You will get an AI chat assistant that answers from multiple company documents
Project details
You will get a custom AI chat assistant that reads your company documents and answers questions in plain language, grounded entirely in your actual content — no hallucinated answers, no guessing.
Most chatbots only work with a single document. I build assistants that handle multiple document categories at once — policies, SOPs, manuals — and automatically detect which category (or combination) a question belongs to before retrieving the answer. Ask about returns and it searches your Sales policy; ask something that spans stock and purchasing, and it searches both, then combines the results into one coherent answer.
I bring 12+ years of technical project delivery experience to this, so you're not just getting a workflow — you get a properly scoped, tested, and documented handoff: a working n8n automation, a branded chat interface your team can actually use, and clear setup instructions. I care about delivering something that works the first time you open it, not a rough prototype you have to debug yourself.
Most chatbots only work with a single document. I build assistants that handle multiple document categories at once — policies, SOPs, manuals — and automatically detect which category (or combination) a question belongs to before retrieving the answer. Ask about returns and it searches your Sales policy; ask something that spans stock and purchasing, and it searches both, then combines the results into one coherent answer.
I bring 12+ years of technical project delivery experience to this, so you're not just getting a workflow — you get a properly scoped, tested, and documented handoff: a working n8n automation, a branded chat interface your team can actually use, and clear setup instructions. I care about delivering something that works the first time you open it, not a rough prototype you have to debug yourself.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Tools
Hugging FaceAI Models
LaMDAWhat's included
| Service Tiers |
Starter
$150
|
Standard
$350
|
Advanced
$650
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 8 days |
Number of Revisions | 1 | 2 | 3 |
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 |
Optional add-ons
You can add these on the next page.
Additional Revision
+$50
Extra document category
(+ 2 Days)
+$100Frequently asked questions
About Allavudeen
AI Workflow Automation Specialist | n8n - Make - AI Agents
Tiruchirappalli, India - 6:52 pm local time
With 12+ years of experience as a Technical Project Manager, I've led offshore cross-functional teams, managed client delivery, and handled complex integrations — so I understand exactly where workflows break down and how to fix them permanently with automation.
What I build:
🔷 n8n Automations — RAG Document Intelligence Agents, Conversational Email Agents, Gmail-to-Jira ticket pipelines, and custom multi-step AI workflows
🟠 Make Automations — Standup summarisers, sprint retro processors, blocker escalation bots, and AI-powered status report generators
🤖 AI Agents — LLM-powered agents using Google Gemini and Groq with vector databases (Supabase pgvector), persistent PostgreSQL memory, and structured outputs
My tech stack:
n8n · Make · Google Gemini · Groq API · HuggingFace · Supabase · PostgreSQL · Gmail API · Jira API · Slack · Google Sheets · Python
What makes me different:
I bring a Project Manager's mindset to every automation — requirements first, edge cases handled, documentation delivered. You get a production-ready workflow, not a fragile prototype.
Currently building a 30-automation public portfolio on GitHub — every build is tested, sanitized, and documented.
If your team is spending time on tasks a workflow could handle in seconds — let's talk.
Steps for completing your project
After purchasing the project, send requirements so Allavudeen can start the project.
Delivery time starts when Allavudeen receives requirements from you.
Allavudeen works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements review
I review your submitted document(s) and confirm document categories, tone, and any branding preferences for the chat UI before starting.
Document ingestion & embedding
Your documents are chunked, tagged by category, and embedded into a vector database so the assistant can search them accurately.