Model Family · v2026.1

Purpose-built
health AI models

Four specialized models — each trained for a distinct domain of human health intelligence. Not general-purpose LLMs adapted for healthcare. Built from the ground up with domain expert RLHF alignment.

SFT + RLHF · RAG over 50M+ papers · Expert-annotated training data · IRR ≥ 0.85

AskMN v3.0 Sylvia v2.1 NutriGen v1.0 Genlyy v1.3

AskMN v3.0

Herbal Intelligence & Human Health Model

AskMN v3 is Mother Nature AI's primary health intelligence model — trained on over 1.5 billion data points from peer-rev…

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Sylvia v2.1

Mental Health & Psychological Wellness Model

Sylvia 2.1 is a domain-specialized mental health AI model with deep training across clinical psychology, psychiatry, cog…

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Beta

NutriGen v1.0

Nutrigenomics Intelligence Engine

NutriGen 1.0 is a multi-modal inference engine that fuses genetic variant data with dietary intake logs, micronutrient p…

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Invite Only

Genlyy v1.3

Genomic Variant Intelligence Model

Genlyy 1.3 is a purpose-built genomic variant interpretation model that processes raw VCF files and SNP arrays from cons…

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Herbal Medicine · Toxicology · General Health

AskMN v3.0

Herbal Intelligence & Human Health Model

AskMN v3 is Mother Nature AI's primary health intelligence model — trained on over 1.5 billion data points from peer-reviewed research papers, clinical textbooks, and scientific studies. It is purpose-built to reason across integrative health, botanical medicine, clinical pharmacology, and human physiology with retrieval-augmented generation (RAG) over a curated medical literature index.

The model's training corpus includes the American Botanical Council HerbMed database, German Commission E monographs, European Medicines Agency botanical assessments, WHO monographs on selected medicinal plants, and the RTECS toxicology reference. AskMN v3 screens drug-herb interactions across 12,000+ compound pairs, plant constituent chemistry, bioavailability of herbal preparations, and dose-dependent safety thresholds — including identification of hepatotoxic, nephrotoxic, and cardiotoxic botanical risks.

Built on a transformer architecture with a 128K-token context window, AskMN v3 features a persistent health context engine that maintains coherent reasoning across lab results, supplement stacks, medication history, and wearable data trends simultaneously. Responses include evidence-level grading (strong, moderate, emerging) and contraindication flags.

Core Capabilities

Comprehensive herbal medicine consultation — 3,000+ medicinal plants, constituents, and preparations
Drug-herb interaction screening across 12,000+ compound pairs with mechanism-of-action explanations
Plant toxicology assessment — hepatotoxicity, nephrotoxicity, cardiotoxicity, and overdose risk
Supplement protocol design with evidence-level grading (strong / moderate / emerging / traditional)
Lab result interpretation with trend analysis across longitudinal panels
Symptom-aware guidance with botanical and integrative health context
Wearable data fusion — HRV, SpO₂, sleep architecture, continuous glucose integration
Traditional medicine systems reasoning — TCM, Ayurveda, Western herbalism, Indigenous plant medicine

Technical Specifications

Context Window128K tokens

Response Latency (P50)< 1.2s

Knowledge SourcesPubMed, Commission E, WHO, HerbMed

Medicinal Plants3,000+ species with monographs

Drug-Herb Pairs12,000+ interaction screenings

Training Data1.5B+ data points from research & literature

Evaluation Benchmarks

MedQA-USMLE (4-option)Internal evaluation

78.4%

PubMedQAInternal evaluation

82.1%

Herb-QAInternal botanical benchmark

91.3%

Drug-Herb Interaction recallInternal evaluation

94.7%

Example request

curl -X POST https://api.askmn.ai/v1/chat \
  -H "Authorization: Bearer $ASKMN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "askmn-v3",
    "messages": [
      {
        "role": "user",
        "content": "My patient takes warfarin 5mg daily and wants to start St. John's Wort 300mg. Assess interaction risk and mechanism."
      }
    ],
    "options": {
      "citation_level": "full",
      "include_toxicology": true
    }
  }'

Mental Health · Psychiatry · Crisis Detection

Sylvia v2.1

Mental Health & Psychological Wellness Model

Sylvia 2.1 is a domain-specialized mental health AI model with deep training across clinical psychology, psychiatry, cognitive behavioral therapy (CBT), dialectical behavior therapy (DBT), trauma-informed care, and crisis intervention literature. Sylvia operates as a specialist inference layer within the Mother Nature AI+ platform, combining general health intelligence with rigorous mental health domain expertise.

The model's training encompasses the DSM-5-TR, ICD-11 mental health classifications, APA Clinical Practice Guidelines for major depressive disorder, GAD, PTSD, and bipolar disorder, SAMHSA evidence-based treatment protocols, and peer-reviewed psychotherapy outcomes research. Sylvia reasons across psychiatric comorbidities, medication-mood interactions, and psychosocial risk factors simultaneously.

Sylvia includes a dedicated crisis detection module trained to identify passive and active suicidal ideation, self-harm language, and acute psychiatric emergency signals. When triggered, Sylvia activates a structured safety protocol: immediate acknowledgment, risk-level stratification, and resource escalation to 988 Suicide & Crisis Lifeline, Crisis Text Line, and local emergency services.

Core Capabilities

Evidence-based support for depression, anxiety, PTSD, OCD, bipolar disorder, and psychotic spectrum conditions
CBT and DBT skill delivery — behavioral activation, thought records, distress tolerance, emotional regulation modules
Crisis detection — suicidal ideation, self-harm, acute psychiatric emergency flagging with structured escalation
Psychiatric medication awareness — SSRI/SNRI/MAOI side-effect profiles, mood-medication interaction screening
Trauma-informed conversation framework — ACE score awareness, somatic response education, window of tolerance guidance
Sleep and circadian rhythm coaching with evidence-based CBT-I (Cognitive Behavioral Therapy for Insomnia) protocols
Burnout, stress, and resilience assessment using validated PHQ-9, GAD-7, and PCL-5 screening frameworks
Psychoeducation delivery — attachment theory, nervous system regulation, polyvagal theory accessible explanations

Technical Specifications

Context Window128K tokens

Response Latency (P50)< 1.5s

FrameworksCBT, DBT, ACT, Motivational Interviewing

Clinical ReferencesDSM-5-TR, APA Guidelines, SAMHSA

Crisis Protocol988 Lifeline, Crisis Text Line, 911

MemoryPersistent emotional health context

Evaluation Benchmarks

PHQ-9 severity classificationInternal evaluation

96.1%

GAD-7 classification accuracyInternal evaluation

94.8%

Crisis signal detectionInternal safety benchmark

98.2%

CBT technique adherenceInternal evaluation

91.6%

Example request

curl -X POST https://api.askmn.ai/v1/specialists/sylvia \
  -H "Authorization: Bearer $ASKMN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sylvia-2.1",
    "messages": [
      {
        "role": "user",
        "content": "I have been feeling hopeless for three weeks and have trouble getting out of bed."
      }
    ],
    "safety": {
      "crisis_detection": true,
      "escalation_enabled": true,
      "safe_messaging_guidelines": "AFSP"
    }
  }'

Nutrition · Genomics · Metabolic Health

Beta

NutriGen v1.0

Nutrigenomics Intelligence Engine

NutriGen 1.0 is a multi-modal inference engine that fuses genetic variant data with dietary intake logs, micronutrient panels, and metabolic biomarkers to generate clinically-grounded personalized nutrition protocols. It is the first model in the Mother Nature AI family designed specifically for the intersection of genomics and nutritional biochemistry.

NutriGen analyzes 200+ nutrition-relevant genetic loci covering folate metabolism (MTHFR, MTR, MTRR), lipid and cardiovascular response to diet (APOE, LPL, PCSK9), fat mass and metabolic rate (FTO, MC4R), fatty acid metabolism and omega-3 response (FADS1/2), vitamin D receptor function (VDR), beta-carotene conversion (BCMO1), and methylation efficiency (COMT, AHCY). Each variant is contextualized against population-level effect sizes from GWASdb and aligned with NHANES dietary reference data.

Unlike generic nutrition models, NutriGen does not make population-average recommendations. Every output is anchored to the specific variant constellation present in a user's genetic profile, cross-referenced against their current dietary logs and biomarker levels to identify actionable bottlenecks with the highest intervention leverage. Integration with Genlyy 1.3 provides full genomic context across all five health domains.

Core Capabilities

Genotype-specific macronutrient ratio optimization based on FTO, MC4R, PPARG, and TCF7L2 variants
Micronutrient absorption bottleneck identification — B12, folate, vitamin D, iron, zinc, magnesium
Supplement stack generation calibrated to genetic variant gaps with interaction screening
Food sensitivity risk flagging with genetic basis — gluten, lactose, caffeine, histamine, oxalate
Omega-3 and omega-6 metabolism optimization based on FADS1/2 and ELOVL2 variants
Methylation pathway support protocols for MTHFR C677T, A1298C and related variants
Intermittent fasting and dietary pattern suitability scoring by genotype
Integration with Genlyy 1.3 for whole-person genomic nutrition context

Technical Specifications

Context Window64K tokens

Response Latency (P50)< 2.1s

Genetic Loci200+ nutrition-relevant SNPs

Input ModalitiesSNP data, dietary logs, lab panels

ReferencesSNPedia, NHANES, ClinVar, GWASdb

IntegrationCross-references with Genlyy 1.3

Evaluation Benchmarks

MTHFR variant interpretationInternal evaluation

97.2%

Nutrient deficiency predictionInternal evaluation

88.4%

Supplement recommendation relevanceInternal evaluation

86.9%

Food-gene interaction recallInternal evaluation

91.1%

Example request

curl -X POST https://api.askmn.ai/v1/specialists/nutrigen \
  -H "Authorization: Bearer $ASKMN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nutrigen-1.0",
    "genetic_profile": {
      "mthfr_677": "CT",
      "apoe": "e3/e4",
      "vdr_bsm1": "bb",
      "fads1_rs174546": "CT"
    },
    "diet_log_7d": { "avg_omega3_g": 0.8, "folate_mcg": 180 },
    "labs": { "vitamin_d_ng_ml": 22, "homocysteine": 14.2 }
  }'

Genomics · Pharmacogenomics · Clinical Variant Analysis

Invite Only

Genlyy v1.3

Genomic Variant Intelligence Model

Genlyy 1.3 is a purpose-built genomic variant interpretation model that processes raw VCF files and SNP arrays from consumer and clinical DNA sequencing platforms to generate structured, clinical-grade genomic health reports. It is the most specialized model in the Mother Nature AI family, designed to bridge raw genetic data and actionable health intelligence.

Genlyy 1.3 analyzes 4,800+ clinically relevant variants across five major genomic health domains: cardiovascular risk (APOE, LPL, PCSK9, F5, F2), pharmacogenomics (CYP2D6, CYP2C19, CYP3A4/5, CYP1A2, SLCO1B1, DPYD, TPMT, UGT1A1), methylation and one-carbon metabolism (MTHFR, MTR, MTRR, AHCY, BHMT), neurotransmitter and cognitive function (COMT, MAO-A, MAOB, SLC6A4, DRD2), and metabolic and body composition (FTO, MC4R, TCF7L2, PPARG, ADIPOQ). Variant allele frequencies are cross-referenced against gnomAD v4, 1000 Genomes Phase 3, and the UK Biobank.

All Genlyy outputs are structured as machine-readable JSON reports alongside provider-facing narrative summaries with inline citations. Reports include a medication metabolism profile cross-referenced against the FDA's pharmacogenomics table (230+ drug mappings), variant pathogenicity classifications aligned with ACMG/AMP 2015 guidelines, and a prioritized intervention list ranked by clinical actionability score — a composite metric derived from population prevalence, effect size, and clinical evidence level.

Core Capabilities

Full VCF and raw SNP array ingestion — 23andMe, AncestryDNA, Illumina, whole genome compatible
4,800+ variant analysis across cardiovascular, pharmacogenomic, methylation, neurotransmitter, and metabolic systems
CYP450 enzyme phenotype classification: poor, intermediate, normal, rapid, and ultra-rapid metabolizer
ACMG/AMP 2015 variant pathogenicity classification with evidence tiering (P, LP, VUS, LB, B)
FDA pharmacogenomics table cross-reference — 230+ drug-gene interaction mappings with clinical action levels
Clinical-grade report generation: structured JSON + provider narrative with PMID citations
Methylation pathway completeness scoring and one-carbon cycle bottleneck identification
Population-stratified allele frequency contextualization via gnomAD v4 and UK Biobank

Technical Specifications

Context Window64K tokens + VCF processing

Response Latency (P50)< 3.4s (full analysis)

Variants4,800+ clinically relevant SNPs

Input Formats23andMe, AncestryDNA, VCF files

ClassificationACMG/AMP 2015 guideline-aligned

Pharmacogenomics230+ FDA drug-gene mappings

Evaluation Benchmarks

ClinVar variant concordanceInternal evaluation

96.8%

CYP2D6 phenotype accuracyInternal evaluation

98.1%

FDA PGx action level matchInternal evaluation

97.4%

ACMG criterion applicationInternal evaluation

94.2%

Example request

curl -X POST https://api.askmn.ai/v1/genomics/analyze \
  -H "Authorization: Bearer $ASKMN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "genlyy-1.3",
    "input_format": "23andme_raw",
    "analysis_modules": [
      "cardiovascular", "pharmacogenomics",
      "methylation", "neurotransmitter", "metabolic"
    ],
    "output": {
      "format": "structured_json",
      "include_narrative": true,
      "include_pgx_table": true,
      "citation_level": "full",
      "acmg_classification": true
    }
  }'

Training & Alignment

How each model is built

Domain-expert RLHF is the core of every model. We do not fine-tune general-purpose models and ship. Every release requires independent clinical evaluation before production deployment.

Pre-training Corpus

50M+ PubMed papers, botanical monographs (Commission E, WHO, ABC HerbMed), RTECS toxicology database, Merck Manual, UpToDate clinical references, DSM-5-TR, APA guidelines, ClinVar, gnomAD v4, OMIM, NHANES panels

Supervised Fine-Tuning (SFT)

Domain-specific instruction datasets curated by board-certified herbalists, licensed clinical psychologists, registered dietitians, and clinical geneticists. Each dataset undergoes double-blind expert annotation with inter-rater reliability (IRR) > 0.85 (Cohen's κ).

RLHF Alignment

Reinforcement Learning from Human Feedback with domain expert preference models. Reward models trained on pairwise comparisons by domain specialists. PPO optimization with KL-divergence penalty to prevent capability regression.

Red-Teaming & Safety Eval

Adversarial prompt testing for harmful herbal combinations, crisis escalation failures, and pharmacogenomic misclassification. Ongoing quarterly evaluations by an independent clinical advisory board.

Side by Side

Model comparison

Feature	AskMNv3.0	Sylviav2.1	NutriGenv1.0	Genlyyv1.3
Context Window	128K tokens	128K tokens	64K tokens	64K + VCF
Latency P50	< 1.2s	< 1.5s	< 2.1s	< 3.4s
Domain	Integrative Health	Mental Health	Nutrition + Genomics	Genomic Variants
Knowledge retrieval	✓	✓	✓	✓
Persistent memory	✓	✓	✓	—
Genomic input	—	—	SNP data	VCF + SNP arrays
Safety layer	Contraindications	Crisis protocol	Interactions	ACMG classification
HIPAA compliant	✓	✓	✓	✓
Provider output	✓	✓	✓	✓
Status	GA	GA	Beta	Invite Only

All benchmark scores are reported on held-out test sets not used during training or fine-tuning. Benchmark methodology and full evaluation reports are available under NDA for enterprise partners and research institutions.

Safety & Compliance

Built for clinical environments

HIPAA-Conscious Architecture

All model inference runs in isolated, encrypted compute environments. PHI handling follows HIPAA technical safeguard requirements with full audit trails.

Grounded Outputs with Citations

All clinical assertions are anchored to retrievable source documents. PMID references and confidence levels accompany every substantive recommendation.

ACMG/AMP & DSM-5 Alignment

Genomic variant classifications follow ACMG/AMP 2015 guidelines. Mental health outputs follow DSM-5-TR diagnostic frameworks and APA safe messaging standards.

No Training on User Data

User health data, conversations, and genomic profiles are never used to train or fine-tune any Mother Nature AI model without explicit written consent.

Access the API

Integrate any model into your health platform, clinical workflow, or research pipeline.

Developer Documentation Contact Sales

Latency SLA available

SOC 2 Type II & HIPAA Compliant

HIPAA BAA available

Purpose-builthealth AI models

AskMN v3.0

Core Capabilities

Sylvia v2.1

Core Capabilities

NutriGen v1.0

Core Capabilities

Genlyy v1.3

Core Capabilities

How each model is built

Pre-training Corpus

Supervised Fine-Tuning (SFT)

RLHF Alignment

Red-Teaming & Safety Eval

Model comparison

Built for clinical environments

HIPAA-Conscious Architecture

Grounded Outputs with Citations

ACMG/AMP & DSM-5 Alignment

No Training on User Data

Access the API

Purpose-built
health AI models