You will get AI customer support agent with RAG knowledge base and escalation
Rising Talent
Project details
Generic AI chatbots answer from training data and hallucinate when they don't know. That destroys customer trust fast. I build AI
Customer Support Agents grounded in your actual documentation FAQs, product manuals, help articles, internal wikis so every answer is accurate, traceable, and on-brand.
It is a production support system with RAG retrieval, confidence scoring, escalation logic, CRM sync, and full audit trails.
WHAT I DELIVER:
✅ RAG knowledge base ingests your docs, PDFs, URLs, Notion, Confluence, or internal database
✅ Semantic search retrieval answers grounded in your actual content, with source citations
✅ Confidence scoring agent knows when it does not know and escalates instead of hallucinating
✅ Smart escalation creates structured tickets with conversation summary, issue tag and priority score
✅ CRM / helpdesk sync Zendesk, Intercom, HubSpot, Freshdesk
✅ Sentiment detection flags frustrated or high-value customers for immediate human routing
✅ Multi-channel website chat, WhatsApp, Slack, email
✅ Analytics dashboard resolution rate, escalation rate, top unanswered questions, CSAT trends
✅ Works with OpenAI, Anthropic, Azure OpenAI, and self-hosted LLMs
Customer Support Agents grounded in your actual documentation FAQs, product manuals, help articles, internal wikis so every answer is accurate, traceable, and on-brand.
It is a production support system with RAG retrieval, confidence scoring, escalation logic, CRM sync, and full audit trails.
WHAT I DELIVER:
✅ RAG knowledge base ingests your docs, PDFs, URLs, Notion, Confluence, or internal database
✅ Semantic search retrieval answers grounded in your actual content, with source citations
✅ Confidence scoring agent knows when it does not know and escalates instead of hallucinating
✅ Smart escalation creates structured tickets with conversation summary, issue tag and priority score
✅ CRM / helpdesk sync Zendesk, Intercom, HubSpot, Freshdesk
✅ Sentiment detection flags frustrated or high-value customers for immediate human routing
✅ Multi-channel website chat, WhatsApp, Slack, email
✅ Analytics dashboard resolution rate, escalation rate, top unanswered questions, CSAT trends
✅ Works with OpenAI, Anthropic, Azure OpenAI, and self-hosted LLMs
AI Algorithms
CycleGAN, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language Understanding, Sequence Modeling, Text Recognition, Time Series ForecastingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, Jasper AI, PyTorch, StreamlitAI Models
BERT, ChatGPT, GPT-4, LLaMA, Naive Bayes Classifier, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$700
|
Standard
$2,000
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 6 days | 15 days | 25 days |
Number of Revisions | 1 | 3 | 6 |
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 |
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CR
Carmen R.
May 11, 2026
Senior Distributed Systems Architect for MCP Security Gateway
It was a great pleasure working with Talha.
SC
Shariann C.
Apr 11, 2026
Software Developer for Advanced AI Projects (Agentic LLM Platform)
He seemed to know what he was doing but he was very delayed in deliverables so we could no longer work together.
JL
Jesse L.
Aug 30, 2023
Formatter for Phonetics/Translation book (Using type setter)
Work wasn't bad, Talha completed the task and patiently revised multiple requests in details and formatting. Was polite and responded fairly quickly to messages.
PA
Pavel A.
Mar 12, 2023
Stable Diffusion Trainer is needed
Talha did a good job of training a custom stable diffusion model
About Talha
Agentic AI Engineer | RAG, LangChain, LangGraph | LLM Chatbot & Python
100%
Job Success
Dina Mor, Pakistan - 12:40 pm local time
Most "AI freelancers" ship a working prototype and call it done. I build for what happens after launch: scale, security, and monitoring from day one, so you're not rebuilding it in six months.
WHAT I BUILD
Full-Stack & SaaS Development: frontend and backend engineering for web apps, mobile apps, CRMs, and custom software, built with React and Next.js on the frontend and Python, FastAPI, and Node.js on the backend, backed by PostgreSQL, Redis, Docker, and AWS. Multi-tenant architecture with RBAC, Stripe billing, and white-label support, scalable to 50,000+ concurrent users.
AI Agents & Multi-Agent Systems: autonomous agents that reason, plan, and execute across complex workflows with LangChain, LangGraph, CrewAI, and Model Context Protocol (MCP). Orchestrator-executor architectures with human-in-the-loop checkpoints built in from the start.
RAG & LLM Pipelines: Retrieval Augmented Generation with hybrid semantic search, embeddings, and vector database optimization across Pinecone, Qdrant, Weaviate, ChromaDB, and pgvector, with sub-100ms query latency. LLM fine-tuning with LoRA, DSPy, and RAFT for legal, medical, and financial data.
AI Chatbots & Conversational Agents: customer support agents and conversational AI with escalation, memory, and multi-turn context handling, integrated directly into your existing product and CRM.
AI Automation & Integration: n8n, Make, Zapier, and GoHighLevel workflows wired into your existing tools, CRMs, and legacy systems. Function calling and strict structured outputs for reliable, repeatable runs.
AI Security: prompt injection defense, PII detection, knowledge-graph audit trails, and guardrail layers for regulated healthcare and fintech, on top of OpenAI and Anthropic Claude deployments.
LLMOps & Observability: LangSmith, Langfuse, Helicone, LiteLLM, and Guardrails AI for tracing, evaluation, cost monitoring, and reliability engineering in production.
RESULTS
73% hallucination reduction on a fintech platform at 1M+ transactions/day. 60% reduction in manual ops cost for healthcare and real estate. 100+ systems shipped across freelance and full-time roles, for 35+ businesses worldwide.
SELECTED WORK
MCP Security Gateway, secure and observable gateway controlling agent tool access. AI Voice Agent SaaS, platform that catches missed calls and pushes leads into a CRM flow. LeadIntel AI, lead-generation SaaS with AI automation and personalized outreach. StudyFlow OS, enterprise AI learning platform with retrieval-grounded answers.
INDUSTRIES
Healthcare, Fintech, Legal, Real Estate, B2B SaaS, Logistics, Insurance, E-Commerce, MedSpa, Staffing & Recruiting, Education & EdTech, Cybersecurity, Proptech, and Wellness & Health Tech.
STACK
Backend: Python, FastAPI, Node.js, REST, GraphQL. Frontend: React, Next.js. Infra: AWS, Docker, Kubernetes, CI/CD, Redis, PostgreSQL. LLMs: OpenAI (GPT-4o, GPT-4.1), Anthropic Claude, Gemini 2.5 Pro, LLaMA 4, DeepSeek R1, Mistral, Ollama. Agentic: LangChain, LangGraph, CrewAI, LlamaIndex, MCP. Automation: n8n, Make, Zapier, GoHighLevel. Vector DBs: Pinecone, Qdrant, Weaviate, ChromaDB, pgvector. LLMOps: LangSmith, Langfuse, Helicone, LiteLLM, Guardrails AI. Project Management: ClickUp.
HOW I WORK
I start by scoping the actual problem and the success metric, not the tooling. You get a shared ClickUp board with the full task breakdown and timeline from the start, so you always know where things stand without asking for a status update. I check in every 3 to 5 days, flag risks before they become blockers, and ship working demos early instead of one big reveal at the end.
WHO I WORK WITH
Founders and teams who need a full-stack build with AI woven in from the start, whether that's a new product, an internal tool, or an agent layer added to something that already exists. From scratch, from a hallucinating RAG system, or a multi-agent platform, I take it from architecture to deployment.
WHAT YOU GET
Production-ready code, architecture diagrams, test coverage, deployment scripts, and a short handover doc so nothing lives only in my head. Built to scale, secure by default, and easy for your team to extend.
Open to hourly, fixed-price, and ongoing retainer engagements. If this sounds like what you need, with full visibility every step of the way, send me a message and let's scope your project. I reply quickly.
Steps for completing your project
After purchasing the project, send requirements so Talha can start the project.
Delivery time starts when Talha receives requirements from you.
Talha works on your project following the steps below.
Revisions may occur after the delivery date.
Step:
Client purchases the project and sends requirements.
Step:
Review of your knowledge sources, support channels, escalation logic and helpdesk integration requirements.