Your Corporate
ChatGPT on Your Data
Combine all procedures, instructions, contracts and years of company experience into a single corporate RAG-intelligence. Employees find the needed item among thousands of PDFs in 2 seconds instead of hours of searching.
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Search for information in PDF, DOCX, Notion, Confluence and databases
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AI quotes the exact source and page of the document in each response
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Zero Data Retention: company data is not used for public training
Why is this critical right now?
Knowledge is Lost in Folders
Important procedures are scattered across Google Drive, local disks and chats. No one remembers where the current version is located.
Long and expensive onboarding
A novice daily user diverts the team leader with dozens of questions, spending paid time with key specialists.
Risk of commercial secret leakage
Employees copy confidential contracts into a regular ChatGPT, violating NDA and company security.
What does it give to business?
Why is the implementation of this technology taking off in the first months.
Immediate access to expertise
Any employee receives an accurate answer of the level of a senior lawyer or tech lead in seconds.
Absolute accuracy without guesses
RAG-system strictly holds accountable for the provided documents and indicates exact references to the section and file.
Automatic analysis of contracts
AI compares contract versions, finds hidden pitfalls and forms legal conclusions.
Access Rights Division (RBAC)
A regular manager sees only general regulations, and financial reports are available only to top management.
Work with local LLM
Possibility to launch the system fully autonomously in a closed corporate environment without going online.
Knowledge preservation upon employee departure
Employee experience remains in the company's knowledge base, and it does not follow the employee
How We Implement It
No magic. Just a clear engineering process
Audit and document collection: we structure files (PDF, DOCX, tables), clean them of duplicates and noise.
Chunking and embeddings: breaking down documents into logical fragments and vectorizing through embedding models.
Configuration of vector database (Pinecone / Qdrant): creating a fast vector index storage.
Building a RAG pipeline: we program hybrid search (Semantic + Keyword) and reranking (Re-ranking).
Interface development: creating a convenient web chat for employees or Telegram/Slack bot.
Security and testing: setting up SSO authentication, role-based access, and verifying accuracy.
Frequently asked questions
What is RAG in simple terms?
Retrieval-Augmented Generation (RAG) — this is a technology that before generating a response first finds accurate facts in your closed documents and presents them to the neural network as a single source of truth.
Is it safe to load our contracts?
Absolutely. We use Enterprise API from OpenAI/Anthropic with Zero Data Retention policy or deploy open models (Llama 3, Mistral) on your physical servers.
Which file formats are supported?
PDF, Word, Excel, PowerPoint, text files, Notion pages, Confluence pages, Google Docs, email archives.
What happens if there is no answer in the database?
The system will honestly inform: "In the company's provided regulations, there is no information on this" — it will not fabricate facts.
How is the knowledge base updated?
Automatically. When uploading a new file to the folder, the system indexes it within a few minutes without reconfiguring the program.
Ready for Implementation?
Describe your task. We will conduct a free audit and propose an AI solution architecture specifically for your business.