AI LLM (opensource on local Server) for PDF data analysis

@corpmember29

HELLO, I HAVE CAREFULLY REVIEWED YOUR REQUIREMENTS AND FULLY UNDERSTAND THE SCOPE. I AM AN EXPERIENCED AI LLM CONSULTANT WITH 10+ YEARS IN DEVELOPING AND DEPLOYING AI SOLUTIONS, INCLUDING FINANCE-SECTOR PROJECTS. I WILL PROVIDE 2 YEAR FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE, AND WE WILL WORK WITH AGILE METHODOLOGY TO DELIVER THE SYSTEM FROM ZERO TO FULL DEPLOYMENT, INCLUDING PROOF OF CONCEPT, API ACCESS, AND WEB INTERFACE. I WILL DESIGN AN ON-PREMISE, WINDOWS-NATIVE LLM SYSTEM CAPABLE OF READING LARGE PDF FILES WITH COMPLEX TABLES, USING FINANCIALLY-PRETRAINED OPEN-SOURCE MODELS (Mistral, FinBERT, Bloom, FinGPT, etc.). THE ARCHITECTURE WILL INCLUDE DATA EXTRACTION MODULES, LLM INFERENCE PIPELINE, API INTERFACE, AND OPTIONAL WEBUI, ALL OPERATING FULLY OFFLINE WITHOUT GPU REQUIREMENTS. I WILL IMPLEMENT STRATEGIES TO REDUCE HALLUCINATIONS AND ENSURE HIGH ACCURACY, WITH STEP-BY-STEP ACTIVITY PLAN AND DOCUMENTATION. I EAGERLY AWAIT YOUR POSITIVE RESPONSE TO START THE PROOF OF CONCEPT AND FULL IMPLEMENTATION. THANKS

@andrijaar2

Hello! As an experienced AI LLM Consultant, I am sure I would be a great fit for accomplishing the development of a system capable of reading large PDF files and analyzing their data. I am proficient in backend and frontend development, as well as in AI integrations, so I don't see any unexpected challenges in meeting the deadline we agree on. I'd love to discuss the project details and make sure we're aligned before starting. Looking forward to your message. Cheers, Andrija!

@Herculesz

Hi The hard part here is not just running an LLM offline on Windows, but extracting highly variable financial tables from 300+ page PDFs with low hallucination and stable accuracy. I can design an on-prem pipeline that combines deterministic PDF/table extraction, document chunking, validation rules, and a quantized finance-oriented local model served on Windows without GPU dependency. Rather than relying on raw prompting alone, I would use a retrieval and verification flow so answers are grounded in extracted source data and can be traced back to the exact PDF sections. The system can expose both REST APIs for your VB/ASP/ASP.NET applications and a local web interface for ad hoc analysis, while remaining fully offline. I have experience with Python-based AI pipelines, local model serving, structured document processing, API integration, and reducing hallucinations through guardrails and post-check logic. A PoC-first approach makes sense here because the extraction layer and answer validation will determine whether the full project is viable. I can provide a practical architecture, required software stack, and a detailed implementation plan focused on accuracy, maintainability, and clean deployment on Windows servers. Thanks, Hercules

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Effective AI LLM Solutions for PDF Analysis in Finance ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for an AI LLM Consultant. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in AI solutions. I will create a system to read large PDF files, analyze their data, and ensure it operates fully offline without a GPU. ➡️ Why Me? I can easily build your AI LLM system as I have 5 years of experience in AI and machine learning. My expertise includes PDF data extraction, API development, and model deployment. Additionally, I have a strong grip on relevant technologies like Python, which will ensure a successful project outcome. ➡️ Let's have a quick chat to discuss your project in detail. I can showcase samples of my previous work and how I can add value. Looking forward to our conversation! ➡️ Skills & Experience: ✅ AI Solutions ✅ Large Language Models ✅ PDF Data Extraction ✅ API Development ✅ Python Programming ✅ System Design ✅ Financial Sector Experience ✅ Local Deployment ✅ Open-Source Tools ✅ Accuracy Improvement ✅ Project Management ✅ Technical Documentation Waiting for your response! Best Regards, Zohaib

@Softeria

✅ Proposal for AI LLM (opensource on local Server) for PDF data an With a strong background in deploying AI solutions, specifically Large Language Models (LLMs) in the financial sector, I am ideally suited for your project. My experience includes integrating models like FinBERT and Bloom on-premises, ensuring high accuracy and minimal hallucinations without cloud dependencies. I have developed systems for extracting and analyzing data from complex PDF structures, using open-source tools tailored for local server implementation on Windows. My expertise extends to creating robust APIs and ensuring full system functionality offline. I am prepared to demonstrate capabilities through a PoC and commit to rigorous daily updates for project alignment. Lets initiate this project to leverage my technical skills for your needs.

@juanjosec63

Hi, As a individual developer and I can jump into on your suitable time. I can help in your project, focusing on building a local AI service with API/WebService access for your VB, ASP, and ASP.NET applications, plus a secure web interface for direct requests, full deployment, documentation, and internal team training. With my expertise in full-stack development and experience working with modern web technologies like Python, FastAPI, OpenAI-compatible APIs, LLaMA, local LLM deployment, RAG pipelines, vector search, and secure backend integration for business systems, i can fix this quickly. I understand this needs to be delivered as a complete working solution instead of a partial MVP, and I can handle the architecture, API layer, model integration, web interface, optimization, and cleanup of unnecessary files and libraries so the final system is stable, maintainable, and ready for go-live. You can expect clear communication, fast turnaround, and a high-quality result that fits seamlessly into your existing workflow. Best regards, Juan

@kamran2012

Hi, I will build your on-premise LLM system on Windows — PDF table extraction from 300+ page financial documents, a locally deployed open-source model via Ollama, API access for your VB/ASP.NET applications, and a web interface for ad-hoc queries. Fully offline, no cloud dependency. I am available for daily update meetings and will deliver the PoC as the first step. The reason GPT-4o and Mistral failed your tests is likely the table parsing step, not the LLM reasoning. I will use a dedicated table extraction layer (Camelot or pdfplumber) to convert PDF tables into structured data before the LLM ever sees them. Feeding pre-structured data to the model instead of raw PDF pages is what eliminates hallucinations on tabular content — the LLM answers from clean fields rather than guessing cell boundaries. I will prepare the architecture document and activity plan as requested and share it before we proceed. Questions: 1) What is the average number of tables per PDF, and do they span multiple pages? 2) What are the typical queries users will ask — aggregations, comparisons across rows, or full narrative summaries? Looking forward to discussing further. Best regards, Kamran

@rinsadsl

As a seasoned AI developer, my commitment to delivering robust, intelligent solutions is well-aligned with your project needs. My vast experience in deploying AI models for a range of applications involving financial data analysis makes me the ideal fit for your project. I am well-acquainted with Fine-tuning and have worked with prominent Financial LLM models like Mistral, FinBERT, Bloom, etc. Additionally, I am proficient in incorporating open-source tools into my designs like Ollama to optimize your system for substantial offline processing capabilities. One of my fortes is designing APIs and WebUIs that interact seamlessly, irrespective of the complexity of the task or dataset size, ensuring an easy-to-use experience while analyzing large PDF files. I am adept at leveraging cloud-based models when necessary for prompt responses but can assure you precise results with local deployment too. Notably, my proficiency covers your desired technology stack including .py. I encourage a preliminary Proof-of-Concept phase myself to demonstrate my ability to fulfill project expectations. In regards to architecture concerns, I will meticulously configure and document the essential software components and propose a schema design that maximizes your compelling requirements while considering performance optimizations and key factors like high availability (HA) and disaster recovery (DR).

@techplusintl

Hi, To create a system that reads large PDF files and implements an LLM solution, I will utilize Python for data extraction and API development. I understand the importance of accuracy and minimizing hallucinations in the model, and I will focus on using open-source tools and pre-trained models suitable for the financial sector. I will start by listing the required software components and designing the architecture to ensure it meets your specifications. I will also outline a detailed activity plan and address how to improve accuracy and reduce hallucinations. Could you clarify which specific models you prefer for the PoC? Also, do you have any existing infrastructure that we should consider? Please share any additional details or files needed to begin the project. Thanks!

@webmasters486

Hi there, I’ve reviewed your project and understand you need a fully offline, on-premise LLM system on Windows (no GPU) that can accurately extract and analyze complex tables from large PDFs with minimal hallucinations. The core challenge is combining reliable PDF parsing with a controlled LLM pipeline that ensures consistent, verifiable outputs in a financial context. I can design a structured pipeline using Python with tools like PyMuPDF or PDFPlumber for extraction, followed by schema normalization before passing data into a locally deployed model such as FinBERT or a quantized Mistral via Ollama or a custom runtime. To improve accuracy, I’ll implement retrieval-based grounding, strict prompt constraints, and validation layers that cross-check outputs against extracted data. The system will run fully offline, expose APIs for ASP.NET integration, and include a lightweight web interface. I’ll also provide the required document with architecture design, software stack, hallucination control strategy, and a clear day-by-day execution plan, along with a PoC. You’ll receive a clean, optimized solution with full documentation, deployment steps, and team training. I’m ready to align with your timeline and daily updates. Let’s connect and start with the PoC. Best regards, Muhammad Adil Portfolio: https://www.freelancer.com/u/webmasters486

@Shonali1406

With 7+ years of experience in AI/LLM systems and backend architecture, I can design a fully offline, on-premise solution for accurate PDF table extraction and financial data analysis on Windows (CPU-only). I’ve worked with Mistral, FinBERT, and RAG pipelines to reduce hallucinations and improve precision. Skills: • LLMs (Mistral, FinGPT, FinBERT) • Python, API Development • RAG, Vector DBs, NLP • PDF Parsing (Camelot, Tabula) Approach: • Structured PDF parsing + validation layer • Hybrid RAG + rule-based extraction • Local deployment (Ollama/CPU optimized) Let’s connect to discuss the PoC.

@mkh559a5a1fd66e9

I have recently deliver the same process to convert pdf into S1000D schema ruled XML for defense/aerospace industry. This was a bit more complex. This system guves 95-100% accurate results with cloud LLM and 100% .net stack. Now we are using that system, we are also using the same to genrating data to fine tune the SLM, to run off-line. Your case ais a bit simpler it only require to extract tabular data. As your requirement says, that you wants to use it without gpu, thats fine it may require a bit more RAM, Or require to use a tiny model properly quantized. And then fine tune it on mid size dataset. This bidding system allows only 1500 characters texr, no document can be attached. I am able to share documents only chat, and you can initiate the chat. Drop me a message and lets have a short conversation to stay and check on same page. With Regards Maroof K.

@rbtech1984

Hello There!!! ★★★★ (On-premise AI LLM for PDF analysis with high accuracy) ★★★★ I understand you need a fully offline AI LLM system on Windows, capable of analyzing large PDF files with tables, pre-trained in finance, accessible via API and WebUI, and optimized to minimize hallucinations. A PoC is required before full deployment. ⚜ On-premise LLM setup with Mistral/FinGPT/Bloom variants ⚜ PDF parsing & table extraction using Python libraries ⚜ API endpoints for integration with VB/ASP/ASP.NET apps ⚜ Web interface for ad-hoc queries ⚜ Accuracy optimization & hallucination reduction techniques ⚜ Detailed architecture & activity plan with PoC ⚜ Documentation, training, and technical support I have implemented financial LLM systems offline and can design a PoC ensuring accuracy and stable performance without GPU. I’ll provide a clear architecture, task plan, and integrate APIs/WebUI seamlessly. Looking forward to discussing the PoC and next steps. Warm Regards, Farhin B.

@SkillCrafts

I have recently deployed several custom RAG (Retrieval-Augmented Generation) pipelines using Llama 3 and Mistral on local hardware to ensure strict data privacy and eliminate recurring API costs. Your focus on local PDF analysis suggests a need for a secure, high-throughput system that avoids external cloud dependencies while maintaining high extraction accuracy. I can help you architect a solution that balances inference speed with context precision, ensuring your sensitive data remains entirely within your own infrastructure without compromising on performance or intelligence. I will implement a robust RAG architecture using LangChain or LlamaIndex, paired with a high-performance vector database like ChromaDB or Qdrant for efficient document indexing and semantic search. For PDF parsing, I will utilize advanced libraries like Docling or Marker to handle complex layouts, multi-column text, and tables, ensuring the LLM receives clean, structured context for analysis. The model will be served via vLLM or Ollama using GGUF or EXL2 quantization to maximize GPU utilization on your local server, allowing for precise, real-time querying across your entire document library. I will also containerize the entire stack using Docker to ensure seamless deployment and easy scaling within your local environment. To ensure the right hardware alignment, are you planning to run this on consumer-grade RTX GPUs or enterprise-level clusters like A100s? Also, do your PDFs contain many scanned images that would require an integrated OCR layer or a vision-capable model like Llama 3.2-Vision? I am available for a quick chat to discuss the specific libraries that would best fit your local server environment and can provide a detailed deployment roadmap as soon as we connect.

@Feriver

Hello, I can deliver a fully on-premise AI LLM system capable of reading large, heterogeneous PDF files (>300 pages) with tables, extracting structured data, and providing accurate financial analyses. The system will run on Windows servers without GPU, leveraging open-source financial LLMs such as FinGPT, FinBERT, or Bloom, fully offline, and accessed via APIs or a web UI. Accuracy and hallucination reduction will be ensured via prompt engineering, post-processing validation, and optional lightweight fine-tuning on domain-specific datasets. The proposed solution includes a modular architecture: a PDF ingestion and parsing module, LLM analysis engine, local database for caching structured data, API layer for integration with VB/ASP/ASP.NET applications, and a web interface for ad hoc queries. All essential software, libraries, and runtimes will be documented, with minimal footprint and no extraneous components. A detailed daily activity plan will guide PoC, testing, and full deployment. Questions: Are there preferred open-source models for initial PoC, or should I select based on performance? For PDFs with highly variable table structures, is automated schema inference acceptable or should manual mapping be provided initially? Thanks, Asif

@SmartCodeDev

Hello, I can deliver a fully offline LLM system on Windows including the required PoC. I’ll build a robust pipeline to extract and normalize data from large PDFs using a combination of parsing strategies to ensure high-quality structured output before analysis. For accuracy, I’ll implement a RAG-based approach with financial-tuned models and strict validation logic to minimize hallucinations. The system will run entirely on-premise, expose APIs via Python, and include a simple local web UI for direct queries. I’ll provide the full document with architecture diagram, required software, and a detailed day-by-day execution plan, along with a working PoC on your sample files. I’m aligned with daily updates and will deliver a clean, efficient, production-ready setup without unnecessary dependencies.

@techsty2014

Interesting project, I will set up a fully local LLM pipeline on your Windows server — PDF table extraction using a dedicated parsing layer (Camelot or pdfplumber tuned per table structure), feeding into an Ollama-hosted model accessed via REST API and Open WebUI. Your VB/ASP.NET apps will hit the same API endpoints. The key challenge here is table extraction from 300+ page PDFs, not the LLM itself. GPT-4o and Mistral likely failed because the tables were not parsed properly before reaching the model. I will build a preprocessing step that converts tables into structured JSON first, then feeds clean data to the LLM — this dramatically improves accuracy and reduces hallucinations compared to raw PDF ingestion. For the PoC, I will use one of your actual PDF files to demonstrate extraction accuracy and LLM response quality before full project commitment. I will prepare the architecture document you requested with software stack, component diagram, and a day-by-day activity plan. Questions: 1) What are the Windows server specs — RAM and CPU cores available? This determines which local model size is feasible without GPU. 2) Are the PDF table structures consistent within a single file, or do they vary page to page? Looking forward to talking through the details. Faizan

@rahulb058

As a seasoned Full-Stack Developer and Product Management Pro, I'm well-versed in every aspect of your project's requirements. From creating an on-premise AI LLM system for local server use, to developing accessible APIs and Open WebUIs for seamless integration, this is precisely my forte. Additionally, a thorough understanding of running local environments sans GPU will enable me to optimize the performance of the chosen models for all your PDF data analysis needs.

@UzeeTech

Hi, Recently i ahve done one job related to pdf reading and data extraction and in that pdfs we have the equations and figures and images. I have extracted all of them completely with 98% accuracy. I have ready mad system, we can talk in details about your requirements. Lets have a talk, waiting for your reply Thanks