Construction of the Intelligent Document Analysis platform (Next.js + RAG)

Budget: 800 PLN Deadline: 3 days

Good day.

I can implement the MVP of the platform "Ask Your Data" for analyzing regulatory documents with RAG architecture and controlled response generation.

What I can do within the project:

— frontend on Next.js with a user-friendly upload and search interface
— processing of PDF / TXT documents
— building a RAG pipeline: chunking, embeddings, retrieval, reranking
— generating responses based only on the found fragments
— linking the response to specific sources / excerpts of the document
— basic mechanisms to reduce hallucinations
— architecture suitable for further scaling

It is especially important in such a project to correctly build not only the interface but also the logic of response control:
the model should not "invent," but should respond only within the confirmed context. This can be implemented through strict context limitation, citation of sources, and proper retrieval logic.

I can also help determine the optimal stack for the MVP:
Next.js + backend API + vector store + LLM provider.

To start, it is advisable to clarify:
— what volume of documents is planned
— what languages the documents are in
— whether citations / references to sources are needed in the response
— whether there will be multi-user access

I am ready to discuss the architecture and propose a practical plan for implementing the MVP. Expand

Budget: 15000 PLN Deadline: 10 days

Hello.
I have carefully reviewed the project description for creating the Intelligent Document Analysis platform with RAG architecture. I understand the task of creating an MVP tool that will analyze documents (PDF/text) and generate accurate responses to the user based on this data.
I can implement the solution architecture using Next.js for the interface and integrating a document search system with subsequent response generation. It is important to ensure correct processing of data sources and minimize inaccurate model responses through the proper structure of document processing.
I suggest discussing the data structure, document format, and expected functionality of the MVP to determine the optimal architecture and development stages.

Budget: 700 PLN Deadline: 5 days

Build an MVP. Stack: Next.js frontend, FastAPI backend, PostgreSQL + pgvector for vector database, LangChain for RAG pipeline. Upload PDF via PyMuPDF, chunking with overlap, embedding through OpenAI ada-002. Answers with citation of source and page number, to eliminate hallucinations. Question: what documents will be analyzed (bank regulations, GDPR, others)? And how many documents in the first version? 5 days, 700 PLN.

Budget: 1000 PLN Deadline: 1 day

Hello. I have been working with Next.js.I'm ready to cooperate.

Budget: 2000 PLN Deadline: 10 days

Good day. I am interested in your project. I can implement an MVP platform with RAG architecture for working with regulatory documents, where the user asks a question, and the system responds strictly based on the uploaded PDF or text documents with source citations.

Budget: 4800 PLN Deadline: 11 days

I was working on poseidon.codezerogroup.com — a web platform in Next.js with a Python backend and integration of external APIs, which technically aligns with what you need for the RAG platform on regulatory documents.

The "Ask your data" architecture requires precise selection of chunking method, embedding model, and source validation — this is what distinguishes a functioning MVP from a prototype that hallucinates. I will build a RAG pipeline based on LangChain + pgvector (or Chroma) with a mechanism for citing specific document excerpts and metrics for evaluating response quality.

What I will do:
- Ingestion pipeline: upload PDF/TXT, chunking, embeddings (OpenAI/HuggingFace), saving to vector DB
- RAG backend: retriever + reranker, responses with a list of cited excerpts
- Next.js interface: document upload panel, Q&A window with source preview
- Eval pipeline: faithfulness + relevance metrics (RAGAS or custom)
- Deployment: Docker + .env, ready to run on VPS or cloud

--- OPTIONS ---

- Option A (Basic MVP): 4800 PLN (11 days) — ingestion + RAG backend + Q&A UI with source control + eval
- Option B (Production MVP): 7680 PLN (14 days) — Option A + user auth, multi-document collections, admin panel, deployment on server — best scope/price ratio
- Option C (Advanced MVP): 9990 PLN (21 days) — Option B + analytics dashboard, REST API for external integration, fine-tuned retrieval

Completion time: 11 days from the handover of the API key (OpenAI or own endpoint) and sample regulatory documents.

Portfolio:
- https://poseidon.codezerogroup.com — Next.js web platform + Python backend, API integration
- https://ou-uv.com — Flask/Python CMS, multilingual support, integration of external services
- https://codezerogroup.com — B2B system with own CMS

8 years in web development and AI — from simple API integrations to full RAG platforms with a Python backend.

If you want, I can send an example of a working RAG pipeline on your type of data before signing the contract — just let me know what set of documents we are working with.

Since I am new to the freelancehunt service and want to quickly gain a few initial projects for my portfolio, I am offering a 15% discount for the first 5 clients. The offer is valid until 5 orders are obtained. Expand

Budget: 550 PLN Deadline: 3 days

Cześć, Marcinie!

Your core challenge - trusted answers from a fixed document set, zero hallucinations — is something I've solved before.
For a software consultancy, I built a RAG assistant on Flowise that answers strictly from vectorized company documents and explicitly refuses to go outside them. For an HR system, I built an AI agent in n8n backed by Supabase with structured, source-controlled outputs.
Both projects share the same problem you're describing. (you can check in my portfolio)

For your MVP, I'd start with a short discovery call — regulatory documents have nuances that directly affect chunking strategy and retrieval accuracy. That conversation usually prevents a lot of rework.

I work with n8n as the orchestration layer, and I'm flexible on the AI and vector store stack.

Budget: 2500 PLN Deadline: 7 days

I have extensive experience in development with React (Frontend) and Node.js/Python (Backend), so I am ready to take on the project as a whole (Full-stack).

My stack for your task:

Frontend: React, HTML5/CSS3 (Sass/Tailwind), responsive design for mobile devices.

Backend: Node.js (Express) or Python (Django/FastAPI) — depending on what is best suited for the project's logic.

Databases: PostgreSQL, MongoDB, or MySQL.

Why you should choose me:

I write clean, maintainable code without unnecessary libraries.

I always meet deadlines and am available for communication.

I pay attention to details: loading speed, security, and UX/UI.

I would be happy to discuss technical details in the chat. Expand

Budget: 700 PLN Deadline: 3 days

Hello!

I’m ready to help develop an MVP platform for analyzing regulatory documents and generating answers based on a RAG (Retrieval-Augmented Generation) architecture, with controlled output and source attribution.

What can be implemented at the MVP stage:

• Upload of PDF and text documents
• Splitting documents into semantic blocks
• Indexing using a vector database
• Retrieval of relevant fragments before answer generation
• Generating answers solely based on the retrieved sources
• Displaying references to specific document fragments

Key focus on reducing hallucinations:

• Answers are built only on extracted context
• Generation outside the document database is restricted
• Confidence scores are monitored
• Option to return “answer not found in documents” when data is insufficient

Typical MVP tech stack:

• LLM + retrieval pipeline
• Embeddings + vector search
• Backend API
• Web interface for Q&A

Additional possibilities:

— Differentiation between document types
— Updating the database without retraining
— Logging of queries
— Architecture prepared for future scaling

I’m ready to discuss the MVP format, proposed tech stack, and estimated cost after clarifying the document volume and use cases. Expand

Budget: 1000 PLN Deadline: 2 days

Good day. I am ready to complete this project as I have extensive experience in application development.

Budget: 700 PLN Deadline: 5 days

Good day. I have been programming professionally for 4 years. During this time, I have created more than 5 successful MVPs. I have worked on both web development and AI development. If needed, I can send my portfolio in private messages. I would be happy to collaborate with you.

Budget: 75 PLN Deadline: 1 day

Good afternoon. I am ready to implement.

Budget: 850 PLN Deadline: 1 day

✋ Hello! We are the IT company dZENcode.

We are implementing the MVP "Ask Your Data" with RAG architecture: frontend on Next.js, backend on Python, integration with a vector storage, and citation of sources to improve the accuracy of answers, excluding incorrect data, based on the team's experience, best practices, and our own developments.

Is there a ready database or document structure for processing?
Preferred embedding storage: pgvector, Qdrant, Pinecone?

You can find detailed information about our services and rates on our website: Freelancehunt
Take a look – we will discuss the details of the work further, write when you are ready.

The final cost is determined only after clarifying the volume and requirements.

___________________
Sincerely,
Manager of dZENcode

Our strengths:
💎 10+ years providing IT services: Outsourcing, Outstaffing
🔥 90+ in-house specialists
🚀 Projects "from scratch" and for support
⚙️ SLA and post-production support
✅ Contract with the company, guaranteed result!
🔥 250+ public reviews since 2015. Expand

Budget: 100 PLN Deadline: 1 day

Hello,

I would be glad to help you build the MVP platform for analyzing regulatory documents and generating accurate responses using a RAG (Retrieval-Augmented Generation) architecture. I have experience working with modern web technologies and AI integrations, and I understand the importance of building systems that rely on verified sources rather than generating uncontrolled answers.

For this project, I can implement a solution where documents such as PDFs and text files are processed, indexed, and stored so that user questions are answered strictly based on the provided materials. The system can use vector embeddings and semantic search to retrieve the most relevant sections of the documents, and the language model will generate responses using only those sources. This approach helps significantly reduce hallucinations and keeps full transparency over the origin of the answers.

I can build the platform with a clean and scalable architecture, including document ingestion, indexing, a question-answer interface, and clear citation of document sources in each response. The system can also support uploading new documents, filtering data, and improving the retrieval process as the dataset grows.

I would be happy to discuss your requirements in more detail and help design a reliable MVP that demonstrates the core functionality of your platform.

Best regards. Expand

Budget: 2000 PLN Deadline: 10 days

Good day. I can implement an MVP platform on Next.js + RAG for document analysis with controlled responses based on sources, uploading PDF/text, and reducing hallucinations. I am ready to discuss the stack, stages, and cost.

Budget: 2000 PLN Deadline: 10 days

Hello!

I see your project as a platform for precise document analysis using Next.js and RAG architecture. My expertise is in processing PDF/text, building RAG pipelines for reliable answer retrieval without hallucinations, integrating LLM, and creating an MVP with data source control.

I can quickly assemble a working prototype with document upload, answer generation, and accurate linking to sources, with the ability to scale and expand functionality.

I am ready to discuss the architecture, timelines, and start immediately.

Thank you for your attention!

Budget: 5000 PLN Deadline: 10 days

Hello, Marcin

I can build your MVP from scratch as fast as possible.
I have prepared architectural patterns to run production ready pipelines.
Only the best practices and modern tools will be used in delived code.

Write me PM, waiting U.

Budget: 800 PLN Deadline: 5 days

Hello, I will do a turnkey project for you. Quickly and efficiently. The deadline is up to 5 days.

Budget: 10000 PLN Deadline: 15 days

Hi,
I’m excited to apply for the Programmer – MVP Platform for Regulatory Document Analysis role. With strong experience in RAG architecture, NLP, and data-driven applications, I specialize in building tools that extract accurate insights from large text datasets while maintaining full control over source reliability.

Key strengths I bring:
⚙️ Expertise in PDF/Text parsing, vector databases, and RAG pipelines
🤖 Skilled in automating responses with minimal hallucination using verified sources
🧠 Strong focus on scalable, maintainable MVP development

I’m eager to contribute my technical skills to create a robust “Ask your data” solution that delivers precise, reliable answers for your users.

Thank you for your time and consideration.

Best regards,
Jeo Vincent Carretas Expand

Budget: 1000 PLN Deadline: 3 days

Good morning!

I have experience in creating applications on Next.js and implementing RAG architectures for analyzing PDF/text documents. I am ready to build a precise "Ask your data" platform, ensuring source control and elimination of hallucinations.

I invite you to contact me to discuss the details.

Budget: 4300 PLN Deadline: 5 days

Hello Marcin!

RAG-based document analysis is exactly what we do daily at FlipFactory. We currently run a production RAG system with 836+ document chunks, vector search, and Claude API — powering our internal knowledge base with zero hallucinations.

Directly relevant experience:
✅ FlipAudit — automated document analysis platform (PDF parsing, AI-powered insights, source citations)
✅ Production RAG pipeline: PDF ingestion → text chunking → vector embeddings → semantic search → Claude API with grounded responses
✅ 12 MCP servers in production (TypeScript, published on npm)

Technical approach for your MVP:
1. Document ingestion — PDF/text upload, smart chunking with overlap for context preservation
2. Vector store — ChromaDB or Pinecone for embeddings (OpenAI ada-002 or Cohere)
3. RAG pipeline — semantic search → top-K retrieval → Claude API with strict source grounding
4. Hallucination control — every answer includes exact source citations (document name, page, paragraph). No source = no answer. Confidence scoring per response.
5. Frontend — Next.js 15 + TypeScript + Tailwind, chat-style UI with source highlighting
6. API — RESTful endpoints for document management, querying, and admin

Stack: Next.js 15, TypeScript, Claude API (Sonnet for speed, Opus for precision), PostgreSQL + pgvector (or ChromaDB), Vercel deployment

Timeline: 5 days to working MVP with document upload, search, and cited answers.

Portfolio: flipfactory.it.com

Happy to discuss architecture details — we can start immediately. Expand

Budget: 2500 PLN Deadline: 7 days

Hello!

I have experience in developing AI systems based on RAG (Retrieval Augmented Generation) for working with corporate and regulatory documents. The main focus of such systems is source control of responses, minimizing hallucinations, and accurate citation of documents, which aligns well with your task.

Technology stack used:

Backend

Python FastAPI or Django
LangChain / LlamaIndex (RAG pipeline)
PostgreSQL
pgvector or Qdrant (vector search)

AI / NLP

OpenAI / Claude / local LLMs (if needed)
embeddings for semantic search
chunking + reranking for response accuracy

Document Processing

PDF parsing (PyMuPDF / pdfminer)
OCR if needed (Tesseract)
pipeline for document indexing

Frontend

Next.js / React
chat interface like “Ask your documents”
Infrastructure

Docker

background jobs (Celery / Redis)
AWS / VPS

Best regards,
Andriy 🚀 Expand

Budget: 1800 PLN Deadline: 12 days

hello,

this project is less about building a simple “chat with pdf” interface and more about creating a controlled RAG workflow where answers are grounded in the uploaded documents and the system keeps full traceability of sources.

that is exactly the right way to approach this kind of tool, especially for regulatory and text-heavy documents where hallucinations are the main risk.

for the mvp, i would focus on the parts that actually matter:

document upload and parsing for pdf/text

structured chunking and indexing

reliable retrieval pipeline

answer generation based only on retrieved context

source references / traceability in responses

next.js interface for asking questions and reviewing results

the most important point here is to make the system useful and trustworthy, not just “ai-looking.” in this kind of product, retrieval quality and source control are more important than model creativity.

my estimate for a solid first mvp would be around 1800 pln and 12 days.

one important question before starting: for the first version, do you want the system to always show source excerpts/citations with each answer, or is that planned for a later stage? Expand

Budget: 900 PLN Deadline: 5 days

Hello. The project looks interesting and large-scale. If you are planning to create an MVP for document analysis, I am ready to help with the development. Before starting, we need to clarify some details. Are there already defined requirements for the functionality? What database is planned to be used? Regarding the deadlines, considering the necessary verification and testing, I believe that implementation may take approximately 5 days. The price is from 600-800 UAH per hour, depending on the complexity of the project.