Omer AI
Next-generation health intelligence that reasons, not guesses.
Built on medical knowledge graphs with deterministic inference paths. Zero hallucinations. Full transparency. Personalized to you.
GraphRAG Knowledge Architecture
S2Y integrates the Cognee perception framework with advanced GraphRAG (Graph-enhanced Retrieval Augmented Generation) architecture—not traditional Vector RAG.
Graph
// Inference Path
Spike Protein --[causes]-->
Endothelial Damage --[triggers]-->
Microclots
Structured Medical Knowledge
Omer AI constructs a massive Medical Knowledge Graph that structures Long COVID pathological mechanisms into entities and relationships. When AI answers questions, it traverses this deterministic logic network—not performing token prediction.
Spike Protein Research
World's largest monolithic spike protein & Long COVID paper collection
Global Vaccine Batches
Vaccine batch distribution datasets from major countries worldwide
CDC VAERS Database
U.S. Centers for Disease Control VAERS adverse event reports
Clinical Protocols
S2Y proprietary clinical protocols and treatment guidelines
Determinism
AI recommendations strictly follow medical guidelines and S2Y clinical protocols. Every answer traces back to verified knowledge nodes.
Explainability
"I recommend increasing taVNS duration because your HRV data shows sustained sympathetic activation, and the knowledge graph indicates 30+ minute sessions more effectively activate the CAP pathway."
Personalization
Personal Health Knowledge Graph (PHKG) remembers your drug allergies, genetic mutations, and lifestyle patterns for true functional medicine-level care.
Your health, encoded as a living graph
Omer AI doesn't just remember what you tell it — it builds a structured, queryable model of your health history that deepens with every interaction.
PHKG
Why a knowledge graph beats a profile
Traditional health apps store data in tables. Omer AI stores it as a semantic graph — meaning it understands that your HRV dip follows your symptom flare, not just that both numbers exist. Relationships matter.
Contextual memory across sessions
Omer remembers that last month your fatigue worsened after reducing tVNS frequency — and factors that into today's recommendations.
Cross-domain inference
Connects your microclot risk markers with your HRV trends and sleep data, drawing conclusions that require data from multiple nodes simultaneously.
GDPR-native right to explanation
Every node in your PHKG is yours. You can inspect, export, or delete any part of your graph at any time.
Intelligent Reasoning Pipeline
A systematic, evidence-driven approach to health intelligence
Query Understanding
Natural language processing parses your health concerns with medical context awareness.
Knowledge Graph Traversal
AI navigates the medical knowledge graph, following verified pathways between entities.
Contextual Synthesis
Retrieved knowledge is synthesized with your personal health profile (PHKG).
Deterministic Response
Generate evidence-based insights with full reasoning path transparency.
A graph that gets smarter over time
Powered by the Memify post-processing pipeline, Omer AI's knowledge graph continuously evolves — pruning stale information, reinforcing patterns that matter, and deriving new insights from existing relationships.
Every device reading, symptom report, and conversation becomes a structured node in your health graph via LLM-driven entity and relationship extraction.
- tVNS session data
- HealthKit & Google Fit
- Symptom severity scores
The graph post-processes itself: edges are reweighted by usage frequency, stale nodes are pruned, and new facts are derived from existing multi-hop relationships.
- Edge reweighting by frequency
- Stale node pruning
- Derived fact generation
Recommendations shift as your health picture changes. A protocol that worked at month 1 gets updated when your HRV baseline improves — automatically, without you asking.
- Protocol re-evaluation
- Dosage drift detection
- Progress milestone recognition
GraphRAG vs 60% for standard RAG — because multi-hop graph reasoning eliminates the 40% failure rate of flat vector retrieval for synthesis queries.
What you can ask Omer AI
Questions that require connecting your device data, research literature, and personal history — impossible for general LLMs, native for GraphRAG.
My HRV has dropped 18% over the past 3 weeks despite consistent tVNS sessions. What could explain this?
Your PHKG shows a symptom log entry 22 days ago noting increased post-exertional malaise (PEM). The knowledge graph links PEM → sympathetic overdrive → HRV suppression, which persists even with vagal stimulation if session timing doesn't account for recovery windows. Recommendation: shift sessions to morning (within 2 hours of waking) to align with circadian vagal tone peak.
- HRV decline node
- → PEM log (22 days ago)
- → Sympathetic overdrive pattern
- → CAP pathway inhibition
- → Timing optimization protocol
I have an MTHFR C677T mutation. How does this affect my recovery strategy?
Yes. MTHFR C677T reduces methylfolate production, impairing glutathione synthesis. The knowledge graph links this to reduced antioxidant capacity, which means oxidative stress from viral persistence is amplified. Your Apple Health data shows sleep quality correlates strongly with oxidative stress markers. Additionally, your D-dimer reading from last month (1.8 μg/mL) suggests active microclotting, so prioritize reducing fibrinogen load concurrently.
- MTHFR C677T node
- → Glutathione deficiency pathway
- → Oxidative stress amplification
- → Sleep quality correlation (HealthKit)
- → D-dimer lab result cross-reference
Should I increase my tVNS session duration this week?
Based on your last 7 days: HRV baseline is 12% above your 90-day average (positive signal), sleep quality scores are stable, and you reported no PEM episodes. The knowledge graph indicates these are the three precondition nodes for safe duration extension. Recommend increasing from 25 to 35 minutes. Re-assess in 5 days.
- HRV 7-day trend
- → Sleep quality log
- → PEM absence confirmation
- → 3-node precondition check
- → Duration extension protocol
A New Standard in Health AI
GraphRAG vs Traditional LLM Architecture
GraphRAG eliminates speculative responses
Shows complete inference chain
Curated medical ontology
Individual health profiles
Right to explanation built-in
Devices feed the graph.
The graph refines the devices.
S2Y devices and the S2Y App aren't just therapeutic tools — they're continuous data sources that make Omer AI's recommendations more precise over time.
Common questions
How is Omer AI different from asking ChatGPT or Claude a health question?
General LLMs predict tokens — they generate plausible-sounding text that can hallucinate medical facts. Omer AI traverses a curated medical knowledge graph, so every answer traces back to a specific, verifiable node. You can see exactly which data relationships produced each recommendation. ChatGPT cannot do this.
What data sources does the medical knowledge graph include?
The graph is built from the world's largest monolithic spike protein and Long COVID paper collection, global vaccine batch datasets, CDC VAERS adverse event reports, and S2Y proprietary clinical protocols. All sources are version-controlled and traceable.
Is my Personal Health Knowledge Graph (PHKG) shared with anyone?
Never. Your PHKG is cryptographically isolated from all other users. S2Y cannot access it without your explicit permission. Under GDPR, you have the right to view, export, or permanently delete every node in your graph at any time.
Does Omer AI replace my doctor?
No. Omer AI is a health intelligence tool — it helps you understand your data, prepare for clinical appointments, and make informed decisions. It is not a substitute for medical diagnosis or treatment. All recommendations should be reviewed with your healthcare provider.
How does the self-improving memory work in practice?
Via the Memify pipeline: after each interaction, the graph post-processes itself. Edges between nodes you frequently query are strengthened. Stale data (symptoms you haven't reported in months) is down-weighted. New relationships are derived from existing multi-hop paths — e.g., if Omer notices your tVNS sessions consistently precede HRV improvement by 48 hours, it creates a new timed relationship node.
Can Omer AI use data from my Apple Health, Garmin, or other wearables?
The S2Y App collects health data from Apple HealthKit and Google Fit, including HRV, sleep, steps, and activity metrics. Combined with S2Y device data (tVNS, RTM), this data flows directly into the knowledge graph for comprehensive health analysis.
What does "92.5% accuracy" mean in the comparison?
This refers to GraphRAG's performance on synthesis queries — questions requiring data from multiple sources to answer — compared to 60% for standard vector RAG. Standard RAG fails ~40% of the time on these queries because it retrieves isolated chunks without understanding relationships between them.
Is the system available in languages other than English?
Omer AI currently operates primarily in English, with the knowledge graph structured in English medical ontology. S2Y is expanding multi-language support in 2026, with Japanese and Spanish prioritized first based on user demand.
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