You will get Production RAG System, Evaluated and Observable
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Project details
Most RAG systems work in demos. They fall apart when real users ask questions in their own words, with their own vocabulary, against your actual data.
This catalogue delivers a production retrieval augmented generation system built specifically around your knowledge base, your users, and your quality standards.
What you get: a complete RAG pipeline with document ingestion, embedding generation, hybrid retrieval, semantic caching, and streaming responses. Evaluation built in from the start using RAGAS and DeepEval measuring faithfulness, answer relevancy, and contextual recall. Langfuse observability on every query so you can see retrieval latency, token costs, and cache hit rates in real time.
Custom options include vector database setup using Qdrant or Pinecone, semantic caching for cost reduction at scale, streaming response delivery, and LLM optimization across cost, latency, and quality for your specific query volume.
If your knowledge base exists but your AI cannot search it reliably, this is the engagement that fixes it.
This catalogue delivers a production retrieval augmented generation system built specifically around your knowledge base, your users, and your quality standards.
What you get: a complete RAG pipeline with document ingestion, embedding generation, hybrid retrieval, semantic caching, and streaming responses. Evaluation built in from the start using RAGAS and DeepEval measuring faithfulness, answer relevancy, and contextual recall. Langfuse observability on every query so you can see retrieval latency, token costs, and cache hit rates in real time.
Custom options include vector database setup using Qdrant or Pinecone, semantic caching for cost reduction at scale, streaming response delivery, and LLM optimization across cost, latency, and quality for your specific query volume.
If your knowledge base exists but your AI cannot search it reliably, this is the engagement that fixes it.
AI Algorithms
Large Language Model, Long Short-Term Memory Network, Multimodal Large Language ModelAI Applications
AI Chatbot, AI Text-to-Speech, AI-Generated Code, AIOps, Conversational AI, Synthetic Data GenerationAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, StreamlitAI Models
ChatGPTWhat's included
| Service Tiers |
Starter
$499
|
Standard
$1,799
|
Advanced
$4,499
|
|---|---|---|---|
| Delivery Time | 10 days | 18 days | 28 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 |
Frequently asked questions
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AH
Ali H.
Oct 15, 2025
Voice AI Consultant | Retell.ai + LangGraph Agents for CRM Automation
This guy delivered exactly what we needed. I especially appreciate his detailed knowledge of LangGraph, highly recommended for any LangChain & LangGraph or Voice AI Agent projects.
About Muhammad
AI Engineer | Agentic AI | Multi-Agent Systems | LangGraph | RAG
100%
Job Success
Karachi, Pakistan - 7:31 pm local time
Your agent worked perfectly in the demo. Three weeks into production and nobody can explain why it gave a wrong answer, what it cost to run last week, or whether things are getting worse. That is what happens when large language models go live without evaluation, observability, or cost controls baked in. Most teams only find out after it is already a problem.
Founders, ops teams, and CTOs hire me when they need autonomous AI agents and LLM-powered systems that plug into real workflows and stay reliable after launch. No black boxes. No surprise API bills. No agents doing things nobody signed off on.
AWS Community Builder | AWS Agentic AI Certified | 5+ years in cloud engineering before AI.
➤ What That Looks Like in Practice
A property management company was manually reviewing every tenant maintenance request. Each one took 2-4 hours to classify, route, and action. Built an AI triage and dispatch agent using LangGraph that now handles 60-85% of requests with zero manual review. Response time dropped from 2-4 hours to under 30 minutes. Manager capacity doubled without adding a single hire. Shipped with a production eval suite and CI regression gates that catch bad changes before they reach tenants.
➤A shared AI helpdesk needed to serve multiple companies on one system with zero data bleed between them. Built a multi-tenant support agent with per-tenant namespacing, rate limits, token budgets, and tool permissions tied to each company's own policies. Every action is logged. Shipped with 6 adversarial attack tests in CI that must all fail before any code reaches production. Security is enforced by the infrastructure, not filtered after the fact.
➤A customer support RAG system was slow, expensive, and returning inconsistent answers. Rebuilt the retrieval pipeline with semantic caching and streaming. Perceived wait time dropped 2.6x. Costs dropped 6.4x. The root cause was a vocabulary gap between how customers phrase questions and how the knowledge base was indexed. Fixed at the source. System now handles 1M queries per month at $400-600 all-in.
(Full case studies with technical breakdown in portfolio below.)
➤What I Build For You
1. Agentic AI Systems and Multi-Agent Workflows
Custom LangGraph agents, multi-step tool calling, autonomous AI agents, AI chatbot systems, and human-in-the-loop controls built for real operational AI workflow automation. Handles complex business processes without breaking when conditions change.
2. RAG Pipelines and Knowledge Systems
Production retrieval-augmented generation backends with vector database integration, hybrid retrieval, semantic caching, and streaming responses. Systems that retrieve accurately and stay cost-efficient at scale.
3. LLM Evaluation, Optimization and Observability
Evaluation frameworks built before the agent ships. LLM-as-a-judge graders, prompt engineering, regression test suites, CI gates, and Langfuse or LangSmith traces on every run. LLM optimization across cost, latency, and quality so the system improves instead of drifting.
4. AI Guardrails and Production Hardening
Deterministic checks around every non-deterministic model call. PII redaction, RBAC, token budgets, cost caps, and adversarial attack testing. Built for teams that need AI compliance automation and are operating under regulatory frameworks including the EU AI Act.
5. Generative AI App Development and Cloud Infrastructure
FastAPI backends on AWS ECS, Lambda, and Bedrock. Full-stack generative AI app development from architecture to deployment. CI/CD pipelines, Docker, and production-grade infrastructure built by an engineer with 5+ years in cloud before AI.
➤Tech Stack
AI and Agents: Python, LangGraph, LangChain, OpenAI Agents SDK, Claude Agents SDK, AWS Strands, Pydantic AI
RAG and Retrieval: Qdrant, Pinecone, Haystack, Redis, hybrid retrieval, semantic caching
Evaluation and Observability: RAGAS, DeepEval, Langfuse, LangSmith, LLM-as-a-Judge, pytest, GitHub Actions
Infrastructure: FastAPI, AWS ECS, Lambda, Bedrock, Docker, PostgreSQL, MCP, A2A
Frontend: React, Next.js
Every day your team spends reviewing, routing, and manually actioning requests is a day your agentic system should have handled automatically.
Send me a message describing the workflow your team is stuck on right now. I will come back within 24 hours with a straight answer on whether AI fixes it, what it will cost to run, and whether you should hire me at all.
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 call & corpus review
30-min call within 24 hrs of requirements. We confirm scope, walk through your sample documents, agree on success criteria, and lock the evaluation question set. You leave with a written brief.
Ingestion pipeline & vector index
Build the ingestion side: parse your documents, apply parent–child chunking, embed, and index in a Vector Database. PII redaction runs at this stage if your tier includes it.