You will get a production AI agent that automates one workflow end to end
Project details
The AI work that pays back fastest is often the boring part: the repetitive, high-volume task your team does by hand every week. I build one agent that owns that task end to end, with a human check on anything that touches money or customer data.
You get a working agent on your own data, wired with the guardrails that keep it safe in production: scoped access, fallbacks, logging, and evaluations, plus documentation your team can run with. The top tier adds RAG over your documents and integration into the tools you already use.
Proof: I have shipped a customer-service agent handling 50,000+ daily queries that cut resolution time 65% and lifted CSAT 40%.
You get a working agent on your own data, wired with the guardrails that keep it safe in production: scoped access, fallbacks, logging, and evaluations, plus documentation your team can run with. The top tier adds RAG over your documents and integration into the tools you already use.
Proof: I have shipped a customer-service agent handling 50,000+ daily queries that cut resolution time 65% and lifted CSAT 40%.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AIOps, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, PyTorch, TensorFlowAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$2,500
|
Standard
$7,500
|
Advanced
$12,000
|
|---|---|---|---|
| Delivery Time | 10 days | 21 days | 30 days |
Number of Revisions | 1 | 2 | 2 |
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
About Md Waquar
GenAI Tech Lead | Staff GenAI Engineer | GenAI Architect
Mumbai, India - 12:26 am local time
Where I can help:
- AI agents that handle a repetitive, high-volume task from start to finish, with a human check on anything involving money or customer data.
- RAG systems that answer questions from your own documents, policies, and contracts, and show the source for every answer so your team can trust it.
- Connecting AI to the tools you already use, with the safeguards that keep it dependable day to day: controlled access, safe fallbacks, and ongoing quality checks.
- AI strategy: working out which processes are actually worth automating, defining the result each one should deliver, and estimating real cost and return before any building starts.
Key outcomes:
- Customer-service AI handling 50,000+ daily queries: 65% reduction in resolution time and a 40% increase in CSAT.
- Hybrid ML and LLM retention engine: 60% reduction in churn and a 6% increase in sales within three months.
- Enterprise RAG platform: 95% answer accuracy with 60% faster query resolution.
- Enterprise AI transformation program: $28M in measured business impact and 50+ solutions deployed to 95% organizational adoption.
Technical expertise: LangChain, LangGraph, LlamaIndex, MCP, multi-agent frameworks, OpenAI, Anthropic, Gemini, vector databases (Milvus, Qdrant, Weaviate, Pinecone), AWS Bedrock, Azure AI, Vertex AI, Python, FastAPI, Docker, and Kubernetes.
You will hear from me with a clear update every day, and what I hand over is a working system your team can actually use, not just a report or a set of slides. Tell me the task that is costing you the most of your time or money, and I will lay out a practical plan to fix it.
Steps for completing your project
After purchasing the project, send requirements so Md Waquar can start the project.
Delivery time starts when Md Waquar receives requirements from you.
Md Waquar works on your project following the steps below.
Revisions may occur after the delivery date.
Scope and design
I confirm the workflow, map the agent's actions, and design a safe architecture for it.
Build with guardrails
I build the agent on your data with scoped access, fallbacks, logging, and evaluations.