.

How do you keep AI features reliable as prompts change?

I version prompts in the repo, never in a database, so a rollback is git revert. I run one eval suite per prompt on every pull request, and I log every response with a hash of the system prompt so I can debug a regression instead of guessing.

Whichever fits the task and budget. I use Claude heavily and OpenAI where it's stronger, and I build the integration so swapping models is a config change, not a rewrite. I'll benchmark both on your actual prompts before committing.

Full-time or fixed project?

Either. I take one remote full-time engagement at a time with startups in the EU, UK, US, or Canada, and I also take fixed-scope AI projects: an AI feature, a streaming rebuild, or an eval pipeline for an existing integration.

AI Mobile App Developer for Claude, OpenAI, and Backend Integrations

I ship AI features that hold up in production: streaming responses, prompt orchestration, tool use, vector search, and the eval pipelines that keep them reliable. The hard part is rarely the API call. It's making the feature feel fast on a phone, keeping it stable as prompts change, and not lighting your token budget on fire. I've done it for a 100,000-user app, solo.

Role: Senior Mobile + AI Engineer
Engagement: Full-time or project
Location: Lahore · GMT+5
Reach: 500K+ users · 15+ apps

The short version

Ali Wajdan is an AI mobile app developer who builds real AI features in production apps, not chat-wrapper demos. He has shipped AI into apps used by hundreds of thousands of people, including DreamyBot, a Flutter and FastAPI consumer app with 100,000+ users where one engineer built the streaming Claude layer, prompt orchestration, and eval pipeline, and Monetiza, which uses Claude in production to categorize transactions. He adds AI to existing mobile apps and builds new ones end to end: the Flutter or SwiftUI client, the FastAPI or Node.js backend, streaming responses over server-sent events, vector search, and tool use. He versions prompts in the repo, runs one eval suite per prompt on every pull request, and logs every model response for debugging. He works remotely from Lahore (GMT+5) as a full-time hire or on a fixed-scope project.

100K+
Users on DreamyBot's solo-built AI stack
38%
Day-30 retention in the AI category
1 eng
Mobile, backend, and AI layer, one person

/ When to hire me

You want to add AI to an existing app without it feeling bolted on.
Your current AI feature is a chat wrapper and users have stopped opening it.
You need streaming responses that feel instant on a phone, not a spinner.
You want prompts and evals managed like code, so the feature stays reliable.

/ What I build

Streaming LLM responses over server-sent events into a Flutter or SwiftUI UI.
Prompt orchestration, tool use, and multi-step agentic flows.
Vector search and retrieval for grounding answers in your data.
Eval suites that run on every PR so a prompt change can't silently regress.
Response logging and cost controls so you can debug week six, not guess.
The FastAPI or Node.js backend that serves all of it.

Stack.

Tools for this work

Models

Anthropic Claude, OpenAI, embeddings

AI backend

FastAPI, Python, server-sent events, streaming

Retrieval

Vector search, RAG, Postgres pgvector

Mobile

Flutter, SwiftUI, StreamBuilder rendering

Reliability

Versioned prompts, per-prompt evals, response logging

Selected work.

Proof, with numbers

CASE 02 / CONSUMER AI

DreamyBot.

Consumer AI
Flutter
FastAPI
Python
Node.js

An AI consumer app with real subscriptions, not a demo. 100,000 users. One engineer doing mobile, API, and the AI layer. Streaming responses, prompt orchestration, RevenueCat tiers, a backend that kept up before I had to hand it off.

100K+Users across mobile and web
4.7★iOS App Store rating
38%Day-30 retention in the AI category

Read the case study

DreamyBot AI consumer app chat interface with streaming responses powered by Anthropic Claude.

dreamybot.ai2024 to 2025

CASE 01 / FINTECH

Monetiza.

Fintech
iOS + Android
Flutter
RevenueCat
Firebase

A personal-finance app that had to feel as trustworthy as the bank apps it sits next to on the home screen. I picked the architecture, shipped both stores, and ran the subscription stack end to end.

120K+Active users across iOS and Android
4.8★App Store rating, 8K+ reviews
11×Subscription revenue growth in year 1

Read the case study

Monetiza personal-finance app home screen showing accounts list, subscription gating, and AI-categorised transactions.

monetiza.app2023 to 2025

Ali shipped what would have taken our team a quarter in three weeks. He owns the whole stack, mobile, API, infra, without us having to chase him.
Founder, Monetiza · series-seed fintech

Common questions.

FAQ

Do you ship real AI features or just chat wrappers?
Real ones. Streaming responses, prompt orchestration, tool use, vector search, and eval pipelines. DreamyBot's AI stack handles 100,000 users with prompts versioned in the repo and one eval suite per prompt. Monetiza uses Claude in production for transaction categorization.
How do you add AI to an existing mobile app?
I start with one feature that earns its keep, build the backend endpoint and streaming layer, wire it into your existing screens, and put an eval suite around the prompt before launch. You get a feature that ships, not a six-month rewrite.
How do you make AI features feel fast on a phone?
I stream the model's response token by token using server-sent events from FastAPI into a Flutter StreamBuilder. I don't reach for websockets unless the app needs bidirectional. The DreamyBot AI-stack post in /writing covers it with code samples.
How do you keep AI features reliable as prompts change?
I version prompts in the repo, never in a database, so a rollback is git revert. I run one eval suite per prompt on every pull request, and I log every response with a hash of the system prompt so I can debug a regression instead of guessing.
Claude or OpenAI?
Whichever fits the task and budget. I use Claude heavily and OpenAI where it's stronger, and I build the integration so swapping models is a config change, not a rewrite. I'll benchmark both on your actual prompts before committing.
Full-time or fixed project?
Either. I take one remote full-time engagement at a time with startups in the EU, UK, US, or Canada, and I also take fixed-scope AI projects: an AI feature, a streaming rebuild, or an eval pipeline for an existing integration.

More of what I do

Adding AI to your app?

Tell me what you want it to do. Thirty minutes, no pitch: we work out the smallest AI feature that's worth shipping, and whether a full-time role or a fixed project fits.

Book a 30-min intro call Email me about a project