Employees find knowledge in seconds
No more folder digging. Question → concrete answer → source link. In 10 seconds instead of 30 minutes.
AI Search — employees find company knowledge in seconds
We deploy an internal AI search engine on Azure AI Search + GPT-4. Employees ask questions in natural language, AI answers based on procedures, documentation and the knowledge base. Permissions fully respected. Go-live in 4–8 weeks.
01
1–2 hours per employee per week lost to searching
02
Senior employees constantly interrupted by basic questions
03
New hires struggle to find procedures, frustration early on
Customer problem
Employees hunt knowledge across folders — hours per week
In a typical 100-person company each employee loses 1–2 hours a week searching for procedures, instructions, customer documents or answers to recurring questions. If they don't find it, they ask others. That multiplies the hours lost, and senior employees get interrupted constantly.
An internal AI search engine solves this completely. The employee asks a question in natural language (e.g. "what's the advance refund process", "what VAT applies to training in Germany"), AI searches company documents and gives a concrete answer with source links. No folder digging, no asking the same questions for the hundredth time.
Why it matters
1–2 hours per employee per week lost to searching
Senior employees constantly interrupted by basic questions
New hires struggle to find procedures, frustration early on
Knowledge locked in single individuals — risk on turnover
Service desk repeats the same questions to outside experts
What we deliver
What we deliver in the implementation
An AI Search rollout combines knowledge preparation with AI configuration. We deliver a ready-to-use assistant integrated with M365.
01
Knowledge audit and preparation
Identifying the key sources (SharePoint, OneDrive, Confluence, IT knowledge base). Assessing document quality. Recommendations for clean-up.
02
Azure AI Search configuration
Indexing documents from SharePoint and other sources. Setting up semantic search with embeddings. Permission-aware filtering.
03
Q&A configuration with GPT-4
Connecting semantic search with GPT-4 (Azure OpenAI) for answer generation. RAG (Retrieval Augmented Generation) with source citations.
04
User interface
Assistant available in Teams, on the SharePoint intranet or as a standalone app. Chat-like UI with history, answer rating, suggestions.
05
Permission enforcement
AI never shows documents the employee can't access. Full integration with SharePoint and Microsoft Entra ID permissions.
06
Knowledge domain configuration
Separate knowledge collections for HR, IT, Sales, Operations. Employees pick a context or the system detects it from roles.
07
Quality monitoring and improvement
Dashboard showing top questions, answer quality (employee ratings), knowledge gaps. Iterative model improvement.
08
Training and 30 days of support
Sessions for users (how to ask effectively), admins (how to monitor) and knowledge leads (how to maintain quality).
Technology stack
Technologies we use
Azure AI + Microsoft 365 at the core, optionally enriched with Claude / OpenAI for more demanding scenarios.
Azure AI SearchAzure OpenAI Service (GPT-4)Claude (Anthropic) — optionalSharePoint OnlineMicrosoft Teams (assistant UI)Microsoft Entra ID (permissions)Microsoft Copilot Studio (optional)
Your solution
Typical AI Search scenarios
Search across HR and people procedures
Employees ask about leave, travel, benefits, company policies. AI answers based on the current HR procedures.
Assistant for IT service desk
IT support staff ask AI about known issues, runbooks, instructions. Ticket handling time drops drastically.
Technical knowledge for engineers
Technical documentation, specs, product instructions. AI pulls the exact bit of information instead of returning a 50-page PDF.
Sales assistant
Salespeople ask about products, price lists, discount policies, customer history. AI answers in seconds.
Solution fit
Sprawdźmy, które elementy rozwiązania najszybciej ograniczą pracę manualną i uporządkują procesy w Twojej organizacji.
Impact and metrics
Effects of AI Search in the organization
Clients we have implemented AI Search for report similar effects in the first 2–3 months.
-75%
shorter knowledge lookup time
5h+
per employee per week reclaimed
3×
faster onboarding for new hires
1
shared source of company knowledge
Business benefits
Knowledge out of single heads
Senior employees stop being interrupted by basics. Their knowledge, once written down, is available 24/7.
Faster onboarding
New hires ask AI instead of pestering colleagues. They reach productivity in days, not months.
Who this is for
Who this is for
Companies with a rich knowledge base
Organizations with 200+ procedures, technical documents and policies — where employees really lose time searching.
Companies with a large service desk or HR
Organizations where support teams answer the same questions repeatedly — AI Search massively offloads them.
Growing companies with turnover
Organizations where new-hire knowledge is the bottleneck. AI Search shortens onboarding.
Organizations on Microsoft 365
AI Search works best on a well-organized SharePoint. M365 is the natural foundation.
Implementation process
AI Search implementation process
We implement the solution in a structured model that clarifies project stages, integration with the current environment and further development across the organization.
Stage01
Knowledge audit (1–2 weeks)
Identifying key knowledge sources. Assessing document quality. Clean-up recommendations (if SharePoint is messy, we have to fix that first).
Stage02
Azure AI Search setup (1 week)
Indexing sources, configuring semantic search, integrating SharePoint / Entra ID permissions.
Stage03
Q&A setup with GPT-4 (1–2 weeks)
Azure OpenAI setup, prompt engineering, RAG (Retrieval Augmented Generation), Teams interface.
Stage04
Pilot with a test group (1–2 weeks)
20–50 employees test AI Search. We gather feedback, correct answers, fill knowledge gaps.
Stage05
Go-live and 30 days of support
Full rollout to the organization. Training. 30 days of active answer-quality monitoring.
Stage 1 of 5
Knowledge base AI-readiness audit
Model recommendation (GPT-4 vs Claude vs Copilot)
Rollout plan with concrete ROI estimate
FAQ
FAQ about AI Search
How long does AI Search take to implement?
Typically 4–8 weeks: knowledge audit (1–2 weeks), Azure AI Search + GPT-4 setup (2–3 weeks), pilot (1–2 weeks), go-live + support. If SharePoint is messy we have to organize it first (+ 4–8 weeks).
Will AI show documents the employee shouldn't see?
No. AI Search fully respects SharePoint and Microsoft Entra ID permissions. The employee only sees documents they have access to — AI only searches what's available to them.
Will our documents end up training OpenAI models?
No. Azure OpenAI has a Microsoft contract guaranteeing that customer data is NOT used to train OpenAI models. That's a key difference from the free ChatGPT.
Do you support languages other than Polish?
Yes. GPT-4 supports 95+ languages. Employees can ask in Polish, English, Ukrainian, German — AI answers in the language of the question, regardless of the document language.
Can I use Claude instead of GPT-4?
Yes. For demanding scenarios with long documents (above 100 pages) Claude can perform better (larger context window). We can also combine models — Claude for long docs, GPT-4 for fast questions.
What's the monthly cost?
Azure AI Search and Azure OpenAI are billed per query/token. Typical cost for a 100-person company with moderate usage: a few hundred EUR per month. Plus maintenance — a few hours of consultant time per month.
What happens when AI doesn't know the answer?
AI says it directly: "I didn't find an answer in the company documents." It doesn't hallucinate. That's a natural moment to fill the knowledge gap — these gaps are reported to the admin.
Related materials
Related solutions
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In-depth analysis
AI knowledge search — what to know
An internal search engine powered by AI (Azure AI Search + GPT-4) is one of the fastest-returning AI investments in an organization. Employees really lose 1–2 hours a week searching for procedures, instructions and answers to recurring questions. AI Search cuts that 75%, with a typical 4–6 month payback.
A good AI Search rollout is not just Azure OpenAI configuration. The foundation is a clean knowledge base — usually SharePoint — with proper metadata, taxonomy and permissions. AI won't replace messy documents with well-organized knowledge; it can only retrieve, faster, what's already organized.
AI Search delivers the biggest impact in organizations with a rich knowledge base (200+ procedures), in companies with a large service desk or HR, and in organizations preparing a Microsoft Copilot rollout. AI Search is often the first, smaller step into AI at work — with lower risk and faster ROI than a full Copilot deployment.