// About
What We Do
Information warfare is the defining threat of this decade. State actors run influence operations across 80+ countries. AI-generated persuasion content is scaling faster than human analysts can review it. Legacy content-moderation systems are optimized for virality metrics, not cognitive defense.
We build the other side of that equation.
Esothel Labs develops detection systems that identify manipulation at the psychological mechanism level - not fact-checking, not sentiment analysis, not keyword blacklists. We detect how content is engineered to exploit cognitive vulnerabilities: manufactured urgency, precision-targeted framing, identity exploitation, belief-cascade engineering, and coordinated inauthentic behavior patterns.
// Current Production Capability
M.I.N.D. - Modeling Influence, Neutralizing Deception
M.I.N.D. v3.0 is our core detection framework, currently deployed and operational.
| Pipeline | 6 weighted analysis layers, each targeting an orthogonal manipulation dimension |
| Modules | 68 detection modules across core directorates (CogArc, Experimental, Sentinel) |
| Detection | Phrase/rule-based heuristics + lightweight ML priors + structural pattern analysis |
| Output | Manipulation Potential Score (MPS) on a 0–1 continuous scale with discrete threat levels |
| Languages | HPL v3.0 (Human Persuasion Language) - custom DSL for formal threat specification |
| Platforms | Python SDK, JavaScript SDK, Telegram bot, Discord bot, browser extension, REST API |
The Six Production Layers
| Layer | Name | What It Detects |
|---|---|---|
| L1 | Pattern Frequency | Unnatural repetition, keyword density anomalies, timing patterns |
| L2 | Emotional Arc | Engineered emotional trajectories, sentiment manipulation sequences |
| L3 | Network Propagation | Coordinated amplification signatures, cascade fingerprints |
| L4 | Choice Architecture | False dichotomies, manufactured scarcity, manipulative framing |
| L5 | Behavioral Loop | Commitment escalation, intermittent reinforcement, habit engineering |
| L6 | Cognitive Load | Information pacing designed to overwhelm critical thinking |
Validation
- Internal benchmark: ~205 samples (165 threat + 40 benign), hand-curated by domain experts
- Results on that set: Precision 1.000, FPR 0.000, zero false positives observed
Telegram & Discord bots · Chrome/Firefox browser extensions · REST + WebSocket API · Python & JavaScript SDKs · Docker single-command deploy
Design Philosophy
Precision over recall. M.I.N.D. is calibrated to never flag legitimate content as manipulative. We accept lower recall - missing some manipulation - as the explicit cost of maintaining operator trust. When M.I.N.D. flags something, you can act on it. That’s the point.
// Research Pipeline
Where We’re Heading
Beyond the production core, Esothel Labs maintains an active research program exploring next-generation detection approaches grounded in computational neuroscience.
Active Inference Research Extensions (v9.0)
We are developing a theoretical framework that maps manipulation detection onto the Free Energy Principle (Friston, 2010) and predictive processing theory. The core insight: manipulative content can be characterized as adversarial perturbation of the brain’s generative models.
- Layer 14 (Active Inference) implemented as a proof-of-concept research layer
- 15 Predictive Noetic Directorate modules targeting specific predictive-processing exploitation mechanisms
- Default weight: 0.0 - Layer 14 does not contribute to production MPS scores
- Detection: phrase-based heuristics only (not deep semantic or variational inference computation)
- Evaluation: internal synthetic benchmark only, deterministic (seed=42), no external validation
- Preliminary internal signals suggest complementary detection on synthetic data (~+1.2 pp recall)
Research Roadmap
- Near-term: External validation on naturalistic corpora. Independent replication.
- Medium-term: Embedding-based semantic detection (beyond phrase matching). Public benchmark contributions.
- Long-term: Physiological validation studies (EEG, HRV, MMN) connecting text-based detection to measurable cognitive effects.
// Publications
Research Output
14 manuscripts in preparation for arXiv submission (with full qualification of metrics and limitations).
Core Framework & Architecture Papers (2)
Six-layer pipeline architecture, internal benchmark results, configurable detection modules.
Scaling & Recall Studies (2)
Preliminary evaluation on large-scale synthetic benchmarks. Recall hardening methodology with auto-tuned HPL rules.
Detection Framework Papers (4)
Epistemic authority detection, collective belief dynamics, adversary adaptation modeling, coordinated influence detection. All theoretical/proof-of-concept.
Active Inference Series (6)
Theoretical frameworks mapping manipulation detection to free energy minimization, predictive coding, collective phase transitions. Proof-of-concept implementations.
// Applications
Who This Is For
- Defense and intelligence: Cognitive domain defense, influence campaign attribution, population resilience assessment
- Government and policy: Information environment monitoring, election integrity, counter-disinformation infrastructure
- Enterprise security: Employee cognitive security training, executive protection, supply-chain information integrity
- Research institutions: Academic collaboration on active inference, predictive processing, physiological validation
- Venture studios and investors: Due diligence on cognitive security as an emerging category
Contact
Serious inquiries from government, defense, enterprise, and research partners.
General: contact@esothel.com · Research: research@esothel.com · Security: security@esothel.com
We respond to substantive inquiries within 48 hours. No pitch decks on request - we demo live capability.
Get in Touch