myibrahim.cloud

AI product

AI products and MVPs, built end to end and shipped to real users

An assistant that answers from your documents. A chatbot in your product. An AI feature your customers will actually use. I build the whole thing — model integration, retrieval, backend, interface, deployment — and I'm honest about what AI can't do before you pay for it.

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  • 5-star rated mentor on Udacity
  • 1,000+ students trained
  • 10+ large projects
  • 100+ projects delivered
  • 100% success rate

Who this is for#

Founders and product teams who need an AI product or feature that works outside a demo: a support assistant grounded in your documentation, a search that understands questions, an internal copilot over company knowledge, a document-processing pipeline, or an AI feature inside an existing app.

It's equally for companies that tried a quick chatbot, saw it make things up, and now want it done properly.

What you get#

  • A scoped, honest plan. The first conversation is about whether AI is the right tool. If a boring solution is better, I'll say so.
  • Retrieval done right (RAG). Your documents chunked, embedded and searched with hybrid retrieval, with citations so users can verify answers.
  • The full product, not a notebook. Backend (Python/FastAPI or Django, or Node/NestJS), a clean interface (React), authentication, admin tools, and deployment to your cloud.
  • Evaluation and monitoring. A test set of real questions with expected answers that runs on every change; dashboards for cost, latency and quality in production.
  • Handover. Documentation, runbooks and a walkthrough with your team.

How I build it#

  1. Scope. One workflow that matters, the data it needs, and a definition of "good".
  2. Prototype on real data, fast. Two weeks in, you're clicking through something that answers real questions from your real documents.
  3. Build the product. Retrieval pipeline, prompts and tools, backend, interface, permissions, deployment.
  4. Measure and harden. Evaluation set, guardrails, cost and latency tuning, monitoring.
  5. Launch and support. Gradual rollout, a review after the first weeks, and optional ongoing help.

Proof#

  • Manzel Masr — a property marketplace I founded: a 24/7 AI assistant searches listings conversationally, in Arabic, using retrieval over the live database (Django, PostgreSQL, pgvector, LangChain, OpenAI). Read the case study.
  • Legal document tools at Syntheia.io — I designed the backend algorithms for multi-document comparison that helps lawyers find flaws in contracts, and an app that summarises long legal documents. A microservice redesign improved accuracy by 15% and performance by 30%.
  • A real-time voice AI receptionist for US dental offices, where I lead the backend. See voice AI.
  • PDF chatbot — a retrieval-augmented app for chatting with PDF files.

Stack I typically use#

GPT-4o / Claude / Azure OpenAI · LangChain and LangGraph · pgvector or Milvus for vectors · Python (FastAPI, Django) or TypeScript (NestJS) · React · PostgreSQL, Redis · Docker and Kubernetes on Azure, AWS or GCP.

Where and how I work#

Remote from Cairo (UTC+2) for clients in the United States, Europe and the Middle East, in English or Arabic. Weekly written updates, demos on real data, and no surprises on scope.

Questions people ask

How long does an AI MVP take?

A focused MVP — one workflow, real data, deployed — usually takes four to six weeks. The first working prototype is typically in your hands within the first two, so we can adjust direction while it's cheap to do so.

Do you use OpenAI, Claude or open models?

Whichever fits the job. GPT-4o and Claude for quality, Azure OpenAI when your data has to stay in a specific region, and open models on your own infrastructure when privacy or cost demands it. The code is built so the model can be swapped.

Will it hallucinate?

Less than you fear and more than zero. I reduce it with retrieval over your real documents, citations the user can check, strict instructions about what the assistant may claim, and an evaluation set that measures answer quality on every change.

Who owns the code and the data?

You do. Everything is delivered in your repositories and your cloud accounts, with documentation and a handover, so you're never locked into me.

Can you work with our existing team?

Yes. I often work alongside an in-house team, own the AI and backend parts, and leave the codebase in a state your developers can carry forward.

Want something like this? Pick the service and send me a ready-made message on WhatsApp or email. I reply within 24 hours.
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