You will get AI image or video generation added to your web app
Project details
I've built and run this exact stack in production — an AI video platform
serving 18,000+ users, where generation jobs run for a minute or more across
Replicate and OpenAI and still have to land reliably every time.
Most AI integrations work in a demo and break in production. The 90-second
render that times out. The webhook that fires twice and double-charges. The
job stuck in "processing" forever with no way to recover it. Those failures
are the actual work, and they're the ones I've already hit and solved.
You get a real async job pipeline — queuing, retries, idempotent webhook
handling, live status in the UI, and per-user limits so one account can't run
up your inference bill. Clean, documented code that ships inside your existing
app, not a prototype your team has to rebuild.
Six years full-stack across Next.js, Node, FastAPI, PostgreSQL, and AWS, so
the AI feature arrives integrated with your auth, your database, and your
deployment — not bolted on beside them. You keep your provider account and
pay inference costs directly, so you always see what the feature actually
costs to run.
serving 18,000+ users, where generation jobs run for a minute or more across
Replicate and OpenAI and still have to land reliably every time.
Most AI integrations work in a demo and break in production. The 90-second
render that times out. The webhook that fires twice and double-charges. The
job stuck in "processing" forever with no way to recover it. Those failures
are the actual work, and they're the ones I've already hit and solved.
You get a real async job pipeline — queuing, retries, idempotent webhook
handling, live status in the UI, and per-user limits so one account can't run
up your inference bill. Clean, documented code that ships inside your existing
app, not a prototype your team has to rebuild.
Six years full-stack across Next.js, Node, FastAPI, PostgreSQL, and AWS, so
the AI feature arrives integrated with your auth, your database, and your
deployment — not bolted on beside them. You keep your provider account and
pay inference costs directly, so you always see what the feature actually
costs to run.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI-Generated Video, Conversational AI, Natural Language GenerationAI Development Language
PythonAI Tools
Azure OpenAI, Hugging FaceAI Models
ChatGPT, DALL-E, GPT-4, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$450
|
Standard
$900
|
Advanced
$1,800
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 2 | 3 | 4 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | ||
Model Documentation | - | - | |
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$250Frequently asked questions
About Muhammad
AI Engineer | RAG & Generation Pipelines | Next.js, Postgres
Arifwala, Pakistan - 1:14 pm local time
search over your own data, image and video generation, streaming chat, and
the async pipelines that keep all of it from falling over.
Most developers can call an API. The hard part is what happens after: a video
generation that takes 90 seconds, a webhook that fires twice, a retrieval
query that returns confidently wrong chunks, a model bill that triples
overnight. That's the layer I build.
WHAT I BUILD
- Document Q&A and semantic search — Postgres + pgvector, hybrid retrieval,
chunking tuned to your document type, citations on every answer. No vector
database subscription, no framework lock-in.
- Generation features — video, image, and audio via Replicate and other model
APIs, wired into your existing product
- Async job pipelines — queuing, retries, idempotent webhook handling,
progress tracking, graceful failure
- Streaming AI chat — SSE with proper cancellation, token handling, and
reconnection, inside your Next.js or React app
- Cost control — usage metering, per-user limits, model routing, caching, so
your inference spend stays predictable
- The full application around it — Next.js frontends, Node and FastAPI
backends, PostgreSQL with Prisma, AWS deployment
RECENT WORK
AIVideosTube — an AI video generation platform serving 18,000+ users, which I
built end to end. Users submit a prompt, a job runs across Replicate and other
model APIs for a minute or more, and the result lands reliably in their
library. Behind that: a queue that survives failed inference calls, webhook
handling that stays correct when providers deliver twice, per-user usage
limits, and cost controls that keep the unit economics from breaking as usage
grows.
Pakistan Law Bot — AI legal research platform. I built the full application
around the retrieval layer: authentication, subscriptions, the API layer, and
the streaming answer interface that delivers responses token by token with
source citations attached.
HOW I WORK
On retrieval projects, I test against your real documents and your real
questions before writing any application code. RAG fails on retrieval quality
far more often than on the model — if the wrong chunks come back, no amount of
prompt engineering saves the answer.
On generation work, I ask what happens when the model call fails before I ask
which model you want. AI features look finished in a demo and break in
production — the timeouts, the duplicate charges, the jobs stuck in
"processing" forever. I build for that from the first commit.
Six years full-stack, so the AI feature ships inside a working product rather
than as a prototype someone else has to productionize.
Tell me what you're trying to build and what your users will actually do with
it, and I'll tell you what it takes to run it in production.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff & Technical Review
I review your codebase, confirm your model provider setup, and map exactly where the generation feature will live. You get a short written plan covering the approach, the integration points, and anything I need from you before I start building.
Model Integration
I connect your chosen provider — Replicate, OpenAI, or Gemini — and get generation working end to end against real prompts. You'll see working output at this stage, before any UI work begins, so we confirm quality early.