amber

A platform for all your company's knowledge – Search, Assistant, Agents

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7.5/10 KIFOX Score – Good

Location: Germany ⓘ

API Integration Automation Image generation Chatbot Data Analysis Document analysis AI agents Research Speech recognition Text recognition Text generation Knowledge Base Summary

Further link

amber is a German business AI platform that makes dispersed corporate knowledge discoverable and usable for generative AI. The platform integrates with systems such as SharePoint, Teams, Confluence, Jira, Salesforce, file storage systems, email systems, and in-house applications. The data does not need to be fully migrated to a new knowledge system.

Hybrid search combines traditional text search with semantic and vector-based search. The information found is then used as context for generative responses. This enables amber to answer questions based on internal documents and cite the sources used.

Target audience

amber is designed for medium-sized and large companies with knowledge spread across various IT systems. Typical users come from the fields of manufacturing, mechanical engineering, consulting, administration, pharmaceuticals, sales, customer service, human resources, quality management, and IT.

The platform is particularly useful for organizations where employees spend a lot of time searching for documents, contacts, guidelines, and past project results. Small teams can start with the Starter plan; the larger plans are designed for comprehensive enterprise search.

Outstanding features

A key feature is the inheritance of existing access rights. In amber, users should only see content to which they also have access in the original system. This ensures that the internal search does not become an uncontrolled access point for confidential information.

amberAI serves as a corporate GPT based on connected data. amberAgents extends this approach to include recurring and multi-step tasks. Agents can be configured using natural language, equipped with knowledge, and connected to third-party systems via APIs or automation platforms.

Key Areas of Application

Typical applications include internal knowledge search, technical support, searching manuals and guidelines, onboarding, quality management, proposal preparation, and project knowledge. Employees can ask questions, summarize documents, combine information from various systems, and access the underlying sources.

AI agents can also gather information, generate decision-making criteria, and trigger follow-up actions in workflow or business systems. The exact level of automation should be limited based on the risk associated with each process.

Usage & Notes

For production use, relevant data sources are first connected and the imported access rights are verified. Next, search quality, indexing, domain-specific terminology, and retrieval are configured. A phased rollout with pilot groups is recommended.

Generated responses must still be reviewed by subject matter experts. Particularly when it comes to legal, financial, technical, or personal decisions, sources should be disclosed and human approval should be required. Agents with write or action permissions require strictly defined authorizations, logging, and, where appropriate, a dual-control principle.

Target audience	Assessment
Individuals	Probably not – amber is geared toward corporate knowledge, teams, and professional data sources.
Self-employed / Freelancers	To some extent, yes – the Starter plan is available for a single user, but is particularly worthwhile for those with extensive expertise and recurring AI tasks.
SMEs	Very well suited – the primary target audience for corporate AI, knowledge search, onboarding, documentation, and cross-system research.
Large enterprises	Very well suited – Enterprise supports custom infrastructure, staging, SSO, LDAP/AD, auditing, and numerous data sources.
IT and development teams	Very well suited – API, MCP, custom integrations, agents, and Action Layer enable technical automation.
Knowledge management / internal search	Very well suited – The core use case is permission-sensitive search across disparate enterprise systems.
Service, Sales, and Project Teams	Very well suited – Information from CRM, DMS, email, project systems, and documents can be queried centrally.
Organizations with critical data protection requirements	Very well suited – Germany-based hosting, ISO 27001, AVV documentation, no training required, and optional use of your own infrastructure are major advantages.

Hosting & Data

✅ = well covered ⚠️ = partial / indirect ❓ = not available / unclear

On-prem / local hosting	❓
Private cloud / data center	✅
EU SaaS / Managed	✅
Hybrid	⚠️
DPA / AVV	✅
No training on customer data	✅
Open source / transparency path	⚠️

On-prem / local hosting: unclear

The website does not specify a clear on-premises/local deployment option for amber itself. There are only indirect references, such as "offline" executable models or support for third-party on-premises systems, but no explicit self-hosting option for amber.

Private Cloud / Data Center: Covered

The security page mentions “T cloud data centers in Germany” as well as “virtual network isolation per customer.” This suggests a segregated, controlled hosting environment located in the EU.

EU SaaS / Managed: Covered

The provider describes amber as a hosted platform with processing in Germany and customer data hosted “exclusively by Telekom, unless otherwise requested by the customer.” This clearly demonstrates managed SaaS operations within the EU.

Hybrid: Partially

The website shows that amber integrates with the customer’s internal systems, in some cases also within internal networks or on-premises sources. This confirms a hybrid deployment involving internal data sources and the external amber platform, but does not indicate a comprehensively documented hybrid architecture at the hosting level.

AVV / DPA: Covered

The website explicitly refers to “AVV/DPA documents” and, in the Help Center, to “amber_AVV&TOMs (Standard).pdf.” This confirms the presence of an AVV/DPA on the website, even if the detailed content is not fully accessible to the public.

No Training: Covered

The Help Center states that interactions are not used to improve AI models without explicit consent. The open-source page also states that general public data—not customer data—is used for training.

Open Source / Transparency: Partially

There is a dedicated “Open Source Contributions” page with published portions of the code and AI models. This creates transparency but does not demonstrate a fully open or self-hostable product path for amber as a whole.

Data Processing

According to the website, amber operates the models directly itself and “never” uses external service providers or APIs for amberAI. Development and processing steps take place on servers in Germany. The platform and index run in T Cloud data centers in Germany; according to the privacy notice, customer data is hosted and processed exclusively by Telekom, unless the customer requests otherwise. According to the security page, third-party components and processors are vetted and contractually bound prior to use; however, a specific list of subprocessors is not provided on the website.

Conclusion

For EU/EEA users, amber is well-documented according to the information published on the website and is clearly geared toward GDPR-compliant use in the best possible way: Hosting in Germany, references to the General Terms and Conditions (AVV) and Data Processing Agreement (DPA), Privacy by Design, certifications, and opt-in instead of tacit consent for AI training all clearly support this. There are some drawbacks regarding transparency concerning subprocessors and the lack of a clear statement on whether the product itself offers true on-premises/self-hosting.

Sources

On-prem / local hosting	❓
Private cloud / data center	✅
EU SaaS / Managed	✅
Hybrid	⚠️
DPA / AVV	✅
No training on customer data	✅
Open source / transparency path	⚠️

On-prem / local hosting: unclear

Private Cloud / Data Center: Covered

The security page mentions “T cloud data centers in Germany” as well as “virtual network isolation per customer.” This suggests a segregated, controlled hosting environment located in the EU.

EU SaaS / Managed: Covered

Hybrid: Partially

AVV / DPA: Covered

No Training: Covered

Open Source / Transparency: Partially

Data Processing

Conclusion

Sources

Strengths & weaknesses at a glance

Strengths	Weaknesses
• Corporate knowledge remains linked to its original permissions.	• Minimum purchase quantities apply to the full internal search feature in the professional plans.
• Answers include traceable sources.	• System integration, authorization policies, and data quality require a structured onboarding process.
• No manual full migration of knowledge bases is required.	• According to the provider, on-premises enterprise deployment has high hardware requirements.
• Combination of search, chat, deep research, and agents.	• The quality of results depends on the source systems, metadata, and documents.
• Very broad system and API integration.	• Generative responses may be incomplete or incorrect despite RAG.
• German hosting in the T Cloud.	• Autonomous actions require governance, approval limits, and logging.
• ISO 27001-certified information security management.	• The platform is proprietary; it is not a fully open-source enterprise search solution.
• AVV and documented TOMs.	• Some performance claims, such as time savings or efficiency gains, come from the provider and are not substantiated by independent benchmarks.
• No use of customer data for model training, according to the provider.
• Dedicated infrastructure or on-premises path for Enterprise.
• Administrable model selection and SSO.

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