You will get PDF extraction and RAG with high accuracy
Project details
Accurate, traceable data extraction powered by a live backend. Includes QC, validation against your criteria, and delivery in your chosen format with integration support.
AI Algorithms
Large Language ModelAI Applications
AI Text-to-Image, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Models
ChatGPT, GPT-3, GPT-4What's included
| Service Tiers |
Starter
$40
|
Standard
$500
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 1 day | 3 days | 30 days |
Number of Revisions | 5 | 5 | 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 | - | - | - |
Optional add-ons
You can add these on the next page.
Tableau Workbook
(+ 2 Days)
+$200
1 review
(1)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
RA
Rakesh A.
Sep 3, 2025
Data enrichment using GPT
In the age of chatGPT-generated responses, Lonel stood out with his original hand-crafted response. He followed that up with quality and quick solution. Will go to him again for any future LLM-related work.
About Lionel
LLM Engineer 4 Financial Document Analysis and Credit Risk Automation
Watford, United Kingdom - 11:01 am local time
# LLM Engineer — Financial Document Analysis & Credit Risk Automation
I build AI systems that read financial documents like an analyst.
Recent project: an LLM-powered pipeline that ingests quarterly/annual reports (PDF), extracts and reasons over tables, and produces qualitative signals aligned with credit-rating workflows. I also built a high-accuracy PDF reader that answers complex questions by interpreting tabular data—not just the surrounding text.
## What I do
* Parse and normalize financial PDFs (including complex tables)
* Q\&A over filings: “ask the PDF” with reliable, source-grounded answers
* Qualitative credit-risk scoring prototypes aligned to rating criteria
* RAG pipelines over multi-document corpora (10-Ks, investor decks, factsheets)
* Clean APIs, dashboards, and delivery to your stack
## Tech I use
Python, Pandas, PyPDF/PDFPlumber, OCR, vector databases (FAISS/PGVector), LangChain/LlamaIndex, OpenAI/Anthropic models, FastAPI, Postgres.
## Why clients hire me
* Finance-literate: I understand statements, footnotes, and disclosure nuance
* Table-first accuracy: tuned specifically for tricky tabular layouts
* Production focus: robust evaluation, guardrails, and clear deliverables
**Deliverables:** working code repo, API/CLI, documentation, and a quick demo.
**NDA-friendly.** Let’s discuss your use case.
Steps for completing your project
After purchasing the project, send requirements so Lionel can start the project.
Delivery time starts when Lionel receives requirements from you.
Lionel works on your project following the steps below.
Revisions may occur after the delivery date.
Share documents & requirements and acceptance criterias if any
You provide files (PDF, Excel, scanned, or web content) and fill in the requirements form.
Step 2 – Sample extraction & review
I run your files through my live backend and provide a small sample for you to confirm formatting and traceability.