Reviewed pitch deck / rebuilt in current Senlay style
Physical-world verification API
Senlay.
Evidence from the world outside, ready for agents and safety apps.
Problem
AI systems are entering real workflows. They need physical-world evidence.
- Agents plan drone missions, outdoor crews, vessel routes, and crop timing.
- They reason in text. They do not measure conditions.
- They rely on generic forecasts, stale dashboards, device pings, or training-data guesses.
Wind, gusts, waves, air quality, terrain, tides, and sensor freshness change the answer. A confident generic answer can be operationally wrong.
Insight
The missing layer is not more weather data. It is decision-ready physical evidence.
Senlay: "wind is 18 knots, gusts are rising, the live station is fresher than the forecast, confidence is medium-high, and this matters for this decision."
Product
One verification request. Live physical-world verification for a coordinate.
Agent asks about a place and domain.
Senlay gathers sensors, models, marine, air, terrain, and hazard signals.
Values carry source, freshness, distance, and confidence.
Live and forecast disagreement is surfaced.
Response includes agent-ready decision context and risk-event output when the workflow needs escalation.
Demo
Drone go/no-go in one verification request.
"Can a drone safely fly here, now?"
Why now
Agents are moving from chat into real-world workflows.
Chatbots
LLMs answer questions and draft text.
Copilots
LLMs help humans inside tools and workflows.
Operational agents
Agents schedule, route, plan, and trigger real actions. Physical context becomes infrastructure.
Market and wedge
Start with SmartSurf safety. Expand into a outdoor verification platform.
| Stage | Market | Why it works |
|---|---|---|
| Now | SmartSurf schools, stations, and serious riders | Clear GPS-to-risk workflow, real buyer pain, hardware path, and visible safety value. |
| Year 2 | Drone, outdoor work, and agriculture AI | Weather, heat, wind, terrain, and AQ affect launch, scheduling, spraying, and safety. |
| Year 3+ | Marine, SAR, insurance, autonomy, digital twins | Higher value after reliability, evidence, SLAs, and domain validation. |
Business model
SmartSurf-first pilots, developer API pricing, outdoor expansion.
| Tier | Price | For |
|---|---|---|
| Free Developer | $0 | Builders, demos, testing, and feedback. |
| Agent / IoT Developer | $29/mo | Indie devs, prototypes, early AI products. |
| Safety Pilot | from $199/mo | SmartSurf-style safety workflows, risk_event tuning, higher limits, support, and domain packs. |
| Outdoor Verification | custom | Production drone, field, marine, agriculture, safety, and insurance workflows. |
Why they pay: physical-world errors cost money, time, safety, and liability.
Moat
The moat is not raw weather data.
Fragmented weather, marine, aviation, AQ, terrain, satellite, and sensor sources become one contract.
Every value carries source, freshness, distance, confidence, and disagreement.
Wind, water, terrain, and risk knowledge become domain context for agents.
20 years of lived wind, water, and hardware experience.
Path to customer-owned and partner-owned local stations where public data is weak.
Real agent workflows reveal where sources agree, diverge, and need calibration.
Founder
Founder-market fit through 20 years of real-world wind, water, and risk.
- Sole human founder, Hoi An, Vietnam.
- 20+ years certified kitesurfing instructor.
- IYT International Bareboat Skipper, Power & Sail up to 24 m.
- 1NCE Certified IoT Integrator.
- Built SmartSurf GPS safety tracker hardware before Senlay.
The product value is not knowing that wind exists. It is knowing what a raw number means in field conditions, where sensor placement, gusts, exposure, water state, and weak signals change the decision.
Traction and roadmap
Live platform. Public beta. Building toward paid pilots.
| Window | Proof |
|---|---|
| Live today | API, docs, demo, dashboard, BYOK LLM profile keys, Senlay Water reference app, source/freshness/confidence concepts. |
| Next 6 months | Evidence Object v1, drone and outdoor-work domain packs, reliability monitoring, data quality, 5-10 paid pilots. |
| Next 12 months | 25-50 paying developer/team customers, 3-5 enterprise pilots, agriculture domain pack, private sensor path. |
Ask
$500K pre-seed.
- API reliability, monitoring, and Evidence Object v1.
- Drone and outdoor-work domain packs.
- Data quality and confidence scoring.
- 5-10 paid pilot conversions.
- Senior backend/data engineering support.
- Paying customers across Pro and Startup/Team tiers.
- 3-5 enterprise pilot conversations or contracts.
- Agriculture and outdoor-work packs live or in progress.
- Revenue and pilot proof for seed round.
New live-evidence investor brief / rebuilt into the same deck
Live evidence inside
Spot-level verification for outdoor AI apps.
The newer investor brief sharpens the thesis: Senlay is not just better answers. It is better evidence behind the answer.
Failure mode
Agents confidently hallucinate about the physical world.
A browser or agent cache can replay two-hour-old "live" conditions unless dynamic endpoints are explicitly uncached.
A forecast may be reasonable for the region but wrong at the beach, ridge, field, marina, or launch site.
Without source and disagreement, the answer sounds certain even when the evidence is weak.
Live case
Hoi An: "Can I kite right now?"
The agent gives generic advice from a forecast-like answer. It cannot inspect local sensor freshness, hardware/model divergence, gust factor, waves, or terrain exposure.
The agent receives current wind, gusts, wave state, direction, source labels, distance, confidence, and a kitesurf-specific interpretation with warnings and recheck guidance.
Same question. Different answer because the agent gets measurable context.
Forecast trust score
Senlay checks whether the forecast itself deserves trust.
| Signal | Hardware | Forecast model | Interpretation |
|---|---|---|---|
| Wind speed | Nearby station first | Model trend | Use hardware as anchor; model as comparison. |
| Gust factor | Live gust/speed ratio | Often smoothed | High gust factor lowers confidence for beginner decisions. |
| Trend | Observed change | Forecast trajectory | Agreement increases forecast trust; divergence lowers it. |
| Verdict | Evidence | Prediction | Do not hide disagreement inside one number. |
Sensor hierarchy
Closest valid hardware anchors. Farther hardware audits.
Find nearest real hardware sensor with valid QC.
Apply minimum local modifiers: terrain for land, bathymetry/marine context for sea.
Use farther sensors to compare trend and detect suspicious divergence.
Compare live observations with forecast models.
Return trust score and explain why the forecast can or cannot be trusted here.
This rule applies across locations and domains: nearest trustworthy measurement first, model and remote sensors as context.
Founder velocity
Agents found real bugs. Senlay shipped fixes and re-verified.
- Kelvin values passing through as Celsius.
- VPD unit confusion.
- Agent-facing endpoint cache risk.
- UI mode mismatch between kitesurfing and drone prompts.
Senlay is being tested by the exact class of customer it serves: AI agents that need physical context, source freshness, and clean interpretation. The product improves under agent pressure.
Thesis
Not just better answers. Better evidence behind the answer.
If AI agents become the operating layer for real-world workflows, they need a context layer for measurable physical conditions. Senlay is building that layer.
Evidence Object v1, reliability, monitoring, confidence scoring.
Drone and outdoor-work domain packs, 5-10 paid pilots, customer evidence.
Tip: use the browser print dialog to export this rebuilt HTML deck to PDF. The source combines the reviewed 12-slide pitch materials with the 7-slide live-evidence investor brief.