You will get an AI knowledge base with cited answers from your documents
Project details
You will get an AI knowledge system that allows your team to ask questions and receive accurate, cited answers from your own documents.
Most teams I work with are sitting on hundreds of PDFs, internal docs, and knowledge bases, but finding the right answer still takes 10-20 minutes, or requires asking someone who “just knows”. This replaces that entirely.
After this build:
→ Your team asks a question in plain English
→ The system finds the answer from your documents
→ Every response includes the exact source (document, page, section)
→ If the answer isn’t there, it doesn’t guess
The difference is reliability. This isn’t a basic chatbot or prompt setup. It’s a structured system designed to retrieve the right information and return it with clear references you can verify.
Before handover, I test the system against real questions from your domain and provide an accuracy report, so you know exactly how it performs on your data.
You receive a complete, working system, with your documents, your use case, and full control going forward.
Most teams I work with are sitting on hundreds of PDFs, internal docs, and knowledge bases, but finding the right answer still takes 10-20 minutes, or requires asking someone who “just knows”. This replaces that entirely.
After this build:
→ Your team asks a question in plain English
→ The system finds the answer from your documents
→ Every response includes the exact source (document, page, section)
→ If the answer isn’t there, it doesn’t guess
The difference is reliability. This isn’t a basic chatbot or prompt setup. It’s a structured system designed to retrieve the right information and return it with clear references you can verify.
Before handover, I test the system against real questions from your domain and provide an accuracy report, so you know exactly how it performs on your data.
You receive a complete, working system, with your documents, your use case, and full control going forward.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, StreamlitAI Models
BERT, ChatGPT, GPT-3, GPT-4What's included
| Service Tiers |
Starter
$2,200
|
Standard
$4,200
|
Advanced
$6,800
|
|---|---|---|---|
| Delivery Time | 8 days | 12 days | 18 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.
Priority Delivery
+$600
Extended Testing
+$400Frequently asked questions
About Anesah
AI Agent & Automation Engineer | LangChain, Python, n8n, RAG, APIs
London, United Kingdom - 6:06 am local time
systems that help businesses capture more revenue and eliminate manual work: lead
capture and qualification systems, AI customer support agents, workflow
automation, RAG knowledge bases, multi-agent pipelines, and executive
dashboards.
Available for fixed-scope projects and ongoing contract engagements.
I work across the full AI automation stack:
→ Multi-agent orchestration (LangGraph, LangChain)
→ RAG knowledge systems (LangChain, ChromaDB, vector search)
→ Workflow automation & orchestration (n8n, Python, webhooks)
→ AI-powered extraction, classification, and routing
→ Custom Python backends and data pipelines (FastAPI, PostgreSQL)
→ Executive dashboards (Power BI)
I also work with complex, process-driven environments, including
chemical, manufacturing, and pharmaceutical settings, where the domain
context is critical.
Background: MSc Computer Science (AI/ML), Chemical Engineering (IChemE-accredited), platform security engineering at Intigriti (bug bounty platform serving 300+ organisations, including defence-critical tech suppliers), with experience supporting operations for Morgan Stanley and Knight Frank
Stack: Python, FastAPI, LangChain, LangGraph, n8n, Zapier, Make, OpenAI/Claude API, ChromaDB, PostgreSQL, Power BI, Docker, Slack API, Airtable, Google Sheets, HubSpot, Stripe, REST APIs
Steps for completing your project
After purchasing the project, send requirements so Anesah can start the project.
Delivery time starts when Anesah receives requirements from you.
Anesah works on your project following the steps below.
Revisions may occur after the delivery date.
Review documents & confirm approach
I review your documents and questions to confirm structure, extractability, and expected accuracy. I’ll outline the approach and highlight anything to address before build.
Ingest & structure documents
Documents are processed, cleaned, and structured into a searchable knowledge base optimised for accurate retrieval.