amber
amber_vs_langdock
Langdock alternative with deep integrations

Many companies are currently looking for GDPR-compliant alternatives to ChatGPT. The challenge posed by employees bringing personal AI tools to work - and the lack of control over who uploads what data and where - is ever-present. Langdock offers a simple alternative here. In this article, you’ll learn how amber and Langdock differ. If you want to get started quickly and value integrations with existing systems, you should definitely check out amber as well.

Quick comparison

amber

A European enterprise platform focused on internal corporate knowledge through AI-powered search and AI assistance. At its core is a semantic index/vector approach (for precise retrieval) combined with an action layer for system automation. Particularly effective in established IT infrastructures.

Langdock

A model-agnostic AI platform with integrations with search APIs and MCPs (as of 07/2026), and a multi-model API that is modeled similary to the ChatGPT API. Documents from other systems must be individually selected via Langdock for AI processing (as of 06/2027)

Technical approaches 
amber
  • amber relies on its own semantic vector index with optimized AI retrieval logic and queries the central index - rather than third-party systems - at search time. This usually ensures precise, consistent results with fast response times - even in complex IT environments.
  • amber integrates deeply with on-premises and cloud systems, consistently enforces permissions, and offers standard connectors as well as multi-channel access (e.g., via the Outlook app, desktop app, etc.) for seamless integration.
  • Through consistent, permission-based context, amber improves response quality, reduces distractions, and increases relevance for end users.
  • The central index usually enables faster response times, as search queries do not need to be sent live to third-party providers.
  • AI agents benefit from a reliable knowledge base, as results are enriched with robust, context-specific corporate knowledge.
  • For employees, the experience remains simple and intuitive: ask a question, receive a precise and trustworthy answer - across all systems.
  • amber scales even in heterogeneous IT environments without unnecessarily increasing operational overhead for teams.
  • The index and software are hosted on Telekom’s sovereign T-Cloud in Germany.
Both
  • Both solutions use AI models hosted in the EU.
  • Both approaches enable actions in third-party systems via APIs or MCP.
  • Both companies offer access to multiple AI models from various providers.
Langdock
  • Langdock is primarily based on the search APIs of the connected systems (as of 07/2026).
  • It is hosted on Azure in the EU by default.
History of amber

amber was founded in Aachen in 2020 to make internal company knowledge quickly and easily accessible with AI. The challenges that amber solves are mostly along these lines:

  1. 1
    Grown IT infrastructures – IT infrastructures have grown over the years, with information scattered across drives, M365, Atlassian, DMS, or intranet
  2. 2
    Knowledge carriers leave the company – the “remaining” employees face challenges in quickly accessing internal knowledge
  3. 3
    Processes & products are becoming more complex – employees must keep track of ever-increasing amounts of data
  4. 4
    Employees use private AI solutions – Employees are aware of the challenges mentioned above, which is why they use private AI tools to become more efficient

While the fourth challenge only arose after the release of ChatGPT, we began addressing the first three challenges in 2020 with what is known as enterprise search. The initial goal was to build a kind of “internal Google” for the company. Amber’s focus was therefore initially on integrating a wide variety of software solutions. It has since evolved into a sort of “corporate brain”

amber differs in that it builds its own index and uses its own AI logic for searching, whereas Langdock relies on the search APIs of platforms such as SharePoint, Outlook, and others. Consequently, the quality of the results depends heavily on the respective source systems (as of 07/2026).

Similarities & Differences
Similarities
  • Both offer AI models from various AI providers that are hosted in Europe in compliance with the GDPR. Both support a “bring-your-own-model” approach.
  • Both offer a platform that can be used to automate processes.
  • Both platforms allow users to set up AI assistants and agents.
  • Both platforms are ISO 27001-certified; however, while amber is fully ISO 27001-certified, only Langdock’s processes - not its business premises - are certified (as of 05/2026).
  • Both solutions are GDPR-compliant and comply with the EU AI Act.
amber
  • amber runs on T Cloud - a sovereign cloud from a European provider
  • amber relies on integrations that offset the drawbacks of MCP
  • amber offers a wide range of integration options, including integrations with both on-premises and cloud solutions
  • amber has exclusively European shareholders
  • amber offers a flexible pricing packages
  • amber operates in accordance with SOC 2 standards
Langdock
  • The German company Langdock is wholly owned by an American holding company
  • Langdock hosts its software with American hyperscalers
  • Langdock is SOC 2 certified
amber as an Alternative to Langdock – The Business Impact

Anyone who wants to use AI in a sustainable way will have no choice but to implement deep integrations with core systems. amber uses AI as a core layer here, connecting search, assistance, and automation across on-premises and cloud systems. The semantic search index, deep integrations (including Microsoft 365, DMS/ECM, and CRM), and an action layer ensure well-founded answers with source attribution, rights verification, and immediate process execution. The result: amber can help lead to faster decisions and higher productivity - including sustainable knowledge management based on the right information. Compared to the Langdock approach with a model-agnostic API and integrations via search APIs, you’re less likely to hit limitations when using internal knowledge sources in a precise, permission-based manner.

Try amber

amber offers a free trial period (no credit card required) during which interested companies can get started quickly and invite colleagues:

Try amber now
Conclusion

If you’re looking for a GDPR-compliant Langdock alternative that integrates company knowledge more deeply, amber is the better choice. amber combines semantic AI search, assistance, and automation via its own index with deep on-premises and cloud integrations, as well as end-to-end permissions. This allows internal knowledge and processes to converge securely - decisions become faster and more reliable, productivity increases, and shadow IT decreases. Langdock is an option to start easy, but it reaches its limits when heterogeneous enterprise data needs to be embedded precisely, auditable, and seamlessly into existing systems. amber is the more robust choice for this: deeply integrated, enterprise-ready, and also quick to deploy.

Don’t want to get started right away but have more questions about why amber is a Langdock alternative? Then use our contact form to ask a question: