Skip to content
ALL IT Agency
AI Lab
Contacts

Corporate RAG systems and intelligent knowledge bases without hallucinations

Convert gigabytes of regulations, contracts, instructions, and correspondence into a smart corporate oracle that responds to 2 seconds with precise links to sources. Developing industrial RAG systems (Retrieval-Augmented Generation) based on vector databases (Qdrant, pgvector, Milvus): hybrid search (BM25 + Dense Embeddings), semantic reranking and 100% confidentiality protection.

✓ Zero hallucinations: artificial intelligence forms a response exclusively based on the company's uploaded documents
✓ Exact quotes and references: each response is accompanied by the file name, page number and contract clause
✓ Instant onboarding of new employees: a new employee receives comprehensive answers about company processes within 5 seconds
✓ Flexible access rights (RBAC): an employee sees only those documents' responses to which they have official access
Hybrid Vector Search (Qdrant / Milvus) Semantic Chunking & Reranking Hallucination-Free Guardrails Source Citation & Page Anchors Role-Based Access Control (RBAC)
Get RAG demonstration
Formats and pricing
ALL IT • SERVICE SPEC
SLA 99.9%
Innovative technologies and artificial intelligence WARRANTY UNDER AGREEMENT
Corporate semantic knowledge base RAG

Indexed documents: 18,400 • Pages: 240,000 • Semantic search time: 0.35s • Citation accuracy: 99.6%

⏱ Start: 1–3 days 🛡️ NDA: 100% SLA Warranty
📚
240K Pages
Immediate Index
🎯
99.6% Accuracy
Exact Quotes
⚡
0.35s Search
Hybrid Vector
🔒
RBAC Rights
Isolation of Access
✓ Official contract with fixed cost of work
✓ 100% transfer of rights to code and configuration to the customer
✓ Assigned leading engineer and support 90 days
FULL TRANSFER OF RIGHTS CONTRACT + WARRANTY
[ SPEED OF KNOWLEDGE ]

Knowledge search without unnecessary switching

Search business documents with source references and access controls.

[ 01 ]

Answers with source references

Lawyer, manager or accountant asks the system in natural language and receives an exact paragraph with a link to the original source.

[ 02 ]

Knowledge stays in the company

When the key specialist is dismissed, all the knowledge, cases and instructions accumulated by him remain in the company's active database.

[ 03 ]

Guidance for the support team

Call center operators provide answers to complex technical client questions directly during a phone conversation.

[ 04 ]

Updates from connected repositories

We configure indexing of agreed sources: Google Drive, Notion, Confluence, SharePoint or file servers.

[ DEPLOYMENT STAGES ]

Architecture and components of a reliable RAG-system

Full technological pipeline: from intelligent document splitting to multi-level hallucination filtering

[ STEP 01 ] Chunking

Parsing and semantic splitting (Chunking)

Cleaning and intelligent splitting of complex PDF, DOCX, tables and scans while preserving structure and headings.

[ STEP 02 ] Embeddings

Generation of vector embeddings (Embeddings)

Transformation of text into multidimensional vectors using the most modern models (OpenAI, Cohere, BGE-Large).

[ STEP 03 ] Vector DB

Deployment of a scalable vector database

Installation and optimization of Qdrant, Milvus or pgvector on dedicated servers for ultra-fast search in milliseconds.

[ STEP 04 ] Hybrid Search

Hybrid search and semantic reranking (Rerank)

Combination of full-text search by keywords (BM25) and vector search followed by sorting with Cohere Rerank model.

[ STEP 05 ] Guardrails

Filtering of hallucinations and source verification

Fact-Checking Algorithms: if the answer is not found in the retrieved fragments, the model strictly reports the lack of information.

[ STEP 06 ] RBAC Security

Access control by roles (RBAC)

Integration with corporate SSO: a regular employee will not be able to find confidential orders or top management salaries.

[ Tariff Plans ]

Packages for implementing corporate RAG systems

Select the project scale: from searching within internal regulations to a full-scale corporate search core

Fast start for regulation base (up to 1,000 documents)

Internal Knowledge Bot

Smart chatbot for quick search through instructions, contracts and regulations for employees in Telegram or Slack.

Development time: from 2–3 weeks
Work Scope:
✓ Indexing up to 1,000 PDF / DOCX / Web documents
✓ Configuration of vector database Qdrant in the cloud
✓ Connection of hybrid search with precise links to pages
✓ Chat interface for employees in Telegram and web widget
✓ Protection against hallucinations (Strict Grounding)
✓ 30 Days of Technical Support
Choose the 'Knowledge Bot' Tariff
Most popular for medium and large businesses ★ Popular

Enterprise Semantic RAG Core

Powerful search core for tens of thousands of documents with automatic synchronization with Google Drive/Confluence and access roles.

Development time: from 4–6 weeks
Work Scope:
✓ Everything that is included in the package «Internal Knowledge Bot»
✓ Indexing up to 50,000 documents of any formats
✓ Automatic connectors to Google Workspace, Notion and Confluence
✓ Semantic reranking of results (Cohere Rerank API)
✓ Role-based access control (RBAC) with integration to corporate SSO
✓ 60 days of technical support and search query analysis
Choose 'Enterprise RAG'
For banks, military-industrial sector, and state structures

Air-Gapped Private RAG

100% autonomous deployment in an isolated container of the client on local open-source models without internet access.

Development time: from 8–12 weeks
Work Scope:
✓ Everything that is included in the package 'Enterprise Semantic RAG Core'
✓ Fully On-Premise deployment on GPU servers of the client
✓ Local embedding and generation models (Llama 3 / Mistral / Qwen)
✓ Full isolation from external internet (Air-Gapped Security)
✓ Support for complex engineering drawings, schematics, and tables
✓ Dedicated R&D AI Lab team and SLA 99.9%
Select 'Private RAG'
← Swipe options →
[ COST CALCULATOR ]

Calculate the cost of implementing RAG systems

Specify the volume of documents and security requirements

1. Estimated volume of company documentation: Select Variant

2. Where are your files stored: Select Variant

3. Requirements for Placement and Models: Select Variant

CHOOSEN PARAMETERS OF THE SOLUTION:
⏱ Estimated Timeline: Calculation... 👥 Team: 2–3 engineers
1. Estimated volume of company documentation: Up to 1,000 files (Instructions, orders, regulations)
2. Where are your files stored: Local folders, PDF, Word files and Excel tables
3. Requirements for Placement and Models: Cloud solution with Enterprise API (OpenAI / Claude)
✓ Parameters will be fixed in the application form
Get an exact quote
[ STAGES OF COOPERATION ]

How the project is delivered

Transparent process from first contact to final release on your domain.

01
1 Week

Audit of document structure and formats

We analyze file types (PDF, scans, tables), text quality, and the presence of complex formatting.

02
1–2 weeks

Configuration of the splitting and indexing pipeline

We are testing different chunking strategies (by paragraphs, headings) to maintain the semantic integrity of the text.

03
1–2 weeks

Deployment of vector database and hybrid search

Configure Qdrant, set up embedding models and connect semantic reranker.

04
1 Week

Calibration of accuracy and testing on questions

We compile a test set with 100 real employee questions and refine the accuracy of answer finding to >98%.

05
3–5 days

Launch of a corporate search cabinet

We provide the team with a convenient web interface and bots, set access rights, and train the staff.

Our Clients

Companies that have entrusted ALL IT Agency with the development and scaling of their digital solutions

Partner Case 11
Partner Case 5
Partner Case 6
Meregi Group
Partner Enterprise
TOV Zagin Alpha
Valikhnovski Surgery Institute
DIM Service
Wizeclub
Partner Case 11
Partner Case 5
Partner Case 6
Meregi Group
Partner Enterprise
TOV Zagin Alpha
Valikhnovski Surgery Institute
DIM Service
Wizeclub
[ FREQUENT QUESTIONS ]

Answers to common questions from customers

Key details regarding terms, independent website editing, and rights transfer.

A regular ChatGPT has a limited context window and often forgets details or makes up facts (hallucinates). RAG (Retrieval-Augmented Generation) first finds through a vector database exactly the 2–3 paragraphs from your documents where the exact answer is located, and then asks the neural network to formulate a concise answer strictly based on these paragraphs, indicating the original source.

We implement a multi-level OCR recognition conveyor with support for multimodal models. Scanned documents are recognized, tables are converted into structured Markdown format, and diagrams are described by visual models, which allows finding information even in old scanned orders.

No, this is excluded. We implement an access control system based on metadata levels of vector (RBAC). At each search request, the system automatically filters documents according to the user's access level. An employee physically cannot receive a response from a document to which they do not have access.

We configure automatic synchronization: as soon as a file is changed or added to Google Drive, Notion or internal folder, an automatic worker deletes old vectors and generates new ones in a few seconds. Employees always receive only the most up-to-date data.

Yes, our "Air-Gapped Private RAG" package is created precisely for this. We deploy open generation models and embeddings (for example, Llama 3 and BGE) on your own GPU servers in a fully isolated corporate network with no connections to the outside world.

Share this service with partners or colleagues

Fast delivery to the working chat or copying the link for budget approval

Telegram LinkedIn Facebook

Do you want to combine your company's knowledge into a single intelligent oracle?

Describe the document formats and the number of employees. Our specialists from ALL AI Lab will prepare an interactive demo and calculate the architecture within 24 hours.

Confidentiality

Official NDA contract for system audit

Quick Feedback

We will contact you within 15 minutes during business hours.

Order a demo of corporate RAG system

The tech lead will contact you within 15 minutes during working hours