You will get Develop an Agentic RAG AI Chatbot with "Zero-Trust" Hallucination Control
Project details
If your current AI chatbot invents policies, makes up technical steps or gives wrong answers, the issue isn’t your prompts, it’s the architecture. A generic GPT‑only chatbot cannot admit when it doesn’t know something; it will always generate a plausible answer based on its broad training data. This is a liability for any small or mid‑sized business that needs strict business rules and up‑to‑date information.
In this project you will get a closed‑loop retrieval‑augmented generation (RAG) system. Instead of guessing, the system retrieves verified facts from your proprietary knowledge base and live web sources, asks clarifying questions until it understands the problem, and then synthesizes a response solely from the retrieved data. Hallucinations are reduced to near‑zero because the system stops and escalates when the knowledge base lacks the answer. This service is ideal for small and mid‑sized businesses that need a dependable AI assistant for customer intake, sales enablement, support, or operations. It is also a fit for teams who have experimented with prompt‑engineered chatbots and were disappointed by hallucinations and maintenance overhead.
In this project you will get a closed‑loop retrieval‑augmented generation (RAG) system. Instead of guessing, the system retrieves verified facts from your proprietary knowledge base and live web sources, asks clarifying questions until it understands the problem, and then synthesizes a response solely from the retrieved data. Hallucinations are reduced to near‑zero because the system stops and escalates when the knowledge base lacks the answer. This service is ideal for small and mid‑sized businesses that need a dependable AI assistant for customer intake, sales enablement, support, or operations. It is also a fit for teams who have experimented with prompt‑engineered chatbots and were disappointed by hallucinations and maintenance overhead.
AI Algorithms
Feedforward Neural Network, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Multimodal Large Language Model, Transformer Model, Variational AutoencoderAI Applications
AI Chatbot, Conversational AI, Machine Translation, Natural Language Generation, Neural Style Transfer, Sentiment Analysis, Sequence Modeling, Text RecognitionAI Development Language
PythonAI Models
GPT-4What's included
| Service Tiers |
Starter
$499
|
Standard
$899
|
Advanced
$1,999
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 2 | 3 | 5 |
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 David
Senior AI Automation Engineer (RAG/LLM) | Full Stack Python (FastAPI)
82%
Job Success
Atlanta, United States - 10:52 pm local time
For over 20 years, my philosophy has been simple: "If you’re going to do a job, do it right." I don't just digitize your messy processes or apply "band-aid" fixes; I remove operational friction entirely by building practical, scalable systems that save time, improve accuracy, and let your team focus on growth.
The Fortune 500 Value I Bring to Your Business:
I have engineered high-stakes automation for major enterprises, including a Python-based platform for a Fortune 500 retailer that processed 10,000+ invoices resulting in more than $25 million in annual profit by eliminating manual overhead. I bring that exact same enterprise-grade architectural rigor to your business.
What I Build:
AI Agents & RAG Systems: I design closed-loop, hallucination-free AI assistants. Recently deployed "Computron" an agentic scoping system using LangGraph, FastAPI, and the Claude/OpenAI APIs to conduct multi-turn user interrogations and output structured business logic.
Intelligent Automation: Replacing fragile spreadsheet/Zapier setups with robust Python workflows, API integrations, and cloud-native serverless pipelines (GCP Cloud Run / Azure/AWS).
Excel, VBA & Access Database Guru: Fixing broken legacy MS Access databases, optimizing slow workbooks, and fully automating financial/operational reporting using VBA and Power Query.
Full Stack Web Apps: React and FastAPI applications that turn fragmented data into clean, interactive user experiences (e.g., reducing an enterprise grant review cycle by 1 full month).
CRM Architecture and Migration (Salesforce & HubSpot): Apex, LWC, HubSpot Operations Hub (Custom Coded Actions), and Advanced Workflows. I build the connective tissue between your CRM and external tools (Slack, AP platforms, Shopify) so your sales data perfectly aligns with your operations.
E-Commerce Automation & Custom Development: I build the backend pipes that make your storefront run. I’ve engineered full Python-based supplier integrations that pull product/cost data via API into custom assortment dashboards, allowing merchants to select and push products directly to their store.
My Tech Stack:
AI/ML: RAG, LangChain, LangGraph, Pinecone, ChromaDB, Claude API (Certified Developer), OpenAI API
Backend & Cloud: Python (3.12+), FastAPI, Django, Google Cloud Platform (GCP), Azure, SQL (PostgreSQL, MS SQL), AWS
Frontend & CRM: React.js, Node.js, JavaScript (ES6+), Salesforce (Apex/LWC), HubSpot
Other Tools: MS Excel, Google Sheets, MS Access Databases PowerAutomate, MS Forms, VBA, SSRS, SSIS, Talend
I don't build "pie-in-the-sky" AI chatbots. I use AI to solve real operational bottlenecks — using advanced prompt engineering with strict XML mapping templates to perfectly regulate how it responds.
I work best with growing companies and mission-driven organizations that want to solve a problem once and solve it right. If you want to stop patching leaks and start building a system that scales, let's talk.
(Please ignore the following SEO keywords used for Upwork matching algorithms)
🚀 Keywords: AI Developer, AI Agent Development, RAG Developer, Generative AI, LLM Integration, LangChain, n8n Automation, Workflow Automation, Python FastAPI Developer, React Developer, AI SaaS, API Integrations, Business Process Automation, Salesforce Developer, Salesforce to HubSpot Migration, E-commerce Integration, Shopify API, Access, VBA, Excel Automation, Advanced Excel Formulas, ETL, Database, Google Cloud Run, Registered App, Google Sheets Automation
Steps for completing your project
After purchasing the project, send requirements so David can start the project.
Delivery time starts when David receives requirements from you.
David works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & requirements (1–2 days)
After purchase we schedule a call to understand your use case, gather domain documents, define user flows and success criteria.
Data ingestion & vectorization (1–3 days)
Your documents are chunked, embedded using high‑dimensional vectors and loaded into a vector store. We also configure any required web‑search APIs.