AI document search cuts this to 3 seconds - regardless of how many folders, versions, and attachments you have.
Result: faster decisions and zero time wasted on manual searching.
Real results from deployments in Polish companies
Law firm
Team of 12 lawyers searched for contracts and precedents avg 2h daily. After deployment: time reduced to 30 min. AI finds documents with citations in 3 seconds.
Construction company
12,000 project documents and contracts from 15 years. AI indexed everything in 4 days. Team finds projects from 10 years ago in seconds.
Marketing agency
Agency stored client documents chaotically. AI found 3-year-old brief in 2 seconds - team searched for it 2 hours.
Check if AI document search fits your company
You have more than 500 active documents
Team spends 30-60 minutes daily searching for them
You work on Drive/SharePoint/emails
You need "Google-like" search, but exclusively on your data - fully GDPR-compliant
Each feature translates to saved time and better work organization
Google Drive. SharePoint. Emails. Everything searched instantly, without changing file structure thanks to semantic search (vector search + classic full-text).
Exact citation, document page, and full path. Minimized hallucinations thanks to RAG architecture (first retrieval, then response grounded in your files).
Ask normally: "Smith contract 2023", "Q3 budget". Document AI understands intent and context using the latest LLM models (OpenAI, Anthropic, Google, Mistral) selected for your data.
Learns your industry terminology, abbreviations, and product codes and can filter results by metadata (contractor, year, document type, project).
Everyone sees only what they have permissions for. Audit logs included.
No installation. No configuration. Works everywhere. Can also work as a "knowledge layer" for other AI agents (e.g., voice or chat agent) via API or Model Context Protocol (MCP).
How it works under the hood - clear and concrete
We select the stack for your case. You can specify preferred cloud (AWS/GCP/Azure) and AI model. Backend can run in EU regions, data is processed in compliance with GDPR. We use modern RAG pipelines (LlamaIndex, LangChain) and vector databases that are the standard in enterprise search in 2025.
AWS Frankfurt, GCP Warsaw, Azure Dublin. Zero transfers outside EU. On-prem is also possible if Document AI should operate exclusively in your infrastructure.
OpenAI, Cohere, Mistral, or Llama 3 embeddings. Answers exclusively based on your documents. We use hybrid search (BM25 + vector search), intelligent document chunking, result reranking, and metadata filtering to minimize hallucinations.
PostgreSQL + pgvector, Weaviate, or Qdrant. Millions of files, microsecond responses. Depending on needs, we can also use Elasticsearch / OpenSearch with vector module if your team already knows them.
Google Drive, SharePoint, OneDrive, email inboxes - indexing without changing structure. We can also connect network folders, S3, your system APIs, and make Document AI available to other AI agents as a tool via Model Context Protocol (MCP).
SSO (Google/Azure), granular roles, full audit logs. Every query can be traced. In enterprise projects, we support integration with Azure AD / Entra ID, LDAP, and existing file permission policies.
TLS 1.3 encryption. AES-256. DPA. No model training on client data. If needed, we also conduct full architecture review for IT audits, ISO 27001, SOC 2, and internal security policies.
From security analysis to working search
Not slides. Real AI search on your documents.
After a week you see Document AI searching your documents in browser and on phone
Working demo ready for testing
Document AI starts finding files in 3 seconds.
Document AI finds documents in 3 seconds and answers most questions correctly
Indexed documents + working test search
Learning your industry terminology. Accuracy >95%.
Document AI answers correctly to 95%+ questions and understands your industry terminology,
and the RAG pipeline (LlamaIndex / LangChain) is tuned for your real use cases.
Tested Document AI with improved responses
Team training, admin panel, full production.
Document AI running independently and you have full control
Document AI working independently + admin panel + documentation
Hybrid model: one-time setup (deployment, indexing, training) + monthly fee (hosting, monitoring, AI, support).
You'll receive a precise quote within 24h after a discovery call (20-30 min).
Document AI for one source. Ideal for small teams.
Document AI with multiple sources for the entire company.
For organizations requiring full control and large scale.
* Installment payments available
The monthly fee is all-inclusive: hosting, indexing, AI tokens, updates, and technical support.
Predictable cost with no surprises. Included queries cover most typical use.
Copilot: €30/user × 20 users = €600/mo
Our GROWTH RAG: €249/mo for 20 users
Plus: we search Drive, emails, scans - not just Microsoft
Flat pricing - no per-user trap
** Query limits are estimates based on average document size and complexity. Actual limits may vary depending on your knowledge base volume.
Honest answers to tough questions - no marketing
Yes. Data is processed in compliance with GDPR - we don't use your data to train models. Backend can run in EU, but AI models operate through provider APIs. Full encryption. DPA.
No. Read-only access only.
No. Document AI handles structures "as is".
Yes - IMAP and Exchange.
Yes - in Enterprise version.
3-6 weeks.
RAG (Retrieval-Augmented Generation) is an architecture where Document AI first searches for the right documents in a vector database (semantic search), and only then generates an answer based on them. Thanks to this: • the model doesn't "make up" content, • you can always see the citation and source, • you can have an AI document search working like Google, but exclusively on your data, GDPR-compliant.
Complete guide to implementing Document AI and RAG for enterprise knowledge search. Architecture, costs, security, and real implementation results.
Read more →Document AI for law firms and legal departments. Automate contract review, due diligence, legal research. Process 1000+ pages in hours. Reduce review time by 80%.
Read more →What is RAG (Retrieval Augmented Generation)? How does it work for business document search? Benefits, use cases, comparison with traditional search. Complete guide.
Read more →When to use RAG and when to fine-tune? This technical guide compares costs, accuracy, latency, and maintenance requirements. Make the right architecture decision for your AI project.
Read more →Complete guide to implementing Retrieval-Augmented Generation (RAG) for business applications. Learn how to make AI assistants that answer questions using your actual company data, not just general knowledge.
Read more →Ready demo in 24 hours - on your company's real files.
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