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Our applied-AI lab in Berlin. It is where we build foundations for using language models and standalone products you can use today. The tour leads through four rooms, each running an experiment as you scroll.

Location
Berlin
Tour
4 rooms, 2 wings
Periodic table
9 projects

Mindverse Labs explores and builds novel, cross-industry AI applications together with customers.

Some of the projects our AI team works on start as experiments to validate optimization potential.

This tour takes a closer look at four of them: two internal research projects and two external projects already in daily use. Each room follows the protocol of an experiment: hypothesis, method, finding.

WingA

Internal research

Foundational research into using language models: how does a request find the right model? And how does a prompt reach a language model without giving away personal data?

Room 01 Wing A In development

Hydra: the right model for every request

Not every task needs the biggest language model. Hydra reads the request, weighs quality, cost and response time, and selects one model, with a reason you can trace.

  1. 01Hypothesis

    The task should pick the model, not habit.

    Reading an invoice, reviewing code, drafting a customer reply: each task asks for something different. Send everything to the same model and you often pay for capabilities the task does not need.

  2. 02Method

    Understand the task, weigh the priorities, pick one model.

    The planner identifies the task type, reasoning needs and output format, narrows down the eligible models and weighs quality, generation cost and response time. Each request currently gets exactly one selected model, and every decision goes into the decision record.

  3. 03Finding

    85.43 out of 100 for $0.02 per 100 tasks.

    That is how Hydra configuration D performed in our internal benchmark from September 2026: 100 planned tasks, 98 of them scored with Fable 5.1. It is a development result with documented limits, not proof of universal savings. Hydra is in development, and you can already try the route inspector.

Room 02 Wing A Live

Persona: your text reaches the model, your customers’ data does not

Persona detects personal data in real time and swaps it for consistent pseudonyms before a prompt goes to a language model. The model’s response is translated back automatically.

  1. 01Hypothesis

    A language model needs the content, not the identity.

    To summarise an insurance claim, a model does not need to know the customer’s name, date of birth or phone number. It only needs to tell the people and details apart.

  2. 02Method

    Detect precisely, replace consistently, swap back safely.

    A specialised language model tags names, dates, addresses, numbers and more with character-level precision. Each person gets a stable pseudonym such as ⟨PERSON_1⟩ that stays the same throughout the text. The model’s reply runs back through Persona and gets the originals restored.

  3. 03Finding

    Nine categories in about 45 milliseconds, and nothing is stored.

    Persona runs as an API on servers in Germany, processes text without storing it and is live: 100 free requests a month let you try it without a credit card.

WingB

External projects

Standalone products with their own brands. Each solves one everyday problem and shows us how AI holds up when lots of people use it.

Room 03 Wing B Live

AudioZuText: turning speech into text

Upload an audio or video file or connect a meeting, and get a transcript with speakers, a summary and chapters.

  1. 01Hypothesis

    What is said in meetings gets lost unless someone writes it down.

    Interviews, lectures, customer calls: the most important information often sits in recordings nobody listens to twice.

  2. 02Method

    Transcribe, separate the speakers, pull out what matters.

    AudioZuText converts recordings in 100 languages into text, recognises who is speaking, and generates a summary, chapters and action items. Meeting bots for Zoom, Google Meet and Teams can take notes live.

  3. 03Finding

    More than 130,000 transcriptions, tested in everyday use.

    AudioZuText is live, GDPR-compliant and free to try without signing up: two transcriptions a week up to 100 MB. For teams, transcription with speaker recognition is also built into Mindverse Studio.

Room 04 Wing B Live

Ausfüllpilot: filling in forms, even when they are only scanned

Upload a PDF, check the fields, add what is missing: Ausfüllpilot detects text, checkbox, table and signature fields and fills in the PDF cleanly.

  1. 01Hypothesis

    Forms are the last bit of paper in digital life.

    Child benefit, housing benefit, student finance, business registration: many German forms only exist as non-fillable PDFs or scans. So people print them, fill them in by hand and scan them again.

  2. 02Method

    Detect fields on the page image itself, even on skewed scans.

    Ausfüllpilot finds the fields on the page image and shows them for review. Then the assistant asks targeted questions instead of going field by field. Values set by the AI stay marked.

  3. 03Finding

    Fillable, non-fillable or scanned: one flow.

    Ausfüllpilot is live, with three forms a month for free. Its AI runs only in EU data centres in Frankfurt, and data is deleted automatically and never used for AI training.

The periodic table of Mindverse Labs

Every project an element. Some are standalone products today, others live on in Mindverse Studio or are waiting for their next experiment. You just visited the four dark ones.

  • Infrastructure
  • Speech & audio
  • Documents & legal
  • Search & knowledge
  • Media & health
  • on the tour

The lab rules

Four rules decide what leaves the lab.

  1. §1

    Measure before we claim.

    Hydra publishes its benchmark with confidence intervals, run conditions and limits. A number without context is not a result to us.

  2. §2

    Privacy is part of the setup.

    Where data is processed, how long it stays and whether it is used for training belongs in the experimental setup, not in the small print. Ausfüllpilot deletes automatically and never trains on your data; Persona stores nothing at all.

  3. §3

    Real use beats any demo.

    External projects are put to the test in many people’s everyday lives. More than 130,000 transcriptions with AudioZuText show us what holds up in practice and what does not.

  4. §4

    What proves itself moves into Studio.

    Many features teams use every day started as Labs experiments: transcription with speaker recognition, autonomous AI agents and research with citations.

Built with teams that lead the way

Labs projects are never built in a vacuum: we develop with the departments that genuinely want to improve their processes.

Have an idea but no way to build it? Talk to us, and together we will validate whether and how AI can be implemented optimally for you.

From workshops to a custom AI project, the Mindverse team accompanies you from hypothesis to production.

  • For legal teams

    Mindverse has already built multiple AI solutions for tax advisors, law firms and in-house legal departments, including AnwaltGPT with more than 1,000 lawyers.

    AI for legal
  • For sales teams

    AI-powered market research methods and needs analyses, developed and validated together with sales teams.

    AI for sales & support
  • For operations

    AI-powered document management, reporting and cross-team process design, in use across industries.

    Explore workflows
  • For researchers

    Research in closed environments with large databases, or targeted web research, always with traceable sources.

    Explore AI research

// FAQ

Frequently asked questions about Mindverse Labs

What is Mindverse Labs?
Mindverse Labs is the research and development unit of Mindverse in Berlin. It is home to internal research projects such as Hydra and Persona and to external projects such as AudioZuText and Ausfüllpilot. Experiments that prove themselves become standalone products or part of Mindverse Studio.
How is internal research different from external projects?
Wing A shows our internal research into the foundations of using language models: Hydra picks the right model for each request, and Persona pseudonymises personal data before a prompt reaches a language model. Wing B shows external projects: standalone products with their own brands, each solving one everyday problem.
Can I use the projects already?
Persona, AudioZuText and Ausfüllpilot are live and free to try. Hydra is in development; you can try its route inspector at hydraroute.com.
How do the projects handle data?
Persona processes text on servers in Germany and does not store it. Ausfüllpilot runs its AI only in EU data centres in Frankfurt, deletes data automatically and never uses it for AI training. AudioZuText is GDPR-compliant. With Hydra, routing takes place in Germany; selected models may run with external providers.
Can my company work with Mindverse Labs?
Yes. Many Labs projects are built together with specialist teams. Book a call and we will work out together whether and how your idea can be built with AI.

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