Summary

• Prismus has reached Venture Kick's third-stage milestone, bringing its cumulative programme support to CHF150,000.
• Its Lupa platform uses AI agents to connect research publications, patents, organisations, and technical market information.
• The Zurich company reports 30 paying customers and plans to expand its platform across specialist industries.

Zurich-based Prismus has reached the final stage of the Venture Kick support programme, bringing its cumulative funding through the initiative to CHF150,000 as it expands an AI platform connecting scientific research with potential customers and commercial applications.

The company operates Lupa, which brings together information from research publications, patents, companies, laboratories, and other technical sources to identify markets, partners, and opportunities relevant to specialist technologies.

Completion of the programme was reported on 30 October, with Prismus planning to expand adoption across deeptech industries and make the platform available to more individual users. It currently reports 30 paying customers, reflecting an increase in commercial adoption since its earlier Venture Kick milestones.

The CHF150,000 represents support secured across the programme’s three stages, rather than a new financing round added to its previous awards. Venture Kick combines financial backing with business development support for Swiss companies seeking to commercialise scientific and technological innovations.

Connecting technical knowledge with market demand

Companies developing specialist technologies frequently operate in markets where potential customers are difficult to identify through conventional business research. A new component or scientific technique may address problems across several industries, even when its developers initially understand only one group of applications.

Discovering those connections can involve examining academic publications, patent filings, technical specifications, and commercial activity. The relevant information is often distributed across several databases and may be described using terminology specific to each research discipline.

For example, a company developing specialist sensing equipment might need to establish which laboratories or manufacturers are investigating processes that require a particular type of measurement. Research publications may establish the scientific requirements, while patents and company activity can reveal organisations developing related technologies.

Finding those relationships manually requires technical understanding as well as time. Commercial research teams may need to distinguish a genuine application from a superficial similarity between two scientific terms, then investigate whether the potential customer has the resources and interest to pursue it.

Lupa attempts to combine that work into a searchable environment. Users provide information about their technology or research interests, after which AI agents examine connected information sources and rank relevant markets, companies, laboratories, and potential collaborators.

Results are intended to retain links to the underlying material, allowing users to investigate the evidence supporting a recommendation before deciding whether to approach a business or pursue a research opportunity.

Scientific context shapes the results

The platform’s central information layer, described by the company as Lupa Substrate, connects scientific and industrial information from multiple sources. These include publications, patents, grants, researchers, laboratories, companies, and market developments.

By relating those records to one another, the system aims to make technical information useful beyond the individual database in which it originated. A user investigating an emerging technology can examine related research groups, industrial applications, and companies active in the field without treating each information source as a separate research project.

The company combines its existing information layer with live searching, allowing results to incorporate more recent material where relevant. Its agents then use the connected information to answer questions or identify potential opportunities based on the context provided by the user.

Traceability is important because commercial decisions involving specialised technologies depend on more than plausible explanations. A research paper might establish that a particular method works under laboratory conditions, while a patent could show that an organisation has protected a related approach. Neither finding necessarily proves that a customer is ready to purchase equipment.

Keeping those sources accessible allows business development teams to examine the technical evidence and make their own assessment of commercial fit. The platform can organise and prioritise research, while decisions about investment, customer engagement, and partnerships remain with its users.

Deeptech markets need specialist research

Prismus is targeting industries including photonics, quantum technology, life sciences, advanced engineering, and scientific instrumentation. These markets often involve complex buying decisions because purchasers need equipment or software that meets precise technical requirements.

A research laboratory may choose an instrument based on measurement performance and compatibility with existing systems, whereas an industrial manufacturer must also consider reliability, support, operating costs, and integration into production.

Identifying a technically relevant organisation is therefore an early step in a longer commercial process. The prospective customer may need to evaluate the technology, obtain funding, run a trial, or compare competing approaches before committing to a purchase.

For suppliers, more accurate research could reduce time spent approaching organisations whose technical requirements do not match the product. That benefit depends on the platform’s ability to interpret scientific information correctly and maintain current records as research and markets change.

Research institutions could use similar capabilities to investigate applications for intellectual property or identify collaborators with complementary expertise. University technology transfer teams, for instance, often need to evaluate potential uses for inventions originating in disciplines outside their immediate commercial experience.

As those teams investigate possible applications, information about competing technologies and active research organisations can be as useful as a list of prospective customers. The platform’s combination of scientific and commercial data is intended to support that wider assessment.

Commercial evidence will shape expansion

Lupa’s reported 30 paying customers provide evidence of early market interest, although the company has not published independently audited recurring revenue, customer retention, or comparative productivity results.

The platform’s effectiveness will also depend on how it handles less visible industrial information. Public research publications and patents contain substantial technical detail, but many procurement decisions, manufacturing problems, and early development projects remain confidential.

Even where suitable information exists, scientific terminology can vary between research groups and commercial sectors. Matching a technology to a potential application therefore requires interpretation that may be difficult to automate consistently across unfamiliar disciplines.

Prismus intends to address a broad range of markets with a common information platform, but the effort required to adapt results to different technical fields will influence its operating costs and ability to serve additional customers.

The company is led by chief executive Elisabeth Blyumin, alongside chief technology officer Timen van Gelderen, chief AI officer Gonçalo Hora de Carvalho, and chief revenue officer Peter Kempin. Their backgrounds combine scientific research, software development, AI, and commercial experience.

Venture Kick’s latest milestone provides resources for further development and customer acquisition, following the programme’s earlier financial and advisory support.

Opening Lupa to more individual users could extend its reach beyond organisations purchasing the software through established business development teams. Its commercial growth will depend on whether users can obtain useful, well-supported results without requiring extensive individual configuration or assistance.

The next phase of development will test that repeatability across additional scientific disciplines and industrial markets. Prismus has established an initial customer base; continued adoption will depend on how reliably its software connects technical evidence with opportunities that organisations can investigate and pursue.