CASE STUDY · AI AGENTS
From AI Hype to Cited Evidence
An AI-agent workflow that turns fast-moving claims from videos, posts or discussions into structured, source-linked evidence.
The situation
Technical claims spread faster than teams can properly check them.
New statements appear in videos, posts, newsletters, product announcements and internal discussions. It is often immediately unclear what is actually supported, what has been over-interpreted and which sources are even relevant.
For organisations, this creates a practical problem: they have to assess information quickly without losing the evidence trail.
A normal AI chat answer is not enough for this.
What matters is not just the answer, but which claim was checked, which sources were used and how robust the evidence is.
What we built
Stozn shows how AI agents can turn unstructured statements into verifiable evidence workflows.
One concrete application checks claims that are circulating on social networks.
The system pulls transcript and video context, breaks broad statements into individual claims, searches for matching papers or preprints, and assigns each claim an evidence label — supported, contradicted, mixed or insufficiently evidenced.
Each check is saved as a human-readable knowledge graph that connects creators, claims, sources, papers and authors.
This preserves the evidence behind the assessment so it can later be reviewed, reused or extended.
Business value
The system is relevant for teams that need not just quick answers, but verifiable knowledge work.
Around the topic of AI in particular, there is a lot of hype with exaggerated claims.
Instead of assessing a claim once via chat, a structured evidence record is created: which statement was checked? Which sources support or contradict it? How confident is the assessment?
This reduces repeated research, makes assessments more traceable and creates a better basis for review, internal discussions and later decisions.
- Faster assessment of unstructured statements
- Explicit separation of claim, source and assessment
- Traceable evidence instead of a fleeting chat answer
- A reusable knowledge base for later reviews
- Better support for research, product, strategy and expert teams
- More transparency in AI-supported analysis work
Why this matters
Business AI applications are more than just chat.
Organisations need systems that structure information, preserve sources and make AI-supported work verifiable for people.
The example shows the step from one-off answers to traceable evidence structures: extract the claim, find sources, assess the evidence and store the result as reusable knowledge.
This is decisive precisely for fast-moving technical topics: not every strong claim is wrong, but not every one is sufficiently supported.
Transferable relevance
Stozn works with public video content. The underlying pattern applies more broadly.
Wherever teams need to connect unstructured statements with reliable sources, the same agent approach can be used: extract claims, search for evidence, preserve sources and make results reusable.
- Research intelligence for fast-moving technical topics
- Regulatory and scientific analysis
- Due diligence and investment research
- Media monitoring and disinformation detection
- Product and market analysis
- Expert knowledge management
- Internal AI assistants with a verifiable source base
KEY TAKEAWAY
AI becomes more useful when it does not just generate answers, but preserves claims, sources and evidence in a structured way. That is how fleeting information becomes verifiable knowledge.
NEXT STEP
A similar initiative?
In the free initial call we clarify whether Kernity is the right partner for your situation – and which entry point would make sense. The goal is not to sell a solution on the spot.
30 minutes · video or on-site · no preparation needed