Founded 2017 | HQ Karlsruhe, Germany | 45 employees (approx.) | <€3M revenues (approx.)

The thingsTHINKING “Semantha” platform provides automated document comparison and validation via a trusted technique – producing an identifiable fingerprint to reliably represent a document. With its short time-to-value in terms of customer training and successful use cases in automotive and insurance, the company is worth a look for organizations needing to compare documents or specifications.

The Company

thingsTHINKING was founded in 2017 by a four-person research team from the Karlsruhe Institute of Technology and remains located in that city in southwest Germany. CEO Sven Körner is one of the original founding team members, with the remainder holding senior positions. thingsTHINKING raised €4.5 million from Earlybird Venture Capital in May 2021, and we currently estimate the company’s revenues are sub €3 million.

The Technology

thingsTHINKING’s core platform is Semantha, which is designed to provide nearly out-of-the-box processing for text- and data-heavy documents, with the company suggesting that it requires a “handful” of additional documents as examples for new customers to onboard.

As its center, Semantha assembles what thingsTHINKING refers to as a “Semantic Fingerprint”: a multimodal collection of specific identifiers from word embeddings and document layout/formatting feeding into the pre-trained language models and graph/knowledge bases. The result is a meta fingerprint that combines the output of this variety of approaches into a single common exchange format (see Figure 1).

This fingerprint can then be used for a couple of core use cases; document comparison (Semantha Compare) and document validation (Semantha Requirements). Comparison is designed to sit within an automation where, for example, newly arrived contracts can be quickly verified against previously accepted/approved versions, so that any change deltas can be routed for attention, but those without change can pass without automatic checks. Validation use cases include being able to check requirements documents to see if there are new elements that require human intervention (for example within a Requirements Management tool). These results can be fed back into Semantha’s knowledge base to improve subsequent tasks.

At a deeper level, the Semantic Fingerprint itself is an aggregation of smaller fingerprints from within the document at paragraph and sentence level. Of note here is the ability to determine close but negative matches for similarity; for example, “water soluble” vs. “not water soluble,” where the operator entirely flips the meaning of the sentence and therefore the overall meaning of that section of the document.

Using the default models allied with what has been added to Semantha in terms of customer-specific domain knowledge, thingsTHINKING envisages this being packaged as a “Co Worker”: a packaged app that can be utilized in other parts of the organization to extend the use of the platform to a greater range of employee tasks (and, in parallel, the reach of Semantha within that organization).

As such, thingsTHINKING sees Semantha as being a critical point of augmentation in existing processes and has enabled the platform with a REST API complete with more than 150 preset connectors. The company is building out a partner network to help locate and integrate such opportunities. Their alliance with RPA vendor UiPath makes perfect sense as a starting point for intelligence to be added to process bots; so do integration partnerships with Accenture and SVA to enable scaling of the necessary services to grow their initial customer base.

At present, thingsTHINKING says its customer base is predominantly a mix of automotive (including Hella) and insurance/legal (including Heidelberger Volksbank and Gothaer Versicherung). However, the company intends to grow from that vertical base toward broader connections to enterprise platforms – and employee experiences – using that common exchange format as a vehicle to help share Semantha’s multi-modular output to a greater range of use cases. To assist this widened scope, video and audio embeddings will be added to the platform over the next year.

Figure 1
Semantha Platform Overview

Our Opinion

thingsTHINKING’s Semantha employs a trusted technique – producing an identifiable fingerprint as a reliable representation of a document, allowing it to be quickly compared – from a platform that promises a very short time-to-value in terms of specific customer training. That paying customers are already using this in technical information management environments (automotive, insurance) demonstrates that this promise is not just theoretical, even if the current modest revenues do not reflect significant investment just yet. At its core, the use cases feel solid, with plenty of scope to appeal not only to those information-management-rich environments, but also to a range of adjacent industries, even before the planned extension into audio and video processing.

Advice to Buyers

Where automated precision of comparing documents or specifications is of high value, the thingsTHINKING approach should be considered for evaluation. This evaluation should – as always – focus upon solid representation of the total scope of the information that could be processed, with a strong eye on whether a small number of organizational-specific training materials does produce a desired accuracy rate. It is also worth investigating whether existing search tools within your organization would be able to take advantage of that Semantic Fingerprint as a document vector within the index to be used at query time, to provide additional value.

SOAR Analysis


  • Minimal time-to-value for new customers
  • Sensible approach to document comparison and analysis


  • Adding video and audio processing
  • Integration into a broader employee experience


  • Extend its geographic footprint beyond central Europe, given its solid base in insurance and automotive
  • Continue creating a partner network to help expand product capabilities and geographic reach


  • Established an initially strong base in analysis of industry-specific documents for automotive and insurance
  • Established the beginning of a partner network

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