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 with domain-specific alignment and evaluated internally before product use.
SFT + RLHF · Retrieval over a curated index of medical literature · Internally evaluated
AskMN v3.0
Sylvia v2.1
NutriGen v1.0
Genlyy v1.3
In productionAskMN v3.0
Herbal Intelligence & Human Health Model
AskMN v3 is Mother Nature AI's primary health intelligence model — trained using a large curated corpus of medical, scie…
In productionSylvia 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…
In productionNutriGen v1.0
Nutrition Intelligence Engine
NutriGen 1.0 is a multi-modal inference engine that fuses dietary intake logs, micronutrient panels, metabolic biomarker…
Invite OnlyGenlyy 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…
AskMN v3.0
Herbal Intelligence & Human Health Model
AskMN v3 is Mother Nature AI's primary health intelligence model — trained using a large curated corpus of medical, scientific, botanical and health literature 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
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
}
}'
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 is aligned to publicly available mental health classification and practice guidance, including ICD-11 and 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
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"
}
}'
NutriGen v1.0
Nutrition Intelligence Engine
NutriGen 1.0 is a multi-modal inference engine that fuses dietary intake logs, micronutrient panels, metabolic biomarkers and — where a user provides it — genetic variant data to generate clinically-grounded personalized nutrition protocols. It is the first model in the Mother Nature AI family designed specifically for the intersection of nutritional biochemistry and metabolic health.
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. Outputs are anchored to the person — their current dietary logs, biomarker levels and, when available, variant profile — to identify actionable bottlenecks with the highest intervention leverage. Integration with Genlyy 1.3 provides full genomic context when a user has one.
Core Capabilities
- Personalized macronutrient ratio optimization from dietary logs, biomarkers and (optionally) FTO, MC4R, PPARG and TCF7L2 variants
- Micronutrient absorption bottleneck identification — B12, folate, vitamin D, iron, zinc, magnesium
- Supplement stack generation calibrated to nutrient gaps with interaction screening
- Food sensitivity risk flagging — gluten, lactose, caffeine, histamine, oxalate
- Omega-3 and omega-6 metabolism optimization
- Methylation pathway support protocols for MTHFR C677T, A1298C and related variants
- Intermittent fasting and dietary pattern suitability scoring
- Integration with Genlyy 1.3 for whole-person genomic nutrition context
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",
"diet_log_7d": { "avg_omega3_g": 0.8, "folate_mcg": 180 },
"labs": { "vitamin_d_ng_ml": 22, "homocysteine": 14.2 },
"genetic_profile": { "mthfr_677": "CT", "apoe": "e3/e4" }
}'
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, provider-facing genomic 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, MAO-B, 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
- Structured genomic interpretation: JSON output plus a provider-facing 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
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-specific alignment is central to each model. Releases are evaluated internally before they are used in our products.
Pre-training Corpus
PubMed and PubMed Central, botanical and clinical reference literature, botanical monographs, toxicology references, and publicly described clinical and genomic frameworks. Proprietary textbooks and licensed clinical databases are not represented as ingested training data.
Supervised Fine-Tuning (SFT)
Domain-specific instruction datasets reviewed by subject-matter contributors across herbal medicine, mental health, nutrition and genomics. Annotation is reviewed for consistency before use.
RLHF Alignment
Reinforcement learning from human feedback using preference data collected from subject-matter contributors, with regularisation to prevent capability regression.
Red-Teaming & Safety Eval
Adversarial prompt testing for harmful herbal combinations, crisis escalation failures, and pharmacogenomic misclassification. Ongoing internal safety evaluation.
Members can additionally opt in to contribute their own de-identified conversations to fine-tuning. Mother Nature AI does not use your conversations or health information to train its models unless you explicitly opt in. If you do, your information is de-identified first, and you can withdraw at any time.
Side by Side
Model comparison
| Feature |  AskMNv3.0 |  Sylviav2.1 |  NutriGenv1.0 |  Genlyyv1.3 |
|---|---|---|---|---|
| Context Window | 128K tokens | 128K tokens | 64K tokens | 64K + VCF |
| Domain | Integrative Health | Mental Health | Nutrition | Genomic Variants |
| Knowledge retrieval | ✓ | ✓ | ✓ | ✓ |
| Persistent memory | ✓ | ✓ | ✓ | — |
| Genomic input | — | — | Optional SNP data | VCF + SNP arrays |
| Safety layer | Contraindications | Crisis protocol | Interactions | ACMG classification |
| HIPAA compliant | ✓ | ✓ | ✓ | ✓ |
| Provider output | ✓ | ✓ | ✓ | ✓ |
| Status | In production | In production | In production | Invite only |
Performance figures are not published on this page. Evaluation results will be shared when they can be independently reproduced.
Safety & Compliance
Built for clinical environments
HIPAA-Compliant Architecture
All model inference runs in isolated, encrypted compute environments. PHI handling follows HIPAA technical safeguard requirements, with audit logging of PHI access events.
Grounded Outputs with Citations
Mother Nature AI is designed to ground health information in retrievable medical literature and provide citations where available.
ACMG/AMP & ICD-11 Alignment
Genomic variant classifications follow ACMG/AMP 2015 guidelines. Mental health outputs align with ICD-11 and APA safe-messaging guidance.
Training Only With Opt-In
Mother Nature AI does not use your conversations or health information to train its models unless you explicitly opt in. If you do, your information is de-identified first, and you can withdraw at any time.
API access is coming soon
Mother Nature AI is developing API access to its domain-specific health AI models. Public API access is not currently available. Register interest if you want to integrate these models later.