Copenhagen-based AI SaaS
company Kime has raised €2 million in a pre-seed funding round led by PSV Tech,
with participation from Nordic Makers and a group of angel investors based in
Copenhagen and Stockholm.
As user behaviour shifts
away from traditional search engines toward AI assistants that provide a
limited number of direct answers, brand visibility is increasingly shaped by
how large language models select and prioritise information. Recent research from McKinsey
indicates that AI-based search is becoming an important entry point to online
decision-making and is expected to influence a substantial share of consumer
spending.
Despite this change,
many companies have limited insight into whether their brands are mentioned at
all when users ask AI assistants for recommendations, comparisons, or
purchasing advice.
Kime aims to address
this gap by tracking how brands are represented across major AI platforms,
including which brands are cited, their relative positioning, associated
sentiment, and the sources used to generate responses. The company refers to
this emerging discipline as generative engine optimisation (GEO), reflecting a
shift from ranking in traditional search results toward visibility within
AI-generated answers.
According to founder and
CEO Vasilij Brandt, marketers are increasingly focused on understanding how
their brands appear in AI assistants, and Kime’s objective is to make that
visibility measurable and actionable.
Since launching, the
company has seen early commercial traction. In November, Kime conducted more
than 60 product demonstrations, primarily with senior marketing leaders and
executive teams. It has also expanded its product to include an agency-focused
module that enables agencies to analyse AI visibility across their client
portfolios. The platform is currently used by brands across multiple industries
and is also deployed through agency partnerships.
In its current phase,
Kime is focused on analytics, offering dashboards that show brand visibility
across prompts and AI platforms, competitor benchmarking, sentiment, share of
voice, and the domains and publications informing AI responses.
Looking ahead, the
company plans to develop the platform into a broader layer for AI marketing,
giving teams a centralised way to manage and optimise their presence across
multiple AI assistants as the LLM landscape continues to fragment.