Private Local LLM Developer: SQL Data Room Setup & On-Premise LLM Deployment (NDA Required)

@Tafsol

Hi, So, Basically you need a complete private AI environment where your internal business data can be securely consolidated, structured, retrieved, and used by the model without leaving your infrastructure. -/ What systems and databases will feed the SQL data room, and approximately how much historical data is involved? -/ Will the LLM mainly answer questions from documents, perform natural-language-to-SQL queries, generate reports, summarize operational data, or support multiple workflows? -/ Do different departments or users require different permissions to datasets? -/ Should the AI only read data, or will it eventually be allowed to create/update records or trigger internal actions? Once I have answers to these questions and a clearer understanding of the requirements, I’ll be able to suggest the best approach, suitable technology, timeline, and accurate cost for the project. Freelancer didn't allow me to write more than 1500 words, Let's jump over an on-site chat or on-site freelancer call to walk through everything and then proceed from there. Best regards, Tafsol(MKA) Budget and Duration are TBD

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@hammadali963

Private Local LLM Developer: SQL Data Room Setup & On-Premise LLM Deployment (NDA Required) needs an implementation that stays reliable at the boundaries: PostgreSQL, MySQL, AI, LLM, RAG. The key risk is usually not the happy path; it is inconsistent inputs, failed integrations, duplicate work, and unclear ownership when an operation needs to be retried. I would map the current flow and acceptance criteria first, then define the API and data contracts, validation, permissions, background jobs, retries, logging, and tests. I would build the smallest vertical slice, connect the required services, and verify it with realistic fixtures before expanding the remaining modules. For Private Local LLM Developer: SQL Data Room Setup & On-Premise LLM Deployment (NDA Required), that means keeping the user-facing flow simple while making state transitions observable and recoverable. My closest evidence is FieldCore AI, where I built a document intelligence and RAG pipeline for complex technical PDFs, and L'invite, where I led a production natural-language-to-SQL platform handling 1M+ API calls/month. I would apply the same discipline here without assuming an unfamiliar tool is already in place. Your description should be treated as the release checklist: each named deliverable gets an acceptance test, a documented failure path, and a clear handover note. Bid amount, milestones, weekly allocation, and delivery period will be discussed and finalized. The delivery will be clear for handover.

@extreamcode

Hello, The hidden challenge here is not simply running a local LLM, it is creating a trustworthy data path where SQL history, retrieval quality, access control, and operational ownership all work together without exposing sensitive information. I can collaborate with your IT team to shape the architecture, design the SQL data room, and connect it to a secure on-premise RAG workflow with clear testing and documentation. My relevant focus includes MySQL, SQL, Database Administration, and Microsoft SQL Server, including structured historical storage, data quality, permissions, query performance, and maintainable integration boundaries. I would help evaluate the server constraints and model options, then establish an appropriate retrieval design using a local deployment stack and vector storage while keeping data and logs under your control. I’m also comfortable working under an NDA and transferring knowledge to your internal team. I’ve shared an initial estimate based on your description, and once we go over a few technical or functional details, I’ll confirm the exact cost and delivery schedule. We can begin with an infrastructure and data-source review, followed by phased implementation and validation. Which SQL sources, server specifications, and security or approval rules should the local LLM support for data retrieval and natural-language-to-SQL? Best regards, Asad A few important considerations are which SQL systems and source applications must be synchronized, what server

@kend27

Hi, I'm a software and data engineer with hands-on LLM evaluation and RAG experience, plus a background as a Senior Azure Data Engineer building SQL/Delta data platforms. Proposed approach: start with RAG plus text-to-SQL over a PostgreSQL/SQL Server data room rather than fine-tuning, since it's cheaper, keeps answers traceable to your data, and is easier for your team to maintain. Stack: vLLM or Ollama serving Llama/Qwen (sized to your GPU), pgvector for embeddings, LlamaIndex for retrieval, with role-based access and full query logging. Timeline and cost: Phase 1 – Discovery: 1 week, $600 Phase 2 – SQL data room: 2–3 weeks, $2,000 Phase 3 – LLM deployment: 2 weeks, $1,800 Phase 4 – Testing and handover: 1 week, $800 I'm available for meetings with your IT team on weekdays and happy to sign the NDA upfront. Could you share your server's GPU/RAM specs and main data sources? That will let me firm up the model choice and estimate. Best regards, Ken

@VirasatSolutions

Hello! I can help design and deploy a secure, private LLM environment that runs entirely on your own infrastructure, with a practical architecture your IT team can operate and extend. ++ Proposed approach: → Discovery: review server capacity, security controls, source systems, users, and priority use cases → Architecture: recommend the best local model, such as Llama, Mistral, or Qwen, based on accuracy, language, hardware, and licensing needs → SQL data room: design a clean PostgreSQL, SQL Server, or MySQL layer with ingestion, data-quality rules, historical storage, and governed access → RAG pipeline: add embeddings, vector search, citations, retrieval controls, and evaluation datasets rather than allowing uncontrolled model “learning” from live data → Local deployment: deploy through Ollama or vLLM, with a secure API, role-based access, audit logs, and no external AI dependency → Testing: validate answer accuracy, latency, permissions, data leakage protection, and failure handling → Handover: provide runbooks, architecture documentation, source code, and knowledge-transfer sessions I’m comfortable working under NDA and collaborating directly with internal IT teams. I would structure the engagement by phases, with a discovery milestone first so model choice, hardware requirements, timeline, and per-phase cost are grounded in your actual infrastructure and data volume. Best regards, Abhay

@ankits0192

Hello, I understand you need a private AI environment where sensitive business data remains on-premise while the LLM can securely retrieve and work with structured company information. I would begin by reviewing your server resources, existing SQL systems, data volume, security requirements, and intended use cases. From there, I can define whether RAG, fine-tuning, or a combined approach is appropriate and select a suitable open-source model and serving architecture. The SQL data room will be structured for clean ingestion, historical storage, controlled access, and reliable retrieval. I’ll then connect the local LLM through a RAG pipeline with appropriate embeddings/vector storage, keeping external cloud AI services out of the data path. Testing will cover retrieval accuracy, permissions, logging, performance, and security. I’ll also provide clear deployment documentation and knowledge transfer for your IT team, with NDA requirements respected throughout the project. Looking forward to working with you. Thanks

@rajat07me

A private on-prem LLM only works if the data layer is designed properly first; otherwise the model becomes the easy part and retrieval accuracy, access control, and maintainability become the real risk. I’d approach this in phases: review your server/infrastructure with the IT team, define the SQL data room schema and ingestion flow, then deploy the local model stack with RAG and controlled access/logging around it. I’ve worked on self-hosted AI and database-connected automation setups where privacy, internal hosting, and handover mattered more than flashy demos. For a project like yours, the practical decisions are usually model fit vs hardware limits, vector store choice, chunking/embedding strategy, and how the SQL layer should be structured so your team can extend it later without rebuilding everything. I can also break the work into clear phases with cost per phase before we start. - What server environment are you running now (OS, CPU/GPU, RAM, storage)? - Which SQL source are you using today: SQL Server, PostgreSQL, or MySQL? - Do you want the first version focused on retrieval over existing business data, or do you already expect fine-tuning in scope?

@Azursol

Dear , I understand the importance of a secure, on-premise AI solution for your project and am excited about the opportunity to collaborate with your team. With over a decade of experience in SQL, UML Design, and Machine Learning, I have successfully deployed open-source LLMs on local servers, ensuring data security and privacy. Drawing from my portfolio, which showcases similar projects, I propose a meticulous approach to your project. Starting with a thorough discovery phase, I will design and build a structured SQL data room, followed by the seamless deployment of the LLM on your server. Testing, handover, and documentation will ensure a smooth transition for your IT team. I recommend leveraging tools like Ollama and LangChain for optimal results. Let's discuss how we can achieve your project goals efficiently. Looking forward to further discussing your project needs. Best regards,

@naminghn

Hi there, I noticed your project requires expertise in deploying local LLMs and developing a structured SQL data room. With my extensive experience in deploying open-source models and solid SQL skills, I can help design a data room that effectively organizes your historical data while ensuring it connects seamlessly with the chosen LLM. I recommend utilizing a combination of RAG and fine-tuning for optimal results, ensuring that your internal IT team is involved at each phase for smooth collaboration. I anticipate that each phase can be completed within the estimated timeline, with clear documentation provided to facilitate easy maintenance afterwards. Looking forward to discussing the details further. Regards, Ali

@RusianD

Hi there, I'm Ruslan, a seasoned developer with extensive expertise in AI Model Development, Database Management, Machine Learning (ML), and SQL. Having successfully deployed open-source LLMs on local servers in the past, I'm well-equipped to deliver a safe, on-premise AI solution for your project while respecting data privacy concerns. Given my familiarity with RAG pipelines, vector databases, embedding models, and tools like Ollama and LangChain among others, I'm capable of generating the best-suited approach for your specific needs. Working closely with your IT team, I will understand your current infrastructure deeply, design a structured SQL data room aligned with long-term utility, and configure an open-source LLM on your server while ensuring secure access controls and logging. Moreover, I value security and clear communication foremost. Collaborating with you on this project would be not only about the job but also ensuring smooth maintenance and future scalability. My past clients will vouch for my commitment to confidentiality; I'm ready to sign a Non-Disclosure Agreement (NDA) to protect your sensitive business data. Trust me with the responsibility of this project, and let's create an amazing AI solution together.

@jayantkumar314

With over six years in software engineering, I have gained considerable experience implementing and deploying AI/ML solutions

@sanjana22kr

hello there , I can help your IT team build a fully private, on-premise LLM platform where business data, inference and processing remain within your infrastructure. Core Implementation Infrastructure and data-source discovery with your IT team Architecture selection: Llama/Qwen/Mistral + RAG/fine-tuning based on requirements Structured SQL data room using PostgreSQL/SQL Server/MySQL Data ingestion, cleaning, normalization and historical data storage RAG pipeline with embeddings + vector database Local LLM deployment using Ollama/vLLM LangChain/LlamaIndex-based retrieval and orchestration Secure authentication, RBAC, encryption, logging and audit trails Accuracy, performance and security testing Complete documentation and IT-team knowledge transfer Recommended approach: Start with RAG for controlled access to your business data, then introduce fine-tuning only where it provides measurable value. I’m experienced with Python, PostgreSQL, SQL Server, FastAPI, LangChain, vector databases, OpenAI/local models, Ollama, vLLM, Docker and secure AI deployments. I’m comfortable signing your NDA and working closely with your internal IT team. All code, data and deployment components remain within your controlled environment. thanks

@SoftSandila

Happy to sign the NDA first. What decides whether this works is your hardware. Three questions: 1. What is the model running on - GPU and which one, or CPU only, and how much RAM? That one fact sets the largest model you can serve at a useful speed. On CPU-only hardware I would plan for a smaller model and a retrieval-first design, because a large model will be installed and practically unusable. 2. Which systems hold the data today, and what has to come into the data room? Mostly spreadsheets and an ERP export is straightforward; several live systems makes the pipeline the real project. 3. Have you decided between retrieval and fine-tuning, or is that what you want advised on? And any restriction on model family - Llama, Mistral, Qwen? How I would approach it: a short discovery session with your IT team first, because an architecture recommendation is worthless if it ignores what you actually run. Then the SQL data room - a documented schema fed from your sources, built to keep history rather than overwrite it. Then an open-weight model served on your own hardware, connected to that data through a retrieval layer so answers come from your data with the source cited. Deployment documented, including how to update the model later without me. Worth raising early: the accuracy ceiling is set by the data room's quality, not the model. Answer the three and I will send the phase plan and a fixed quote.

@starttechno

I’d start by mapping your existing SQL sources and server constraints, then choose between Llama, Mistral, or Qwen based on available GPU/RAM rather than forcing a model choice. The data room would use PostgreSQL with pgvector, clean historical tables, metadata, access roles, and an auditable ingestion process; the LLM would run locally through Ollama or vLLM and use a LlamaIndex RAG layer, with no external AI or data calls. Within 5 days, I’ll deliver the SQL data room, local model deployment, retrieval API, secure configuration, logging, validation queries, and concise IT handover documentation. I’ll work under your NDA and coordinate with your IT team through Freelancer, including scheduled project meetings, while keeping the implementation modular for future fine-tuning or model replacement. Is PostgreSQL already available on the target server, or should I include its installation in the initial setup? Muhammad Saad

@Aaleen110

The choice between Llama, Mistral, or Qwen for your private LLM hinges on inference speed and parameter count that fits your hardware, and I would lean towards Mistral 7B or Qwen 7B for a balance of performance and resource requirements, so you can get a capable LLM running locally without needing an enterprise-grade GPU. I will set up a PostgreSQL database for your SQL data room, creating a schema designed for efficient querying by the LLM, and I will use LangChain to orchestrate the data retrieval and LLM interaction, so the RAG implementation is clean. Instead of a standard RAG setup, I would build a custom data loader within LangChain that directly indexes your SQL query results into a vector store, bypassing the intermediate step of exporting to CSV or JSON, so your data stays fresher and retrieval is faster. This approach to indexing directly from SQL means the LLM always accesses the most current data for its responses. I have a 5.0 rating across 10 reviews on here, and Preferred Freelancer status. What is the anticipated volume of data queries your LLM will handle per day on average? A short call on Freelancer to cover the proposed SQL data room schema and the specific vector database integration for RAG.

@vishal1145

Your SQL data room will become a bottleneck if you don't implement proper indexing and query optimization before connecting the LLM - retrieval latency over 500ms will make the system unusable for real-time queries. Quick questions - are you planning to use vector embeddings alongside SQL for semantic search, or purely relational queries? And what's your target hardware spec for inference speed? Here is the architectural approach: - OPEN-SOURCE LLM DEPLOYMENT: I'll benchmark Llama 3.1, Mistral, and Qwen on your hardware to identify which model delivers sub-200ms inference at your required context window. - SQL DATA ROOM + RAG PIPELINE: I'll design a hybrid architecture using PostgreSQL with pgvector for semantic search plus optimized relational tables, connected via LangChain for retrieval-augmented generation. - ON-PREMISE SECURITY: I'll configure role-based access controls, audit logging, and network isolation so your LLM never touches external APIs while maintaining full query traceability. I've deployed 4 private LLM systems for healthcare and fintech clients under HIPAA and SOC2 compliance where data sovereignty was non-negotiable. Let's schedule a technical call with your IT team to validate infrastructure requirements before we commit to a deployment strategy.

@jaroslavcaprata

Hi, I can help design and implement a private LLM environment where sensitive business data remains entirely within your infrastructure. My proposed approach would be: • Discovery of your infrastructure, data sources, security requirements, and expected workloads • SQL data-room architecture using PostgreSQL, SQL Server, or MySQL as appropriate • Data ingestion, cleaning, normalization, and historical data organization • RAG architecture connecting structured SQL data with relevant document/knowledge sources • Local open-source LLM deployment based on your available CPU/GPU resources • Embedding and vector-search layer for retrieval • Secure API/application layer between the LLM and data room • Role-based access, audit logging, and controlled database access • Evaluation of retrieval accuracy, response quality, latency, and resource usage • Deployment documentation and knowledge transfer for your IT team For model/runtime selection, I would evaluate options such as Llama, Qwen, or Mistral together with Ollama or vLLM based on your hardware and workload rather than selecting a model prematurely. I’m also comfortable working alongside an internal IT team and can sign your NDA before accessing confidential infrastructure or data. I would first review your server specifications, database sources, expected users, and query types, then provide a precise phase-by-phase implementation plan and estimate. Best regards, Jaroslav

@richard031596

You need a fully private LLM environment where business data remains on-premise, with a structured SQL data layer and RAG pipeline connecting that data to the local model. My strongest match is on the application and data side: Python, PostgreSQL, LangChain, RAG, OpenAI/LLM integration, embeddings, API development, and database architecture. I can design the SQL data room, data ingestion and normalization layer, retrieval pipeline, access controls, logging, and the application layer connecting these components. For the local model, I would evaluate an open-source model such as Llama, Mistral, or Qwen based on your server's GPU/CPU/RAM constraints rather than selecting one prematurely. Ollama or vLLM can then be evaluated as the serving layer, with the final choice based on performance and operational requirements. I don't want to claim specific prior Ollama/vLLM or on-premise deployment projects that I cannot substantiate. I would therefore begin with an infrastructure and data assessment, establish a small RAG proof of concept, then proceed to the production architecture once retrieval quality and resource requirements are validated. Estimated phases: Discovery 10–15h, SQL/data layer 25–40h, RAG + local LLM integration 30–50h, testing/documentation 15–25h. Can you provide the server specifications and current database systems so the model and serving architecture can be selected properly?