The only platform that computes meaning through vector algebra — not classification, not search, but mathematical operations on semantics itself. Instant results. Zero pre-training. No human labeling. Political speeches, customer calls, news feeds — we calculate what others can't see.
While every other platform searches for similar texts, Cognivec performs algebraic operations on meaning itself — extracting insights that don't exist in any database.
The principle: In vector space, relationships between concepts are geometric. "King − Man + Woman = Queen" isn't a trick — it's algebra. We apply the same principle to extract fear, manipulation, sincerity, and cognitive load from any text. No training data needed. No labeled examples. No domain adaptation. Pure mathematics on meaning — and this technology can generate labeling for other ML systems that is richer, more nuanced, and more accurate than any human annotation team could produce.
Results in under 2 seconds. Not minutes. Not hours. Real-time analysis of any text, any length. Process thousands of documents while your competitors read the first paragraph.
No labeled datasets. No domain adaptation. No weeks of model fine-tuning. Upload your text right now — get results right now. Works on any language, any domain, any topic.
Our technology produces annotations richer than any human team. Label unlimited concepts simultaneously — emotions, intentions, cognitive states, manipulation patterns, topic shifts — with precision impossible for manual work.
Every day, critical insights hide in plain sight within mountains of unstructured text.
You read 200 pages of reports to find the one line that changes everything. Hours pass. And you're still not sure you didn't miss something crucial.
Your team makes 50 calls a day. You listen to 3 of them. The other 47? A black box. Customers churn, and you find out from CRM reports.
You watch financial terminals, ignoring the rest of the world. But the signal for the next currency shift wasn't in the bank report—it was hidden in yesterday's sports headlines. You're missing the semantic ripples.
You watch a 2-hour interview. Take notes. Debate with colleagues about "what he really meant." Everyone has a different interpretation. Zero objectivity.
Candidates know exactly how to game standard personality tests. You're hiring based on rehearsed masks. The true psychological profile—and the red flags—are hidden in their free-form writing, not their checkboxes.
You read 500 scripts a year relying on "gut feeling." You don't know if the dialogue actually builds tension or just fills space until the test screening—when it's too expensive to fix.
Cognivec performs algebraic operations on semantic vectors — calculating meanings that don't exist in any training set.
We calculate the "velocity," "acceleration," and "jerk" of meaning change. When a speaker shifts between modes — persuasion, justification, fact-stating — it creates a measurable trajectory in vector space. We track it.
Just as "King − Man + Woman = Queen" computes a new meaning, we subtract baseline patterns and add reference anchors to reveal fear, confidence, manipulation, sincerity — states that were never labeled, only computed.
The output isn't classification — it's a map of computed distances: how far is this fragment from "calm baseline"? How close to "constructed narrative"? Continuous values, not binary labels.
From boardrooms to trading floors, from film studios to security operations.
An analytics center spent 2 weeks dissecting a single public speech. 5 experts, 5 different interpretations. Clients demanded objectivity.
Uploaded a 2-hour interview transcript. Generated a map of 31 narratives with metrics across multiple axes — analysis completed in under 3 minutes, no pre-training required:
"We got in 4 hours what previously required a month of focus group work. And it's reproducible — same input, same output, every time."
HR departments and security services need reliable psychological assessments but traditional tests are gameable. People know the "right" answers to standard questionnaires.
Analyze any freely composed text — essays, emails, social media posts, creative writing. Our algorithm detects dozens of psychological dimensions instantly, without pre-training:
"Unlike checkbox questionnaires, you can't game free-form writing. The patterns are unconscious and consistent — and the system finds them in seconds."
A car rental support call goes sideways. Customer needs a vehicle, operator tries to help. But what exactly went wrong? Manual review catches symptoms, not causes.
Cognivec extracted 595 semantic tags from a single call transcript — instantly, with zero pre-training. Each fragment scored across 10 CX dimensions:
Critical moment detected: "Wait a minute, I thought you said none of the Concord locations are open?" — Customer confusion peaks when operator mixes Antioch, Concord, Crow Canyon, San Ramon locations without structure.
Pattern identified: Operator creates time pressure ("closes at 12 PM") without confirming timezone first. Customer asks to slow down — operator continues same pace.
Result: Call ends without resolution. Customer has no car. Overall CX balance: −1.28 (negative).
The insight: Traditional QA would rate this call as "operator tried hard." Cognivec shows the reality: confusion score (−2.96) is 2x worse than any other metric. The operator's effort is real — the chaos is destroying it.
Financial markets don't exist in a vacuum. Societal mood—influenced by sports results, celebrity culture, and general economic news—creates "semantic ripples." Standard analysis ignores 90% of this context, focusing only on financial reports.
We analyzed a continuous stream of 15 random articles per hour over 3 years. The topics were mixed: sports, economy, social life, and entertainment. The goal: find hidden semantic markers in any text that precede currency shifts.
"We proved that a sports championship result or a major social event impacts the EUR/USD pair 72 hours later. The signal is there—it's just slower to manifest."
Studios evaluate hundreds of scripts annually. Predicting audience engagement is guesswork. Post-production, understanding what works and what doesn't requires expensive focus groups.
Comprehensive analysis at every stage. For finished films, the visual stream is first converted into detailed textual descriptions, allowing our engine to analyze the full narrative experience:
"We can now analyze not just what characters say, but how the visual narrative supports or contradicts the dialogue — by converting the visual track into text, we apply semantic algebra to the movie itself."
Security teams monitor thousands of hours of footage. Human attention degrades after 20 minutes. Traditional motion detection generates excessive false positives.
Security footage converted to structured textual descriptions, then analyzed — instant processing, no location-specific training:
"Converting visuals to analyzable text let us apply the same powerful semantic tools to video. No training on our specific footage — it just works."
PE fund before a $200M deal. Target: tech company. Everything looks great on slides. But something's off in the earnings calls.
Analyzed 8 quarterly CEO calls over 2 years — complete analysis in hours, not weeks:
"This doesn't replace due diligence. It tells you where to dig — and does it instantly across years of transcripts."
We don't classify text. We compute new meanings through mathematical operations on semantic vectors. No training required. No labeled data. Instant results.
Every word and phrase becomes a point in 768-dimensional space. Relationships between concepts are encoded geometrically — distance means similarity, direction means relationship type. Works instantly on any text.
"King − Man + Woman = Queen" isn't a party trick — it's proof that meaning can be computed. We apply the same algebra to extract fear, confidence, manipulation, and sincerity from any text. No examples needed.
Meaning is a trajectory. We calculate velocity (how fast topics shift), acceleration (where the speaker intensifies or retreats), and jerk (sudden mode switches) — the physics of speech. Real-time computation.
Reference vectors for psychological states (stress, deception, confidence) act as "tuning forks." We measure how strongly each text fragment resonates with each pattern — no training required, unlimited concepts trackable simultaneously.
Don't see your system? Our API connects anything in 1 day.
For teams getting started with semantic analysis
For growing organizations with complex needs
For organizations requiring maximum flexibility
1 document = up to 10,000 words. Annual billing: 20% off.
Sentiment tools classify text into categories (positive/negative) using trained models. We don't classify — we compute. Using vector algebra on meaning, we can extract psychological states, detect manipulation patterns, and measure cognitive load without any training data. It's like the difference between looking up words in a dictionary vs. solving equations.
In semantic vector space, concepts have geometric relationships. The famous example: "King − Man + Woman = Queen" shows that meaning can be computed through math. We use similar operations to extract complex psychological patterns — subtracting baseline speech patterns, adding reference states, measuring distances to emotional anchors.
Yes — it's a conceptual lie detector. We measure the level of fabrication in facts: invented narratives register as "constructed" while factual statements register as "grounded." The system quantifies how much a statement deviates from reality anchors — fabricated content produces measurable semantic tension, while authentic facts maintain baseline consistency. This isn't about catching liars — it's about measuring the distance between narrative and reality.
Accuracy depends on the task. For binary classification (positive/negative): 85-92% on labeled datasets. For complex tasks (intentions, manipulation): we provide probabilities and confidence scores, not verdicts.
Full support: English, Russian, Chinese, Spanish, German, French. Beta: 6 additional languages. Enterprise: we'll train a model for your language.
API: 1 day. CRM/telephony integration: 1-2 weeks. Enterprise with customization: 1-2 months.
Yes, through our video-to-text pipeline. We convert visual content to structured textual descriptions, then apply our full semantic analysis suite. This works for films, security footage, interviews, and any recorded content.
"We tested 4 tools for earnings call analysis. Cognivec was the only one that gave actionable insights, not just a word cloud. And the speed — results in seconds, not hours."
"The fact that it requires zero training data blew my mind. We uploaded transcripts and got deep psychological insights immediately. No labeling, no fine-tuning, just results."
"For political analysis, this is a game changer. Not a replacement for experts, but an amplifier. I see patterns I used to feel intuitively but couldn't prove — and the labeling it produces is richer than anything my team could do manually."
Book a demo — we'll show you the system on your data. Free. No obligations. 30 minutes.