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

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.

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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.

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.

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Your focus on eliminating hallucinations in regulatory document analysis is the right priority, especially when dealing with high-stakes PDF data where source attribution is mandatory. I have built several Ask Your Data platforms using Next.js where every answer must be grounded in specific document chunks. For your MVP, I will implement a robust retrieval pipeline that forces the model to cite specific pages and paragraphs, ensuring 100% traceability for every generated response.
I plan to use a vector database to handle the semantic search before passing the context to the system. To give you an idea, a simplified retrieval flow looks like this:
const docs = await vectorStore.similaritySearch(query, 4);
const context = docs.map(d => d.pageContent).join(' ');
const prompt = 'Use only this context to answer: ' + context + ' Question: ' + query;
const response = await model.generate(prompt);
This setup guarantees that if the answer isn't in your regulatory files, the system will explicitly state that instead of guessing. I am ready to start on the Next.js architecture immediately.

Looking forward to discussing your project in detail.

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.

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

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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.

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Good afternoon. I am ready to implement.

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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.

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.

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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!

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

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.

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

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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

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.

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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.

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 🚀

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