“A platform for all your company’s knowledge—search, assistant, agents.”
amber connects internal corporate systems and makes their content accessible via a central AI search. amberAI answers questions based on internal data and cites the sources used. amberAgents automates recurring, multi-step tasks and can trigger actions in other systems via interfaces.
amber
A platform for all your company's knowledge – Search, Assistant, Agents
Location: Germany ⓘ amber Tech GmbH, Jülicher Straße 72a, 52070 Aachen, Germany
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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
1) On-prem / local hosting
Meaning: The company operates the solution on its own hardware or within its own infrastructure. In the strictest sense, not only the application runs locally, but ideally the model as well.
2) Private cloud / data center
Meaning: The solution runs in a dedicated or more clearly separated cloud environment, often with a hosting provider or hyperscaler, but in a German data center or in a particularly controlled environment.
3) EU SaaS / managed
Meaning: The provider operates the solution itself as a service. The company uses the tool as a ready-made cloud service, ideally with EU data residency.
4) Hybrid
Meaning: One part of the processing remains internal / local / in a private cloud, while another part runs in an external cloud or EU SaaS.
5) AVV / DPA
Meaning: This is the data processing agreement or Data Processing Addendum. It governs that the provider processes personal data on behalf of the customer and is bound by the customer's instructions.
6) No training
Meaning: The provider does not use your prompts, uploads, attachments, chat histories, or outputs for training or improving the general model — ideally excluded by contract.
7) Open-source / transparency path
Meaning: There is a path toward greater technical transparency and sovereignty, for example through:
- open models
- documented components
- self-hostable parts
- traceable architecture
- export / switching options
| On-prem / local hosting | ✅ |
| Private cloud / data center | ✅ |
| EU SaaS / Managed | ✅ |
| Hybrid | ✅ |
| DPA / AVV | ✅ |
| No training on customer data | ✅ |
| Open source / transparency path | ⚠️ |
Overall assessment:
A Germany-hosted enterprise SaaS platform with the option of deployment on the customer’s own infrastructure. Amber connects enterprise systems via native connectors and creates a search index without requiring all information to be manually migrated to a new knowledge system. The platform combines classic text search, vector search, and semantic search to generate answers with source references.
The product family includes amberSearch for permission-based enterprise search, amberAI as a corporate chat with multiple language models, amberAgents for recurring tasks and actions, and amberProjects for project-related work with chat, history, and results.
amber supports more than 70 integrations. Officially listed among others are Microsoft 365, SharePoint, Teams, Outlook, Confluence, Jira, Salesforce, DATEV, Nextcloud, DocuWare, ELO, d.velop, GitLab, HubSpot, and various on-premise systems. Access rights from the source systems are carried over, ensuring that users only see content they are authorized to view in the source system.
Data processing: Depending on the integration, amber processes documents, emails, chat messages, CRM information, wiki content, database entries, images, scans, search queries, prompts, responses, and permission information. The Business plan supports OCR for scanned documents. Via API, MCP, and the Action Layer, agents can retrieve data, update systems, and trigger workflows.
Hosting: By default, Managed SaaS on the T Cloud in Germany. Enterprise enables a staging environment and hosting on the customer’s own infrastructure, but according to the provider, this requires appropriate hardware. On-premise sources can be integrated via secure connections.
Training: According to amber, customer data is not used to train the language models deployed. The business context is provided via search and RAG at the time of the query.
Conclusion:
amber is particularly well-suited when internal knowledge is distributed across many systems and needs to be centrally located, queried, and made available to agents without data migration. For small teams without relevant knowledge bases, the platform may be overkill; for mid-sized and enterprise organizations, it is functionally robust and very well positioned in terms of data protection.
| On-prem / local hosting | ✅ |
| Private cloud / data center | ✅ |
| EU SaaS / Managed | ✅ |
| Hybrid | ✅ |
| DPA / AVV | ✅ |
| No training on customer data | ✅ |
| Open source / transparency path | ⚠️ |
Overall assessment:
A Germany-hosted enterprise SaaS platform with the option of deployment on the customer’s own infrastructure. Amber connects enterprise systems via native connectors and creates a search index without requiring all information to be manually migrated to a new knowledge system. The platform combines classic text search, vector search, and semantic search to generate answers with source references.
The product family includes amberSearch for permission-based enterprise search, amberAI as a corporate chat with multiple language models, amberAgents for recurring tasks and actions, and amberProjects for project-related work with chat, history, and results.
amber supports more than 70 integrations. Officially listed among others are Microsoft 365, SharePoint, Teams, Outlook, Confluence, Jira, Salesforce, DATEV, Nextcloud, DocuWare, ELO, d.velop, GitLab, HubSpot, and various on-premise systems. Access rights from the source systems are carried over, ensuring that users only see content they are authorized to view in the source system.
Data processing: Depending on the integration, amber processes documents, emails, chat messages, CRM information, wiki content, database entries, images, scans, search queries, prompts, responses, and permission information. The Business plan supports OCR for scanned documents. Via API, MCP, and the Action Layer, agents can retrieve data, update systems, and trigger workflows.
Hosting: By default, Managed SaaS on the T Cloud in Germany. Enterprise enables a staging environment and hosting on the customer’s own infrastructure, but according to the provider, this requires appropriate hardware. On-premise sources can be integrated via secure connections.
Training: According to amber, customer data is not used to train the language models deployed. The business context is provided via search and RAG at the time of the query.
Conclusion:
amber is particularly well-suited when internal knowledge is distributed across many systems and needs to be centrally located, queried, and made available to agents without data migration. For small teams without relevant knowledge bases, the platform may be overkill; for mid-sized and enterprise organizations, it is functionally robust and very well positioned in terms of data protection.
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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GDPR-compliant usage possible?
Overall assessment:
Very well suited for GDPR compliance, subject to the specific
data sources and company settings. amber is operated by amber Tech GmbH, based in Aachen. The provider offers AVV/DPA documentation and technical and organizational measures, and operates an ISO 27001-certified information security management system. According to the provider, the platform is designed in accordance with Privacy by Design and Privacy by Default.
A positive aspect is that the platform and search index are operated in German T-Cloud data centers. Virtual network isolation is provided for each customer. amber lists role-based permissions, least-privilege access, 2FA, SAML 2.0, LDAP/Active Directory, encrypted remote access, firewalls, vulnerability scans, and comprehensive audit logging.
amber explicitly states that it does not use customer data to train AI models. According to the provider, the integrated models are deployed in Europe in compliance with the GDPR. Administrators can control which models are used. Processing is based on RAG and search indexes: corporate knowledge is used as context at runtime without retraining a general language model with customer data.
Server location: By default, Germany on T Cloud infrastructure. With the Enterprise plan, amber can be operated on the customer’s own infrastructure. The specific hardware and operational architecture of the customer’s deployment is agreed upon individually.
Deletion and Retention: Sensitive data can be completely removed from the amber servers upon request; deletion metadata is logged. Backups are created for recovery purposes. The exact retention periods for production data, backups, and the end of the contract should be verified in the respective General Terms and Conditions (AVV) and offer.
A negative aspect, or one requiring review, is that amber can integrate numerous external systems and various AI models. Each data source—such as SharePoint, Salesforce, DATEV, Confluence, or email—brings its own permissions, personal data, and retention rules. These source systems and the selected models must therefore be included in the record of processing activities and, if necessary, in a data protection impact assessment.
Conclusion: amber is among the European enterprise AI platforms with stronger data protection measures. To ensure proper implementation, the register of processing activities, retention periods, model approvals, source systems, permission synchronization, and agent actions should be centrally managed by IT and data protection.