You will get RAG system with voice AI integration, real-time document retrieval
Project details
I build RAG (Retrieval-Augmented Generation) systems that let your team or customers ask questions against your own documents and get accurate, source-cited answers in seconds.
Unlike generic chatbots that hallucinate, my RAG pipelines ground every response in your actual data using intelligent chunking, semantic embedding, and vector search with Pinecone, Weaviate, or pgvector.
What makes my service unique is the optional voice layer: I can add a conversational voice interface powered by real-time speech-to-text and text-to-speech, so users can talk to your knowledge base naturally, just like a phone call.
I have firsthand experience building voice AI systems at scale, having reduced AI voice latency from 4 seconds to under 2 seconds in production at Convocore.
My RAG systems support PDF, DOCX, website, and database data sources, with smart re-ranking to ensure the most relevant context is always retrieved.
I deliver with a web dashboard for document management, usage analytics, and a clean API for embedding into your existing app or website.
Unlike generic chatbots that hallucinate, my RAG pipelines ground every response in your actual data using intelligent chunking, semantic embedding, and vector search with Pinecone, Weaviate, or pgvector.
What makes my service unique is the optional voice layer: I can add a conversational voice interface powered by real-time speech-to-text and text-to-speech, so users can talk to your knowledge base naturally, just like a phone call.
I have firsthand experience building voice AI systems at scale, having reduced AI voice latency from 4 seconds to under 2 seconds in production at Convocore.
My RAG systems support PDF, DOCX, website, and database data sources, with smart re-ranking to ensure the most relevant context is always retrieved.
I deliver with a web dashboard for document management, usage analytics, and a clean API for embedding into your existing app or website.
Programming Languages
PHP, Python, TypeScriptWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,100
|
Advanced
$2,200
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 days |
Number of Revisions | 2 | 5 | 9 |
Number of Pages | 10 | 9999 | 9999 |
Design Customization | |||
Content Upload | |||
Responsive Design | |||
Source Code |
Frequently asked questions
About Ammar
Software Engineer
Alexandria, Egypt - 8:34 pm local time
I've worked on AI voice platforms, high-traffic crypto data feeds, and microservice architectures . I don't just write code — I design systems, measure outcomes, and ship things that hold up when it matters.
Results I've delivered:
▸ Cut AI voice latency in half — from 4 seconds to 2 seconds on a live SaaS platform
▸ Reduced platform downtime by 90% through on-call architecture redesign
▸ 27+ client projects delivered with a 5.0/5.0 rating and zero disputes — ever
▸ Achieved 98/100 PageSpeed score on a high-traffic, real-time crypto platform
What I build:
▸ Full-stack web apps — React, Next.js, Node.js, TypeScript, MERN/MEAN
▸ Microservice & event-driven architectures — RabbitMQ, Kafka, Docker, Kubernetes
▸ Real-time systems — WebSockets, VoIP integration, live data pipelines
▸ SaaS platforms, API-first backends, and high-performance admin dashboards
▸ WordPress solutions — 20+ custom themes and plugins, serving 5,000+ active users
What makes working with me different:
✔ I ship with metrics — every project has a measurable outcome attached
✔ Production-hardened mindset — I've monitored, scaled, and recovered real systems under load
✔ Proactive communication — no chasing, no ambiguity, no surprises at delivery
✔ ALX Software Engineering — graduated with 112% (bonus projects pushed past 100%)
Tech: TypeScript · React · Next.js · Node.js · Python · PHP · PostgreSQL · MongoDB · Docker · Kubernetes · AWS · Kafka · RabbitMQ · Datadog
I'm selective about the projects I take on — I work best with clients who care about quality, not just speed. If you need someone who has already built what you're trying to build, let's talk. I'm quick to respond and happy to jump on a short call.
Steps for completing your project
After purchasing the project, send requirements so Ammar can start the project.
Delivery time starts when Ammar receives requirements from you.
Ammar works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery and document audit
Review your document sources, assess data quality and structure, and define the ingestion pipeline strategy (chunking method, metadata extraction).
Infrastructure setup
Deploy the vector database (pgvector/Pinecone/Weaviate), set up the embedding pipeline, and configure the LLM API connections.