When knowledge is scattered across SharePoint, emails, and ticket systems, every answer takes time—Enterprise Search changes that. This guide shows you step by step how Enterprise Search solutions will work in 2026 and what really matters.
Table of Contents
Why enterprise search is a must today
An employee is looking for information about a project from two years ago: SharePoint, Teams, emails, ticket system – nothing. After 30–60 minutes, the question is answered. Multiply that by hundreds of employees and you see the problem: The knowledge is there, but it can’t be found.
Enterprise search solutions do just that: they provide a central, secure search (and today: answers instead of hit lists) across all systems, without creating a new knowledge silo.
In addition, there are three trends that are increasing the pressure:
- Knowledge loss due to retirement and staff turnover
- Data growth and an increasing number of tools
- Shadow AI: Employees use external AI tools because they are faster – often without governance
This guide shows you how modern enterprise search will work in 2026 (including RAG), which criteria really matter, and how to choose a solution that IT and business departments will accept.
Key findings
- Enterprise search is an efficiency lever: Less searching, faster decisions, less duplication of work.
- AI is changing expectations: Employees want answers in natural language – but with sources and permissions.
- Security and trust are the differentiators: Permission concepts, audit logs, data storage, and traceable answers are crucial.
- RAG is the standard approach: Answers are based on found company sources – not on a model’s “free guesses.”
- ROI is calculable: Reduced search time, faster onboarding, fewer queries to experts (values vary depending on use case).
What is an enterprise search solution?
Definition and core concept
An enterprise search solution makes corporate knowledge searchable across all systems, regardless of whether it is stored in Microsoft 365, Confluence, network drives, DMS, CRM, or ticket systems.
The modern requirement is not “more hits,” but rather:
answers in seconds, including sources, context, and permissions.
To achieve this, data sources are connected via connectors, content is indexed, and (importantly) individual permissions are assigned. This means that everyone automatically sees only what they are allowed to see.
Difference from traditional search
| Aspect | Traditional Search | Enterprise Search |
| How it works | Keyword matching (word for word) | Semantic search (understanding meaning) |
| Result | List of documents | Precise answers with context |
| Data sources | Limited (usually one system) | All company sources |
| Security | No consideration of access rights | Automatic respect for permissions |
| Learning ability | Static | Learns from user behavior |
| Multilingualism | Limited | Fully multilingual |
| Ease of use | Complex, requires search operators | Natural language, like ChatGPT |
Practical example:
Your employee is looking for the “customer contract with ABC Corporation.” A traditional search engine would display all documents containing these words, possibly hundreds of them. A modern enterprise search solution understands that your employee is looking for a specific contract and displays the most relevant results at the top, possibly with an AI-generated summary of the contract terms.
The five biggest problems that enterprise search solves
Problem 1: Loss of productivity due to searching instead of working
In many teams, several hours per week are spent on searching, queries, and context switching. This not only costs time, but also focus, and causes frustration.
Why does this happen? Because information is spread across many tools and the search function works differently for each system.
What enterprise search changes: An interface that searches all systems simultaneously and prioritizes results. AI also provides answers with sources, enabling employees to make decisions more quickly.
Measurable impact (typically): Depending on the data situation and use case, search times can be significantly reduced.
Problem 2: Data silos and fragmented company knowledge
Many companies have an “archipelago architecture”: different departments use different systems. Marketing uses HubSpot, sales uses Salesforce, HR uses a local system, IT uses Jira. Each system is an island of valuable information, but there are no bridges between them.
The problem: Your employees often don’t know that information exists because it is stored in a system they don’t use regularly. This leads to duplication of work, poorer decisions, and missed synergies.
The solution: An enterprise search solution connects these islands. It aggregates data from all systems and makes it accessible via a single search interface. Suddenly, a sales rep can see which projects the customer has already carried out with your company without opening Jira.
Measurable impact: After deploying an enterprise search solution, Ruhrkohle AG was able to break down data silos and reduce search time by 40%.
Problem 3: Security risks due to uncontrolled shadow IT
Your employees are pragmatic. If the official solution is too cumbersome, they will use alternatives. In many companies, AI is already being used “in the shadows”: employees use chatbots or AI tools via private accounts because it is faster, often without clear guidelines, approvals, or auditability.
The problem: These tools are not designed for corporate environments. Your employees may enter confidential information that then ends up in the training data for these models. This is a significant compliance and security risk.
The solution: A secure, proprietary enterprise search solution offers the convenience of ChatGPT, but with complete control and security. Your employees can ask their questions, get accurate answers, and the data stays within your company.
Measurable impact: You can eliminate shadow IT risks while increasing employee satisfaction.
Problem 4: Knowledge loss due to demographic change and inefficient onboarding
Over 6 million skilled workers will retire in Germany by 2030. They will take their knowledge with them, unless that knowledge is documented and easily accessible. At the same time, new employees need weeks or months to familiarize themselves with your company’s processes because they cannot find the distributed knowledge efficiently.
The problem: Knowledge is stored in the minds of employees, not in systems. When these employees leave, the knowledge is gone. New employees have to start from scratch or rely on experienced colleagues to explain everything to them.
The solution: An enterprise search solution makes knowledge explicit and accessible. New employees can learn independently by asking questions. Experienced employees can document their expertise independently and do not have to constantly answer questions.
Measurable impact: Müller Maschinentechnik uses amberSearch specifically to help new employees quickly familiarize themselves with processes and projects.
Problem 5: Compliance and data protection risks
GDPR, EU AI Act, SOC2 – regulatory requirements are growing. Many enterprise search solutions train their AI models with customer data, which leads to compliance issues. Others store data in the US, which is problematic under GDPR.
The problem: You need to ensure that your enterprise search solution is compliant. This is not only an IT issue, but also a legal and business issue.
The solution: A modern, European enterprise search solution should be ISO 27001 certified, GDPR compliant, EU AI Act compliant, and host data in the EU. It should not train AI models with customer data.
Measurable impact: You can handle security and compliance requirements without sacrificing modern AI functionality.
How modern enterprise search works:
A look under the hood
The architecture: From data sources to answers
A modern solution follows a clear architecture consisting of several layers:
Layer 1: Data sources and integration interfaces
It all starts with your data sources. These can be cloud applications (Microsoft 365, Google Workspace, Slack), on-premise systems (SharePoint, network drives, local databases), or specialized enterprise applications (Salesforce, SAP, etc.). For each data source, there is a connector that establishes the connection and retrieves the data.
Layer 2: Indexing and enrichment
The data is added to a central index. It is not only stored, but also enriched: the index understands the meaning of the data, recognizes entities (people, places, products), and creates connections between documents. This is the core of an enterprise search solution’s intelligence.
Layer 3: Security and access control
At the same time, your access rights are taken into account. When an employee in accounting makes a file available, the system automatically records who is allowed to access it. This happens transparently, so the employee does not have to configure anything.
Layer 4:Search and retrieval**
When an employee asks a question, it is queried against the index. The search is not simply a keyword match, but a semantic search that understands the meaning. The most relevant documents are returned.
Layer 5: Generation and response
This is the innovative part: the results found are not simply displayed as a list, but are forwarded to an AI model that generates a natural language response. This is the RAG principle (see next section).
The revolution: Retrieval-Augmented Generation (RAG)
RAG is the key concept that distinguishes modern enterprise search solutions from older solutions. Here’s how it works:
Traditional AI search:
An AI model such as ChatGPT is trained with millions of documents. It “memorizes” this information. When you ask a question, the model generates an answer based on what it has learned. The problem: On the one hand, the model can hallucinate (generate invented information), and on the other hand, it cannot handle new, company-specific information.
RAG approach:
Instead of training the model with your company data, the model is only fed the question and the most relevant documents from the index. The model then generates an answer based on these documents. This has several advantages:
- Security: Your company data is not used for training. It remains within your company.
- Up-to-date: Responses are based on the latest documents in the index, not on training data from months ago.
- Reliability: The model can cite sources and hallucinations are minimized.
- Compliance: No training data with customer data = GDPR-compliant.
Practical example:
An employee asks, “What are the terms of our contract with ABC Corporation?” The enterprise search solution finds the most relevant contract in the index, extracts the terms, and generates a concise summary, all with source references. The employee can immediately click on the original source if they need more details.
The safety net: Respecting access rights
This is a feature that many solutions overlook, but it is crucial for your business. A company-wide search must automatically respect who has access to what information.
How it works:
When a document is indexed, your access rights (from SharePoint, Active Directory, etc.) are automatically recorded. When an employee performs a search, only the documents to which they have access are returned. This happens automatically and transparently, even without the employee being aware that it is happening.
Why it matters:
Without this feature, an employee could accidentally access confidential information that they should not have access to. This is not only a security risk, but also a compliance issue.
Multilingual support
Modern enterprise search solutions understand that many companies are international. They should be multilingual, not only in the user interface, but also in search and indexing.
Practical example:
Zentis, an international company, had information in different languages distributed across various national subsidiaries. A modern enterprise search solution centralizes this information and makes it searchable across languages. A German employee can search in German and also receive results from French or Italian documents.
Core functions of modern enterprise search software
1. AI-supported search and natural language queries
The user interface should be intuitive, as with ChatGPT. Your employees can ask questions in natural language without having to learn special search operators. The solution understands the intent and delivers precise results.
2. Federated search across multiple systems
The solution should search cloud and on-premise systems simultaneously without moving data. This is technically complex but essential for companies with hybrid infrastructures.
3. Integration into your existing IT infrastructure
The solution should integrate seamlessly into your existing systems. Integration should not be an isolated project, but rather part of your existing ecosystem. Specifically, this includes:
- Cloud integration: Simple connectors for Microsoft 365, Google Workspace, Slack, etc.
- On-premise integration: Secure connections to your local systems via VPN or APIs.
- Single sign-on (SSO): Your employees do not have to log in again.
- Automatic synchronization: When data changes, it is automatically updated.
4. Security, compliance, and governance
These points should not be optional, but a core feature:
- ISO 27001 certification: Proof of security standards.
- GDPR compliance: Data protection according to EU standards.
- EU AI Act compliance: Compliance with the new EU regulation for AI.
- Hosting in the EU: Your data stays in the EU, not in the US.
- No training with customer data: AI models are not trained with your data.
- Audit logs: Complete tracking of who accessed what information.
5. User-friendliness and high adoption
A solution is only valuable if it is used. This requires:
- Intuitive user interface: No steep learning curve.
- Integration into your workflows: Available in Teams, Slack, or as a browser plugin.
- Fast results: Answers in seconds, not minutes.
- Good documentation and support: Your employees should get help quickly.
6. Analysis and optimization
Your solution should provide you with insights:
- Search trends: What do your employees search for most often?
- Adoption metrics: How many employees are using the solution?
- Feedback loops: How can the results be improved?
Choosing the right search solution: What to look for
Cloud vs. on-premise: Which model is right for you?
| Aspect | Cloud | On-premise |
| Implementation time | Fast (weeks) | Longer (months) |
| IT effort | Low | High |
| Costs (initial) | Low | High |
| Costs (ongoing) | Predictable | Variable |
| Control | Limited | Complete |
| Scalability</ strong> | Simple | Complex |
| Security | Depends on the provider | Fully controlled |
| Compliance | Depends on the provider | Fully controlled |
Recommendation for SMEs:
Most SMEs benefit from cloud solutions hosted by experienced providers. This reduces IT effort and costs. However, the provider should be European and host data in the EU to meet your compliance requirements.
Must-have features at a glance
When evaluating a business search, you should look for the following features:
| Feature | Why it’s important | How to check |
| AI responses | You want answers, not lists of documents | Run a demo, check the quality of the results |
| Multilingualism</ strong> | Many companies are international | Test with documents in different languages |
| Connectors | The solution must connect to your systems | Check the list of supported systems |
| Rights management | Critical for your security | Questions about how access rights are respected |
| Compliance | GDPR, EU AI Act, ISO 27001 | Check certificates and compliance documentation |
| Hosting in the EU | Your data protection | Ask where your data is hosted |
| Support | Important for your success</ td> | Talk to the support team, check the SLA |
| Pricing model | Transparency | Understand the cost model, check for hidden costs |
Our checklist with evaluation criteria will help you choose the right enterprise search solution: Enterprise Search Selection Checklist
Implementation: 3 phases for successful rollout
A successful enterprise search implementation follows a proven process. Here we bundle best practices from project experience, customer feedback, and typical enterprise search rollouts:
Phase 1: Kick-off & technical setup (1–2 weeks)
What happens: The solution is connected to your systems. This is relatively simple from a technical standpoint if the solution is well designed.
Specific steps:
- Your cloud applications are connected via a simple app registration (approx. 1–2 hours).
- Your on-premise systems are connected via a secure VPN (approx. 1–2 hours).
- Your identity provider is configured so that access rights are automatically synchronized (approx. 1 hour).
- Your domain is set up and SSL certificates are created (approx. 1 hour). Resources: Minimal, mainly IT administrator.
Result: The solution is technically ready but not yet populated with data.
Phase 2: Indexing & key user workshop (5–21 days)
What happens: The solution indexes your data. At the same time, you conduct a workshop with key users to understand the solution and provide feedback.
Specific steps:
- Indexing: The solution searches all your connected systems and indexes the data. This can take 5–21 days, depending on the amount of data.
- Key user workshop: You select 3–4 power users from different departments. You conduct a 1-hour workshop with them to understand how they work, what problems they have, and what added value they expect from the solution.
- Early access: Key users receive early access to the solution to test it and provide feedback. Resources: Moderate, mainly key users and project managers.
Result: The solution is populated with your data and tested by power users.
Phase 3: Rollout & user onboarding (2–4 weeks)
What happens: The solution is made available to all your employees and structured onboarding is carried out.
Specific steps:
- Availability: The solution is made available in Teams, Slack, or as a browser plugin.
- Training: Your employees receive a short training session (approx. 15–30 minutes) on how to use the solution.
- Support: A help desk is available to answer questions.
- Feedback: Feedback is collected and the solution is continuously optimized. Resources: Moderate, mainly for training and support.
Result: The solution is available to all your employees and is actively used.
Important note:
A good enterprise search solution should make this process easy and resource-efficient. If the implementation takes months and requires hundreds of hours of IT effort, the solution is not well suited for small and medium-sized businesses.
For more details on this process, see our detailed guide: Technical Guide to Enterprise Search Implementation.
The ROI of Enterprise Search: When does the investment pay off?
Enterprise search solutions are not cheap, but the ROI is often surprisingly high. Here is one method you can use to calculate it:
Direct ROI: Time and cost savings – a sample calculation
Scenario: A company with 500 employees, average salary EUR 50,000 per year.
Assumptions:
- Your employees spend an average of 30% of their time searching for information.
- Enterprise search reduces this by 30% (a conservative estimate).
- This corresponds to approximately 6 hours per week per employee. Calculation:
- 500 employees × 6 hours/week × $25/hour (average hourly rate) = $75,000 per week
- $75,000 × 50 weeks/year = $3.75 million per year Costs of an enterprise search solution:
- Deployment costs: $50,000–100,000 (one-time)
- Ongoing costs: EUR 200,000–500,000 per year (depending on your company size and usage) ROI: EUR 3.75 million in savings – EUR 300,000 in costs = EUR 3.45 million in net benefits per year. Payback period: approx. 1 month. (Assumptions vary – please validate with your own values.)
This is a conservative estimate. Many companies report even higher savings.
Indirect ROI: Better decisions, faster processes
In addition to direct time savings, there are other benefits:
- Better decisions: Your employees have faster access to relevant information, which leads to better decisions.
- Faster processes: Approval processes, customer service, project management – everything becomes faster.
- Higher employee satisfaction: Your employees are less frustrated when they can find information quickly.
- Faster onboarding: New employees become productive faster.
- Fewer errors: Your employees find the right information faster, which leads to fewer errors.
These benefits are harder to quantify, but often greater than the direct time savings.
Case studies: Real-world figures
Ruhrkohle AG:
After implementing an enterprise search solution, search time was reduced by 40%. This led to significant productivity gains, especially in the processing of warranty claims and queries about previous projects.
Zentis:
An international company with information in different languages about various national subsidiaries. After centralization and cross-language integration, the company was able to use expertise more efficiently, develop innovative products faster, and increase competitiveness.
Müller Maschinentechnik:
A medium-sized mechanical engineering company with many experienced employees. After implementing an enterprise search solution, new employees can familiarize themselves with processes and projects more quickly without constantly having to distract experienced colleagues.
Enterprise search solutions at a glance: What you should look out for
The market for enterprise search solutions is diverse. Here is an overview of the main options and their differences:
Elasticsearch: The open-source alternative
What is Elasticsearch?
An open-source search engine used by many companies as the basis for enterprise search solutions.
Advantages:
- Free (open source).
- Complete control over the solution.
- Large community and many resources. Disadvantages:
- Requires your dedicated IT team for maintenance and further development.
- No AI features (must be developed in-house).
- No compliance support (GDPR, EU AI Act).
- No user interface (must be developed in-house).
- Long implementation time (months to years). Ideal for: Large companies with a large IT budget and a dedicated team.
Not ideal for: Medium-sized companies that need a solution quickly.
Splunk: The log management specialist
What is Splunk?
Originally a log management solution, Splunk has evolved into a broader enterprise search platform.
Advantages:
- Strong in log management and security analysis.
- Established provider with extensive support. Disadvantages:
- Expensive (very high license costs).
- Primarily designed for log management, not for general enterprise search.
- Complex operation.
- Compliance requirements often not fully met. Ideal for: Large companies with a strong focus on security and log management.
Not ideal for: Medium-sized companies that need general enterprise search.
Microsoft Azure AI Search: The Microsoft solution
What is Azure AI Search?
Microsoft’s cloud-based enterprise search solution, tightly integrated with Microsoft 365.
Advantages:
- Seamless integration with Microsoft 365 (Teams, SharePoint, etc.).
- Modern AI features.
- Good support from Microsoft. Disadvantages:
- Your data is hosted in the US (GDPR challenges).
- Expensive for medium-sized businesses.
- Compliance requirements (EU AI Act) not fully met.
- Dependency on Microsoft. Ideal for: Large companies that rely entirely on the Microsoft ecosystem.
Not ideal for: Companies with data protection and compliance requirements.
Glean & Coveo: The modern SaaS solutions
What are Glean and Coveo?
Modern, AI-native enterprise search platforms with a wide range of features.
Advantages:
- Modern AI features.
- Good support.
- Many integrations. Disadvantages:
- US-based, your data may be stored in the US.
- Very expensive (often $500,000+ per year).
- Compliance requirements (GDPR, EU AI Act) not fully met.
- Overkill for many medium-sized businesses. Ideal for: Large, international companies with a high budget.
Not ideal for: Medium-sized companies with budget constraints.
amberSearch: The European alternative
What is amberSearch?
A German, GDPR-compliant enterprise search solution, specially designed for medium-sized companies.
Advantages:
- German solution, made in Germany: Trust and local support.
- GDPR-compliant: Your data hosted in Germany, no training with your data.
- EU AI Act-compliant: Compliance with new EU regulations.
- ISO 27001-certified: Proof of security standards.
- SME-focused: Easy implementation, fair prices.
- Modern AI features: RAG technology, natural language search.
- Fast implementation: 3-phase model, approx. 4–6 weeks.
- 200+ successful implementations: Proven expertise. Disadvantages:
- Smaller provider (but growing rapidly).
- Fewer integrations than large providers (but growing). Ideal for: Medium-sized companies that prioritize security, compliance, and fair pricing.
Summary:
For many medium-sized companies, amberSearch is the best choice – not because it has the most features, but because it offers the best value for money and meets your compliance requirements.
Integrations & interfaces: Which systems are supported?
An enterprise search is only as good as its integrations. Here is an overview of the systems that are typically supported:
Cloud applications
- Microsoft 365: Teams, SharePoint, OneDrive, Outlook, Exchange
- Google Workspace: Gmail, Google Drive, Google Docs, Google Calendar
- Slack: Channels, messages, files
- Salesforce: Contacts, leads, opportunities, custom objects
- HubSpot: Contacts, deals, tickets
- Jira: Issues, projects, documentation
- Confluence: Pages, spaces, attachments
On-premises systems
- SharePoint (on-premises): Documents, lists, pages
- Network drives: All file types
- Local databases: SQL Server, Oracle, PostgreSQL
- Document management systems: FileNet, Documentum, etc.
- Email servers: Exchange On-Premise
Specialized enterprise applications
- SAP: Documents, data
- Oracle: Documents, data
- Other ERP systems: Depending on the provider
File formats
- Documents: Word, PDF, PowerPoint, Excel, Google Docs
- Emails: Outlook, Gmail
- Images: JPG, PNG, etc. (with OCR support)
- Videos: With automatic transcription (depending on the solution)
- Websites: HTML, etc. Important:
Not all solutions support all systems. If you use a specialized system, you should check whether this system is supported before choosing a solution.
Security, compliance, and data protection:
The foundation of a trustworthy solution
Security and compliance are not optional; they are the foundation of a trustworthy enterprise search. You should pay attention to the following:
ISO 27001 certification
What is ISO 27001?
ISO 27001 is an international standard for information security. This certification means that the company is regularly audited by an independent body and meets certain security standards.
Why this is important:
Certification provides objective proof of a certain level of security. Without it, you should be skeptical.
GDPR compliance
What is the GDPR?
The EU’s General Data Protection Regulation is the legal basis that regulates how companies and public authorities may process personal data.
Critical points from a data protection perspective for enterprise search:
- Data hosting: Your data should be hosted in the EU, not in the US.
- Data processing: Only authorized employees should have access to your data.
- Data security: Your data should be encrypted throughout.
- Data deletion: Your employees should be able to delete their data. Red flags:
If a provider hosts data in the US or cannot clearly explain how it meets GDPR requirements, you should be cautious.
EU AI Act compliance
What is the EU AI Act?
The EU AI Act is a new regulation for AI systems. It regulates how AI systems may be developed and used.
Critical points for enterprise search:
- No training with your data: AI models should not be trained with your data.
- Transparency: It should be clear how the AI works and what data it uses.
- Auditability: It should be possible to verify how the AI makes decisions. Red flags:
If a provider cannot clearly demonstrate that it does not use your data for training, this is a major compliance risk.
Audit logs
Why it’s important:
For compliance and security, it’s important to track who has accessed what information. A good audit log should show:
- Who accessed which documents?
- When was it accessed?
- What search queries were made? Ideal: A detailed audit log that can be used for compliance audits.
Trends & Future of Enterprise Search
The enterprise search landscape is evolving rapidly. Here are the key trends for 2025 and beyond:
1. AI becomes the standard, not the exception
All modern enterprise search solutions will have AI features. The question is no longer “Are there AI features?” but “How good are the AI features?” This means that solutions without AI will quickly become obsolete.
2. RAG becomes the standard for compliance
RAG (retrieval-augmented generation) is becoming the standard because it meets compliance requirements—AI models are not trained with your data. This is not only a technical trend, but also a regulatory trend.
3. Compliance becomes a differentiator
GDPR, EU AI Act, SOC2 – regulatory requirements are growing. Solutions that meet compliance requirements will have a competitive advantage. This is particularly important for European companies.
4. Multilingualism is becoming standard
Many companies are international. Enterprise search solutions that are multilingual will have an advantage.
5. Integration into workflows is becoming more important
Enterprise search solutions that integrate seamlessly into your workflows (Teams, Slack, etc.) are preferred. Your employees should not have to switch to a separate application.
6. Governance and auditing will become more important
Companies want to know who has accessed what information. Governance and audit logs will become standard.
7. Agent-based automation
In the future, enterprise search solutions will not only answer questions, but also automate tasks. Example: “Find all contracts with Acme Corporation that need to be renewed next month and create a summary for the CEO.”
Conclusion: Enterprise search is not optional, but strategically necessary
Enterprise search is not a “nice-to-have.” It is the lever that connects data silos, reduces search time, and makes knowledge available as a basis for work—securely and transparently.
If you want to get started, take a pragmatic approach:
- Choose a clear use case (support, service, sales, R&D).
- Start with a pilot.
- Measure search time, ticket deflection, onboarding speed, and adoption.
Try amberSearch for free
Convinced that an enterprise search solution makes sense for your company? Then this should be your next step:
You can try amberSearch for 30 days free of charge—no credit card required. This will give you a realistic impression of how the solution works in your company.
FAQ – Frequently asked questions about enterprise search solutions
How long does it take to implement an enterprise search solution?
That depends on the solution and your infrastructure. A well-designed cloud solution can be implemented in 4–6 weeks. An on-premise solution or a complex implementation can take months, depending on the circumstances and size of the company. The 3-phase implementation of amberSearch typically takes 4-6 weeks.
Is searching with amberSearch GDPR-compliant?
Yes. amberSearch is ISO 27001 certified and GDPR and EU AI Act compliant. Your data is hosted in Germany, and AI models are not trained with your data. This is one of the key differences of amberSearch.
Which systems and data sources can be connected?
amberSearch supports cloud applications (Microsoft 365, Google Workspace, Slack, Salesforce, etc.), on-premise systems (SharePoint, network drives, local databases), and specialized enterprise applications. For a complete list, see: Integrations.
How does amberSearch differ from open source solutions such as Elasticsearch?
| Aspect | Elasticsearch | amberSearch</ td> |
| Cost | Free, but requires IT effort | Subscription, but no IT effort required |
| Implementation time | Months to years | 4–6 weeks |
| AI features</ td> | None (must be developed in-house) | Integrated (RAG, natural language search) |
| Compliance | None (must be ensured independently) | Integrated (GDPR, EU AI Act, ISO 27001)</ td> |
| Support | Community-based | Dedicated support team |
| Ideal for | Large companies with an IT team | Medium-sized companies |
Conclusion: Elasticsearch is a great solution if you have a large IT team and want complete control. amberSearch is better if you need a quick solution that works right away and meets your compliance requirements.
Will my data be used to train AI models?
No. amberSearch uses RAG (Retrieval-Augmented Generation), which means that AI models are not trained with your data. Your data remains completely private and is not shared with third parties. This is an important compliance and security advantage.
How much does an enterprise search solution cost?
That depends on the solution and the size of your company. Open source software is free (but requires IT effort). Cloud solutions such as amberSearch typically cost EUR 200,000–500,000 per year for a medium-sized company. Large solutions such as Glean or Coveo can cost EUR 500,000+ per year. For an exact quote, you should talk to the provider.
Information on prices for amberSearch can be found here.
Do we need our own IT department to manage it?
That depends on the solution. A well-designed cloud solution requires minimal IT effort. One IT administrator should be sufficient to manage the solution. An open source or on-premise solution requires a dedicated team. amberSearch is designed to require minimal IT effort—typically, one IT administrator can manage the solution.