Up to 80% less compute.By design.
Most AI sends every question — even “when should I take magnesium?” — to a giant general-purpose model on a power-hungry GPU cluster. SPLII.ai plans first: it compares a specialist-first plan against a generalist plan before anything runs, and only pays for frontier compute when the simple path won’t clear the bar.1
- 40–80%
- Projected compute reduction per request¹
- 3
- Power modes: eco · balanced · performance
- 5
- Scoring signals — power is one of them
- Goal
- Renewable & off-grid AI infrastructure²
In plain English
The problem
AI has a power problem.
A dense general-purpose model activates all of its parameters for every token it produces. Ask it something simple and you still pay for all of them. Expert routing — a mixture of experts at the system level, across separate specialist models rather than inside one network — wakes only the specialist the question needs.
Dense general-purpose model
Every parameter activates for every token — a vitamin question costs the same as a genome.
Routed experts (SPLII)
The gate wakes only the specialist that fits the question. Everything else stays dark.
Concept demo · system-level mixture of experts
The right expert. Nothing more.
How power-aware routing applies to Mother Nature AI, built by Synnc Labs: a nutrition question goes to NutriGen, a genetics question to Genlyy, a mental-health conversation to Sylvia — and only multi-domain questions reach the full-scale AskMN model. Tap a question to watch the gate decide.3
“Which foods help with low ferritin?”
Routed to
NutriGen 1.0
nutrition · low complexity
Relative power draw (target tiers, illustrative)
How it works
Power-aware routing, in four steps.
- 01
Analyze
The request is parsed into an intent and lowered into the capabilities it actually requires.
- 02
Simulate
SPLII’s patent-pending planner scores specialist-first and generalist plans on confidence, cost and power — before running either.
- 03
Activate
Only the chosen specialist runs. The general-purpose giant stays asleep.
- 04
Escalate if needed
Hard, multi-domain questions climb to a larger model — on purpose, not by default.
The models
Four specialists, one roadmap.
The Mother Nature AI specialist family, ordered from the most power-efficient target to the most capable. Each runs today as a specialist agent; dedicated models are being trained against their own corpora.
Nutrition
NutriGen 1.0
Nutrition & nutrigenomics specialist
- Today
- Domain-configured specialist agent on a shared foundation model, with persistent memory
- Dedicated model
- Planned: a compact dedicated model
- Target tier
- Compact · low-power target
- Target power tier
What it handles
Grounded in
Mental health
Sylvia 2.1
Mental wellness specialist
- Today
- Domain-configured specialist agent with crisis routing to 988, Crisis Text Line and 911
- Dedicated model
- Planned: a dedicated model with a clinician-reviewed evaluation set
- Target tier
- Specialized · optimized target
- Target power tier
What it handles
Grounded in
Genetics
Genlyy 1.3
Genomic variant intelligence
- Today
- Partner waitlist — not publicly available
- Dedicated model
- 217K-example pharmacogenomic & variant corpus built (195,552 train / 21,729 eval); fine-tuning underway
- Target tier
- Specialized · on-demand target
- Target power tier
What it handles
Grounded in
General health
AskMN 3.0
General health & herbal intelligence
- Today
- Lead health agent on a frontier foundation model, retrieval over MedlinePlus, NIH ODS and openFDA
- Dedicated model
- 200K-example instruction corpus across 4 task families built for the dedicated v3 model
- Target tier
- Full-scale · standard compute
- Target power tier
What it handles
Grounded in
Green AI principles
Compute is a resource. We spend it carefully.
Right-sized inference
Match every query to the smallest model that can answer it well — not the biggest one available.
Small models, small machines
Compact specialists can run on lighter hardware — even on-device — with fewer active parameters per token.
Domain-specific efficiency
A model trained deeply on one domain beats a giant generalist on that domain, at a fraction of the compute.
Escalate, don’t default
Frontier-scale models are reserved for the questions that truly need them.
Renewable-ready routing
Routing is designed to prefer cleaner compute as renewable and off-grid capacity comes online.
Meter, then claim
We are instrumenting per-route energy so efficiency numbers come from meters, not models — and never trade away accuracy silently.
- 1. Projected range from the SPLII architecture’s planning model (40–80% fewer GPU-hours per request, depending on workload mix), versus sending every request to a general-purpose model. Power is currently estimated from per-expert efficiency ratings; metered energy measurement is in progress, and we will publish measured results with methodology.
- 2. Renewable and off-grid AI infrastructure is a goal we are working toward, not a claim about today’s operations.
- 3. Concept demo. Today Mother Nature AI’s specialists run as domain-configured agents on shared foundation models and are chosen in the app; automatic power-aware gating and dedicated compact models are on the roadmap. Power tiers are relative targets, not measurements.
Let’s build the impossible thing.
Hardware, software, AI or all three. Tell us what you want to exist — we’ll tell you how fast it can.
hello@synnclabs.comText or call us at any time · (934) 300-1042