Investor brief / May 2026
Physical-world evidence engine for water-safety automation and AI decisions.
Senlay verifies weather, ocean, sensor, tracker, terrain, and GPS/device data before software decides what is safe, risky, or urgent.
The first commercial product is SmartSurf — an operational water-safety system for schools, stations, beach operators, resorts, and lifeguard workflows.
Weather, marine, aviation, air quality, terrain, satellite, hydrology, and hazard sources.
Conditions, evidence, confidence, disagreement, and decision meaning in one response.
Rider/board GPS, school monitoring, environmental context, and risk-event escalation in one visible safety workflow.
12-month plan toward Evidence Object v1, domain packs, paid pilots, and customer proof.
The problem
Operational agents need evidence, not just forecast values.
Agents are starting to plan drone missions, schedule outdoor crews, route vessels, time spraying, and support real-world decisions. Generic forecasts and LLM guesses are not enough when wind, waves, weather, terrain, air quality, and sensor freshness can change the answer.
Training data cannot answer "right now."
An LLM can sound confident while using stale, generic, or cached context for a local decision.
Generic weather APIs were built for broad dashboards.
They return values, but usually do not expose source hierarchy, freshness, local disagreement, or domain meaning.
The missing layer is local verification for outdoor decisions.
Agents need measurable conditions with source, distance, confidence, and a plain-language interpretation they can use in a workflow.
Product
One verification request returns conditions, evidence, risk context, and decision meaning.
Senlay is not a consumer weather app. It is infrastructure for SmartSurf, outdoor AI apps, IoT systems, and safety applications that need source-aware physical verification before they act.
Measured conditions
Wind, gusts, weather, waves, tides, currents, air quality, terrain, bathymetry, hazards, and sensor availability.
Evidence per value
Source type, station distance, measured timestamp, freshness, trust score, and whether the value is sensor or model derived.
Evidence-ready interpretation
Structured verification output for safety workflows, application code, and audit trails.
Why different
Enterprise weather platforms sell broad resilience. Senlay verifies local outdoor decisions.
| Capability | Generic weather API | Senlay |
|---|---|---|
| Format | Numbers for dashboards. | Evidence-bearing verification output for software and safety workflows. |
| Source per value | Often hidden or blended. | Source type, station/network identity, distance, and freshness are surfaced. |
| Disagreement | Usually hidden inside one output. | Hardware and model divergence is exposed so agents can decide whether to trust the forecast. |
| Interpretation | Raw values. | Domain context for drones, field work, watersports, agriculture, and marine workflows. |
Evidence Object v1
A proof block attached to physical-world claims.
Evidence Object v1 is the funded milestone that makes each important Senlay value auditable: where it came from, when it was measured, how far away it is, whether sources agree, and why the interpretation was made.
"Wind is risky."
The agent receives a conclusion but cannot inspect whether the source was live, nearby, fresh, or trusted.
"Wind is risky, and here is why."
The answer carries source, timestamp, distance, measured/model status, confidence, disagreement, and interpretation reason.
{
"wind.speed_ms": {
"value": 9.4,
"unit": "m/s",
"evidence": {
"source_type": "sensor",
"age_min": 6,
"distance_km": 4.2,
"confidence": "medium-high"
},
"disagreement": {
"alternate_source": "model",
"delta": 3.3,
"weighting": "live_preferred"
}
}
}Market and wedge
Start with SmartSurf. Expand into outdoor recreation and indie developer workflows.
The first visible proof is SmartSurf: water-sports safety for riders, boards, schools, and stations. The same fusion engine then expands to indie developers and outdoor app teams building drones, field operations, agriculture, SAR, route planning, and recreation workflows.
SmartSurf schools and stations
Clear physical risk, real hardware path, obvious buyer pain, and a visible workflow from GPS trigger to Senlay context to station alert.
AI agents for drones, field ops, agriculture
Weather, heat, wind, humidity, air quality, and terrain affect launch, scheduling, spraying, work safety, and logistics.
Marine, SAR, insurance, autonomy
Higher-value workflows after reliability, evidence contracts, SLAs, and domain validation mature.
Business model
SmartSurf-first revenue, Senlay API expansion.
Custom or from $199/mo depending on pilot scope.
For schools, stations, beach operators, resorts, and lifeguard workflows.
SmartSurf trackers, board/rider monitoring, school dashboard, alert workflow, and safety history.
Developer and enterprise access later for marine, drone, agriculture, field operations, SAR, and insurance workflows.
Moat
The moat is not raw weather data.
Fragmented sources become one contract.
Weather models, METAR, buoys, tides, AQ, terrain, satellite, hazards, and future sensors.
Every value carries trust metadata.
Source, freshness, distance, confidence, and disagreement with alternate sources.
Domain rules from lived field experience.
Twenty years of wind, water, hardware, and safety decisions encoded into structured evidence and risk context.
Closest valid hardware anchors truth.
Farther sensors audit trends and disagreement instead of overriding the local source.
Public data first, private sensors later.
Schools, marinas, farms, field sites, and customer-owned stations can strengthen weak spots.
Agent workflows create calibration data.
Repeated decisions reveal where models, sensors, and domain modifiers need local correction.
Traction and roadmap
Live platform, public beta, and a path to paid pilots.
| Stage | Milestones |
|---|---|
| Live today | Public site, API docs, interactive demo, developer dashboard, BYOK LLM profile keys, SmartSurf integration path, risk_event endpoints, and Physical World Model responses with source/freshness/confidence concepts. |
| Next 6 months | Evidence Object v1, drone go/no-go domain pack, outdoor-work domain pack, reliability and monitoring, data quality scoring, and 5-10 paid pilots. |
| Next 12 months | 25-50 paying developer/team customers, 3-5 enterprise pilot conversations or contracts, agriculture domain pack, and private sensor integration path validated. |
19-slide rebuild
Old pitch logic plus new live-evidence story.
SENLAY / investor deck / rebuilt in current style
Slide deck
The full deck now combines both source sets.
The first 12 slides rebuild the reviewed pitch deck. The last 7 slides rebuild the newer investor-brief PDF around live evidence, hardware/model divergence, sensor hierarchy, and founder velocity.
- 01-12
Problem, insight, product, demo, why now, wedge, pricing, moat, founder, roadmap, ask.
- 13-19
Live evidence update, Hoi An case, forecast trust, sensor hierarchy, agent-verified fixes, thesis.
Founder-market fit
20 years reading wind, water, and risk in real conditions.
Viktor Kryvotsiuk is the sole human founder in Hoi An, Vietnam. Senlay grew out of water-sports instruction, marine safety decisions, and SmartSurf hardware work before becoming a horizontal API for agents.
Viktor Kryvotsiuk
- 20+ years certified kitesurfing and water-sports instruction across Ukraine, Vietnam, Egypt, and Sri Lanka.
- IYT International Bareboat Skipper, Power & Sail up to 24 m.
- 1NCE Certified IoT Integrator.
- Built SmartSurf GPS tracker hardware for riders and boards.
- Shipped Senlay as a live public beta with an AI-agent coding team.
Investor FAQ
Hard questions, direct answers.
Is this just another weather API?
No. Weather APIs return values for dashboards. Senlay returns evidence-bearing context for agent reasoning: source, freshness, distance, confidence, disagreement, and domain interpretation.
Who pays first?
First paid customers are water-sports schools, kite/wing/foil stations, resorts, and beach operators that need rider/board monitoring, local alert workflows, and safety records.
Senlay API revenue comes later from teams building marine, drone, field-operation, agriculture, insurance, and outdoor safety workflows on the same evidence engine.
Why not sell to every vertical immediately?
The first wedge is narrow on purpose. Drone go/no-go and field operations are concrete enough to demo quickly. The horizontal market comes from adding domain packs on the same evidence layer.
Could a large platform build this?
They could build pieces. The hard work is fragmented, operational, and domain-specific: source normalization, freshness, confidence, live/model disagreement, domain rules, and field interpretation.
How does Senlay handle safety?
Senlay is not a safety authority. It provides auditable context. The customer application or operator makes the final decision, which is why source, freshness, confidence, and limits matter.
$500K pre-seed to harden Senlay into paid pilot and API revenue.
Use of funds: API reliability, monitoring, Evidence Object v1, SmartSurf risk-event proof, drone and outdoor-work domain packs, data quality scoring, 5-10 paid pilot conversions, and senior backend/data support.