AI assistant (company knowledge base)

Budget: 27000 UAH Deadline: 3 days

The cost of the first stage is 32,000 UAH, duration - 3 working days. This is an audit of the existing prototype with checks on the API, context transfer to the model, document indexing, vector database, authorization, and current code. 4,000 UAH, in my opinion, does not even cover a normal technical audit of such a task, let alone a full service =/

After the audit, we will provide one of two routes - to fix the existing one or to rebuild from scratch. If rebuilding, the working minimum for a RAG assistant with web access, authorization, query history, database updates without a developer, HTTPS, and readiness for future connection of an analytical repository I would estimate separately, approximately 15-30 working days and from 180,000 UAH.

Stack - Python or Node.js for the server, PostgreSQL plus pgvector or Qdrant for vector search, OpenAI or Claude via API, a separate document processor, web interface for managers, role-based authorization, query log. For responses based on documents, it is necessary to add a link to the source or at least the title of the found document; otherwise, managers will not be able to properly control quality.

Estimated ownership - hosting and database 20-80 USD per month, tokens 20-150 USD per month at the start. We will calculate more accurately after the number of managers, average document lengths, and the number of queries per day. The model should not invent facts, so we make responses only with RAG and a scenario when there is no data in the database.

Two clarifications
> Approximately how many documents or pages are in the knowledge base, and in what formats are they currently collected - PDF, Word, Google Docs, spreadsheets, website?
> Does the current prototype already have a vector database, or are the documents just uploaded to the server without proper content search?

Similar cases from Ingello
> https://business.ingello.com/vorfahr - AI automation with applied business logic, close to an internal assistant for the sales team.
> https://business.ingello.com/fractal - agent and system architecture, useful for tasks where chat needs to be part of a managed process.
> https://business.ingello.com/forma-crm - corporate logic, roles, processes, quality control of team work.

Freelancehunt page of Ingello - https://systems-fl.ingello.com/ua

!!The optimal first step is an audit, not a promise of the entire product within a budget of 4,000 UAH.!! It is better to first find where the knowledge transfer to the model is breaking down, and then decide whether to fix the existing one or build anew. Expand

Budget: 4000 UAH Deadline: 7 days

Hello, I worked on an AI assistant for a law firm — a RAG system based on 200+ documents, Pinecone vector database, Claude API, ~$45/month for tokens with 1000 requests.

Regarding your project — can you clarify which vector database is currently used in the prototype and how the documents are transmitted to the model? This will help quickly identify the issue with the API during the audit.

I suggest we get in touch; I will provide you with free technical consultation and we can create a development plan + I will tell you about my team!

Budget: 3800 UAH Deadline: 5 days

Hello! I am ready to start with a technical audit of the existing prototype - I will check the API, the transfer of document context to the model, indexing, and the reason why the database is not being pulled. Stack: Python, PostgreSQL + pgvector (or Qdrant) for vector search, RAG pipeline with OpenAI/Claude API, web interface with authorization for managers, database updates without a developer through file uploads. Responses will be strictly based on the uploaded documents, with a fair fallback if the information is not available. After the audit, I will provide a clear answer - to fix the existing one or to rebuild from scratch.

Budget: 27000 UAH Deadline: 7 days

Hello. I have experience in developing AI automation. I am ready to collaborate.

Budget: 5000 UAH Deadline: 5 days

Hello! This is exactly the task I'm working on — RAG integration, where a ready prototype needs to be "linked" to a knowledge base.

Recently, I delivered a FastAPI panel for managing accounts with background tasks and real-time status (7500 UAH) — a similar architecture: a service that processes data and responds to requests.

For your case, the standard stack: document loading → chunking → embeddings → vector database (Qdrant/Chroma/pgvector) → retrieval chain. If the prototype is already ready, the knowledge base integration will take 3–7 days depending on the document format and the prototype architecture.

The budget of 4000 UAH seems modest — if the volume is small, that's fine; if not, it’s worth discussing. In what format is the documentation stored (PDF, Word, Google Docs)? What stack is used in the prototype?

Budget: 4000 UAH Deadline: 1 day

Good day, Yuriy!
I am ready to take on your project. I am a certified Anthropic developer — Claude Certified Architect (Foundations), specializing specifically in production architecture with the Claude API, MCP, and RAG pipelines. Certificate verification: https://www.credly.com/badges/3df97b6b-e468-42b9-9bd6-c1f78aa309fc
A similar case. Recently, I implemented a similar project - an internal knowledge graph based on Neo4j + Claude through MCP, which answers employee queries strictly according to corporate documentation, with references to the source and without "fabricated" facts. This is almost exactly your task, only I additionally used a knowledge graph for more accurate searching based on the relationships between regulations.

Two clarifications to calculate more accurately:

How many documents/pages are approximately in the database and in what formats (PDF, Word, Google Docs, spreadsheets)?
In the current prototype, is there already a vector database, or are the documents simply uploaded to the server without content search?

I am ready to start with the audit immediately after receiving the code and access.

Budget: 5000 UAH Deadline: 10 days

Hello, I can implement this. I have built RAG systems based on n8n.

Google Drive

Everything except the LLM itself can be deployed on your server. This will be self-hosted n8n + supabase, as a vector storage.

The price will only be for tokens, and it all depends on the number of requests, the accuracy of responses, and the number of documents in the RAG system. How many documents will there be? Through which interface do you want to communicate with the system? And is there a system administrator who can deploy n8n and supabase?

Budget: 4000 UAH Deadline: 1 day

Good day, Yurii.

The most likely reason: the model receives a request, but no document fragments are found as context — retrieval is not connected to the API call. An audit will identify the break point and determine whether to fix or rebuild.

Your RAG pipeline — pgvector and bge-m3 for semantic search.
LLM: Claude Sonnet or GPT-4o-mini with token calculations tailored to your volumes.
The model will respond strictly based on context, without fabrications.
Web interface with authorization, query history, and database updates with files without a developer.
Architecture — with an extension to an analytical repository.

Similar project: RAG on Go + pgvector + OpenRouter.
Stack: Go, pgvector, bge-m3, Claude/OpenAI API, Docker, HTTPS.
I will prepare the monthly ownership cost after discussing the volumes.

Possible options:
- Recovery and launch
8000–12000 UAH
Recovery of the prototype's functionality and basic access to the knowledge base.
- A working RAG pipeline without model "hallucinations"
- Basic web interface for obtaining responses
- Connection of the chosen LLM API (OpenAI/Claude)
- Manual database update instructions

- Full functionality
15000–25000 UAH
A full-fledged corporate assistant with autonomous knowledge management (Recommended).
- Document upload interface without developer involvement
- Authorization system for sales department managers
- Query history for monitoring response quality
- Optimized architecture for future connection to the analytical repository
- Full calculation of token and hosting costs

- Advanced solution
30000–45000 UAH
An advanced AI tool with enhanced accuracy and support for complex scenarios.
- Improved document processing (OCR for scans, complex tables)
- Extended response generation functionality for various communication channels
- Priority support and cost optimization for tokens over the month
- Integration with cloud storage for automatic file synchronization
- Complete technical documentation package for system architecture Expand

Budget: 27000 UAH Deadline: 21 days

Good day, I can implement such an AI assistant turnkey without any problems - I have over 5 years of experience in development and more than 2 years in LLM/RAG projects.

Stack: Python + FastAPI, LangChain or LlamaIndex for the RAG pipeline, vector database (Qdrant or pgvector), OpenAI or Claude via API. Web interface with authorization - React or Next.js. Everything is deployed on your server via Docker.

The first step is an audit of the existing prototype: I will quickly figure out where the problem is with data transmission to the model, and we will either fix the existing one or reasonably rebuild from scratch - whichever is more effective. I will immediately design the architecture considering future connection to the analytical storage.

Estimated monthly ownership cost: OpenAI GPT-4o tokens - from $30-80/month depending on intensity, hosting - from $10-20/month. Regarding the cost of work - a budget of 4000 UAH will not cover such a volume, the real cost starts from 30,000 UAH depending on the final scope. I am ready to discuss the details in private. Feel free to reach out!

Budget: 4000 UAH Deadline: 7 days

Good day! I have worked on exactly such RAG assistants — a web interface with authorization, a company document database, and responses strictly from sources. Your symptom "information from the database does not reach the model" almost always means that the documents have not been indexed into the vector database or the found fragments are not being inserted into the prompt before the query — rarely the API itself. I will look at the code and access, and in half a day I will give you an exact diagnosis and what is more beneficial: to fix it or to rebuild it. What is the prototype made on — LangChain/LlamaIndex or a custom solution, and what storage is used for embeddings?

Budget: 4000 UAH Deadline: 1 day

Hello!

If we are to work exclusively based on the established knowledge base, then the LLM is needed only for generating responses and understanding questions, while all vectorization (RAG) can be performed by a local model.

I created such an internal manager assistant for ddtuning.
Let me know, and I will show you how it works.

Regarding looking into why it is not working now, we can take a look.

Budget: 7000 UAH Deadline: 7 days

I have built similar RAG systems: FastAPI, vector database (Qdrant / pgvector), embeddings through Claude or OpenAI, web interface with authorization. The first step is to audit the prototype: I will find where the pipeline breaks between the database and the model, and we will decide what is faster — to fix it or to rebuild it.

Stack: FastAPI, pgvector/Qdrant, Claude API (recommended — more accurate on complex queries, transparent token pricing), simple web interface, Docker, HTTPS. Database updates — file uploads without a developer. The architecture is designed with a focus on a future analytical repository.

Approximately 35 hours of work. Ready to start after receiving access.

Write to me — we will discuss the details and get started.

Budget: 4000 UAH Deadline: 5 days

Hello!

I have experience in developing services based on LLM and RAG architecture. I suggest starting with an audit of the current solution: checking the integration with the API, the indexing and search pipeline, after which either restore functionality or reasonably propose a rebuild.

For implementation, I propose the stack: Python (FastAPI), OpenAI or Claude API, PostgreSQL + pgvector (or Qdrant), a modern web interface with authorization and the ability to independently update the knowledge base.

The estimated duration of the first stage (audit + launch of the working version) is 5–10 days. I will be able to provide the exact cost after reviewing the current code and architecture. I will also help estimate the monthly expenses for API and hosting depending on the volume of documents and the number of requests.

Budget: 4000 UAH Deadline: 2 days

Hello. I looked at your project — a classic RAG for an internal knowledge base. First, I will conduct an audit of the existing prototype: the most common issue is that the embeddings are not updated after loading documents or the retrieval chain is incorrectly configured. If the code is live — I will fix it; if there are deeper architectural problems — I will rebuild it on LangChain + ChromaDB/Qdrant, with a web interface on Streamlit or FastAPI + a simple front end. Regarding LLM: Claude Haiku is optimal in terms of price/quality for this case, approximately $15-30/month per sales department. The monthly cost of ownership (tokens + hosting) is within $30-50 depending on intensity. I have created similar solutions for internal documentation. I am ready to start with the audit, the full cycle — up to 2 weeks.