Humans are way better than computers when it comes to common sense and understanding natural language. thingsTHINKING brings common sense to computers.
Many tasks today can only be handled by humans since working with natural language requires contextual information and the ability to understanding the subject matter.
Our technology combines real-world knowledge with statistical information (deep learning, etc.) to get to the actual meaning of natural language - this is what DARPA calls the Third Wave of AI.
thingsTHINKING understands relevant and useful information from your unstructured data not unlike a human. This accelerates finding things, making contextual decisions, and - as we humans like to say - doing the job right.
Think of us as a sidekick, a forklift for your brain.
As winner of the prestigious new.New Festival "Industry Disruptor" Award, we are proud to being recognized for one of the leading NLP/NLU/Semantics AI platforms in 2018.
As featured in various media outlets,
e.g. Financial Times (US), FAZ, Süddeutsche Zeitung, Die Welt, Reuters TV, RTL Television, ARD, DPA, Huffington Post, Die Zeit, Twitter, etc.
"Words exist because of meaning. Once you've gotten the meaning, you can forget the words."
The Shift from Pure Training to More Understanding
There are two technologies to establish AI: Bottom Up AI training (machine learns from examples) or Top Down AI teaching (man explains to machine).
Many times, AI cannot be trained automatically because of a lack of training data (/-quality). This is where we combine both technologies to make AI understand.
Capturing the Meaning of Data
... is the centerpiece of thingsTHINKING‘s technology and can be used for Auditing, LegalTech, InsurTech, Bots, Requirements Engineering, and many other AI use cases.
SEMANTIC PROCESSING PLATFORM
Different Layers of Micro Services
Applications can be built on domain-specific services. They leverage and orchestrate the basic services from the platform and the domain.
The Platform Micro Services
Provides access to higher-level semantic information.
Semantic Model Similarity
Finds semantic differences and similarities.
Tokenizing, base-form, stemming, ...
Semantic Model Persistence
Stores models for later reference and re-use.
Semantic Concept Similarity
Compares the similarity to other concepts.
Detects language of a given text.
Semantic Model Creator
Generates a semantic model from text input.
Semantic Sanity Check
Checks for sanity according to (world) knowledge.
Semantic Collection Creator
Collects semantic models in specific domains.
Requirement Rule Execution
Detects linguistic flaws in requirements, patents, and legal texts.
Standard integration into the leading RPA systems.
Dr. Sven J. Körner
Chief Executive Officer
I combine and implement research results into products with our customers and partners.
Dr. Mathias Landhäußer
Chief Scientific Officer
I connect tT with research and handle our university relations. Also I spearhead our latest research advancements and prototypes.
Abdelmalik El Guesaoui
Chief Product Officer
I am responsible for our product management and -marketing as well as business development and customer relations.
Georg A. Müller
Chief Technology Officer
I am responsible for our technology and software architecture.