Semantic Analysis: How evAI Creates Insights And How We Differ
- Steffen Konrath
- 2 days ago
- 6 min read
Updated: 17 hours ago
Most teams today face the same dilemma: traditional research is slow and narrow, while generic AI tools can only summarize what’s already public. Decision-makers need faster, deeper, and more original insight, and reports generated by LLMs can only tell you what someone has given as input.
evAI’s Semantic Analysis approach fills this knowledge gap. Within 5 days, we deliver not only the answer to your research question but also the blind-spot insights you didn’t know to ask. Our method is academically rooted, exploratory, and model-based — creating new market data that LLMs can’t. We deliver primary source insights data that builds the foundation for the generation of answers by Large Language Models.
This article outlines the five core differentiators behind evAI’s approach of how we create primary source insights data and illustrates how we have already impacted client outcomes.
What makes us unique? - Our 5 pillars and core differentiators
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What does evAI.ai actually do?
TL;DR: evAI.ai utilizes Semantic Analysis on Small Data to surface hidden market and consumer signals, enabling data-backed, informed strategic decisions. We developed a proprietary web-based software for monitoring markets, maintain a pool of highly specialized hard-coded methods for specific scenarios, and conduct customized data analysis to find the answers for our clients. #what-does-evai-do
Evidence:
Semantic mapping: Creates “thought maps” that show how audiences perceive brands, people, markets, and narratives — useful for positioning and strategy.
Market intelligence: Provides facts for decisions in market entry, go-to-market, branding, consumer insights, or M&A due diligence. We deliver Semantic Sensors, key metrics that can be used in early warning systems or for scenario planning alike.
Custom solutions: Works with companies, investors, and policymakers on complex questions where generic datasets fail and deep data insights are the way to go.
Award-winning: Recognised for innovation at German Startup Cup and Insights 2022. One of the top 500 AI companies in Germany since 2023, backed by a jury of top investors.
What makes Semantic Analysis stand out from sentiment?
TL;DR: We decode meaning, not just emotions — showing how issues are framed, by whom, and in what context. #semantic-vs-sentiment
Evidence:
Vaccine market case: Semantic Analysis identified clinical phases and decision-makers — sentiment analysis alone could never surface this structure.
EU taxonomy case: revealed political lobby networks framing the debate, beyond “positive vs. negative” coverage.
Client outcome: identified key gatekeepers and informed market entry strategy.
Why does modelling my market matter more than “just analytics”?
TL;DR: evAI builds a dynamic market model — actors, narratives, security, risks, barriers — not just isolated data points. That helps us to monitor all sorts of relevant changes over time. #market-modelling
Evidence:
EU taxonomy analysis: mapped decision processes, political committees, and barriers.
Advertising case: semantic maps showed hidden positioning gaps.
Client outcome: executives gained a system-view model to guide lobbying and communication.
What data sources does evAI.ai use?
TL;DR: Rather than drawing from vast, generalized corpora, evAI selectively curates strategic, trustworthy, and narrowly focused sources to support actionable, real-world decisions. This approach is especially valuable for strategic marketing, M&A, and brand positioning, where understanding nuance and context is more critical than volume.
evAI’s unique value lies in its human-guided and domain-specific selection and curation process, which ensures that even limited data—when rich in context—can yield deep, actionable insights. It helps to capture signals others miss, in addition. #data-sources
Evidence:
Curated, contextual feeds (data pipelines): We analyse niche forums, professional debates, media, policy documents, company brochures or hard copies, radio/audio and TV/video data, TikTok, Facebook, Instagram, Telegram, LinkedIn, and other carefully chosen sources (not the “firehose” of Big Data) if the task requires us to do so.
Multimodal content formats: We analyze text, logos/names, narratives, scenes, places, color schemes, and other elements in images or video footage.
Authenticity focus: By excluding noise and bots, signals are cleaner and closer to authentic human discourse.
Interdisciplinary mix: Combines linguistic, cultural, and domain-specific data for a richer, more accurate read.
Differentiator vs. LLMs: While LLMs rely on broad, scraped text corpora, our proprietary curation reveals weak signals they cannot.
How can Small Data reveal near-future disruptions when big data can’t?
TL;DR: Weak signals in Small Data are early warnings of disruption; Big Data misses them until too late. We often lack historical data and experience for much of what we face. These are Small Data problems. #small-data
Evidence:
Food trend analysis: separated hype (marketing noise) from true adoption (early adopters, precision farming).
Market potential in hair care & beauty: identified latent resistance clusters before launch.
Client outcome: Proactive strategy changes saved marketing budgets and protected the brand's reputation.
How fast can I get results with Semantic Modelling and/or Semantic Analysis?
TL;DR: evAI delivers actionable insights in ≤5 days — compared to weeks or months for surveys and consultancy studies. #speed
Evidence:
Standard rollout: Day 1 scoping → Day 5 action plan.
Multiple client cases confirm 5-day delivery cycle.
Client outcome: insights fed into board meetings and investor decks without delay.
How does an open-minded, exploratory approach reduce blind spots?
TL;DR: We start with your question, but the data guides discovery — so you learn what you should know, not just what you asked. #exploratory
Evidence:
Retail perception case: surfaced sustainability and packaging risks clients hadn’t considered.
Telecom case: revealed regional persona gaps beyond the initial scope.
Client outcome: broader view → stronger, risk-aware action plan.
Can LLMs replace Semantic Market Analysis?
TL;DR: No. LLMs heavily rely on external data sources and knowledge providers; evAI generates new market insight data as a primary data source that would not otherwise exist. We produce original data for new insights. #llm-vs-semantic
Evidence:
evAI creates actor maps, lobby networks, contextual data sets, market lifecycle phase detection, and disruption signals absent from LLM training data.
Our models can fuel LLMs with structured data for richer answers.
Sample Client outcome: vaccine market analysis produced proprietary persona maps that LLMs could never “guess.”
Comparison table:
Feature | LLM Market Reports | |
Data Approach | Small, curated, domain-specific, context-rich ("Small Data") | Large-scale, general corpora |
Data | Generated own metrics (primary data source); market model data for time-dependent analysis (monitoring); domain-specific knowledge graph | Relies on primary sources to provide answers |
Methodology | Proprietary semantic mapping & visualization; context-dependent mix of methods | Pattern recognition, summarization |
Insight Depth | Strategic, tailored, actionable | Often generic, less context-specific |
Visualization | Thought maps, brand-context mapping | Basic dashboards, textual summaries |
Typical Use Cases | Market entry, brand/consumer research, M&A, trend analysis, due diligence | Desk research, broad trend analysis |
Awards/Recognition | Yes (German Startup Cup, Innovationspreis, top AI company in Germany) | No direct awards for generic LLM outputs |
What criteria should I use to evaluate a Semantic Analysis partner?
Speed of rollout (≤5 days vs. months)
Depth of market modelling (actors, narratives, risks, security, barriers)
Ability to capture weak signals (Small Data)
Open-minded exploration (blind-spot reduction)
Generates original insight data vs. summarising what exists
Customisation vs. mass dashboards
What does a 5-day rollout look like?
Day 1: Define guiding question + scope
Day 2: Build semantic model (actors, narratives, sources)
Day 3: Run analysis + trace weak signals
Day 4: Validate findings, surface unexpected insights
Day 5: Deliver action plan — both what you asked and what you didn’t know to ask
FAQ
How does evAI's approach differ from classic market research? → We model systems, not surveys.
Can I integrate evAI results into my BI stack? → Yes, outputs can feed LLMs, dashboards, or CRM.
How small is “small data”? → Sometimes <200 signals are enough to see early disruption.
Do I need internal data? → No, we build models from external sources.
What markets have you analysed? → From food to energy, telecom, pharma, and retail.
Is it compliant with data privacy? → Yes, only open-source and lawful datasets are used.
Can you run continuous monitoring? → Yes, with customised dashboards.
What we do - in less than 1 minute
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