You will get AI RAG Pipeline — Chat With Docs, Citations, No Hallucinations
Project details
I will build a retrieval-augmented generation (RAG) system so your team can chat with internal documents and get grounded, citation-backed answers — not invented ones.
Supports PDFs, DOCX, and scanned docs with OCR. Built with embeddings + vector storage (Pinecone, Chroma, or pgvector), LangChain-style orchestration, and OpenAI/Claude. Includes a clean chat UI, source tracking, and structured extraction where needed.
Ideal for legal, compliance, healthcare, gov benefits, finance, and knowledge-heavy ops. I’ve shipped policy/document RAG for U.S. benefits programs (SNAP / Medicaid / TANF) where citation accuracy mattered.
Supports PDFs, DOCX, and scanned docs with OCR. Built with embeddings + vector storage (Pinecone, Chroma, or pgvector), LangChain-style orchestration, and OpenAI/Claude. Includes a clean chat UI, source tracking, and structured extraction where needed.
Ideal for legal, compliance, healthcare, gov benefits, finance, and knowledge-heavy ops. I’ve shipped policy/document RAG for U.S. benefits programs (SNAP / Medicaid / TANF) where citation accuracy mattered.
Machine Learning Tools
Amazon SageMaker, ChatGPT, GitHub Copilot, Google AutoML, Google Data Studio, Google Sheets, GPT-3, Keras, Microsoft Excel, MLflow, NLTK, NumPy, NVIDIA AI Platform, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Sonnet, Tableau, TensorFlow, Tesseract OCR, Vertex AIWhat's included
| Service Tiers |
Starter
$3,000
|
Standard
$5,000
|
Advanced
$7,000
|
|---|---|---|---|
| Delivery Time | 7 days | 15 days | 30 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 3 | 6 | 10 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Frequently asked questions
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DH
Dawson H.
May 26, 2025
Firebase + Stripe + Mux Integration Dev (Backend MVP for Comedy Streaming Platform)
Incredibly Danyal was professional, responsive, and great to collaborate with during the planning phase of our project. Although we ultimately decided to take the build in a different direction before work began, I was genuinely impressed by his proposal and communication throughout. I’d happily consider working with him again on future projects.
RN
Raghu N.
Aug 24, 2024
Python Web Scraper code for Indeed and LinkedIN job posting
Danyal has been very helpful and has been extremely flexible.
PD
Peter D.
Apr 8, 2024
Data science and geospatial APIs
BH
Brent H.
Jan 2, 2024
Developer Needed to Build Web Calculator
Danyal is wonderful. He's smart, talented, and hard working. He built a calculator that went beyond my expectations. I highly recommend him.
DT
Dunc T.
Mar 11, 2023
Build a Flask Website for Google Cloud Platform
I had a pleasure working with him, and would recommend him.
About Danyal
AI Agents & Automation | n8n / Make | SaaS MVPs, RAG, Voice AI
100%
Job Success
Kuwait City, Kuwait - 11:00 am local time
Recent work includes Ilmora (white-label AI learning platform for universities), Miracle Model AI (Next.js SaaS with AI image/video pipelines + Stripe), a government-grade RAG system for Rhode Island eligibility programs (SNAP / Medicaid / TANF), and production n8n/Make systems for real estate lead qualification and ads ROI reporting.
What I build:
AI Agents & Workflow Automation End-to-end systems for lead qualification, follow-up, reporting, content ops, and internal processes. Built on n8n and Make, with custom API integrations, webhooks, Airtable/CRM logic, and human-in-the-loop where it matters. Examples: real estate lead scoring with GPT + automated daily follow-ups; Meta/Google ads ROI reporting to Slack every 15 minutes; AI blog and Instagram posting pipelines that run on a schedule.
AI SaaS & Full-Stack Products Multi-tenant SaaS from zero to production: auth, billing, dashboards, affiliate systems, and multi-API orchestration. Stack: Next.js, Node.js, Python, Stripe, Firebase/Postgres, Google Cloud. Shipped products include Ilmora, Miracle Model AI, and SpyingAgent(competitor Facebook Ads tracking).
RAG & LLM Applications Grounded AI that cites real sources instead of guessing. Policy/document RAG, multi-agent research systems, chatbots, and content/data pipelines using OpenAI, Claude, and custom retrieval layers.
Scraping & Data Automation High-volume scrapers and cleaning pipelines across marketplaces, social, and ad networks — including backends that process 20,000+ leads/day with deduplication.
Why clients hire me I’ve also managed $21M+ in paid ad spend across Meta, Google, YouTube, and TikTok. So when I automate ad or lead workflows, I understand the business logic — not just the APIs.
I don’t ship brittle Zapier templates. I build production systems that handle volume, edge cases, and real ops.
If you need an AI agent, automation system, SaaS MVP, or RAG product, let’s talk.
Steps for completing your project
After purchasing the project, send requirements so Danyal can start the project.
Delivery time starts when Danyal receives requirements from you.
Danyal works on your project following the steps below.
Revisions may occur after the delivery date.
Data assessment & architecture
Audit formats, goals, chunking strategy, vector DB choice.
Pipeline engineering
Ingestion, OCR, embeddings, LLM orchestration, citation layer.