Bring fragmented signals together.
Hardware sensors, public networks, forecast providers, satellite events, terrain, marine feeds, GPS tracks, and customer IoT devices enter through one integration layer.
Physical-world context infrastructure
The multi-field sensory layer for AI agents.
AI agents can reason, write, and plan — but they cannot sense what is happening outside. Senlay connects them to live sensors, environmental models, terrain, oceans, hazards, and device telemetry, then returns evidence-backed context they can use before acting.
Why Senlay exists
Agents should not guess when the world can be measured.
Training data can describe a place. A generic API can return a number. Senlay adds the missing trust layer: what was observed, where it came from, when it was measured, whether sources agree, and how much the result should influence a real-world decision.
Attach evidence to every answer.
Observations and predictions stay distinct. Freshness, proximity, confidence, source health, and model-versus-reality disagreement remain visible instead of being hidden.
Return context an agent can act on.
Field-specific rules turn raw readings into structured context, risk events, and plain-language uncertainty without pretending the API is a final safety authority.
One core, many fields
A horizontal sensing layer with domain-aware outputs.
The evidence contract stays consistent while the sources, rules, and decision context adapt to each physical workflow.
Autonomous and drone operations
Wind, gusts, precipitation, visibility, terrain exposure, airspace weather, and go/no-go context.
Marine and water safety
Waves, tides, current, buoy observations, wind direction, coastal geometry, GPS drift, and rescue-margin context.
Agriculture and field work
Heat, humidity, evapotranspiration, frost, precipitation, soil-relevant context, and spray-window conditions.
Environmental monitoring
Air quality, fire and hazard feeds, satellite events, earthquakes, volcanoes, and source freshness.
Connected products and IoT
Private station networks, trackers, device telemetry, customer sensors, webhooks, and normalized evidence records.
AI assistants and workflows
Agent-ready context strings, versioned JSON, API-key access, model integrations, and auditable risk events.
How it works
From unreliable upstreams to explainable agent context.
Senlay is an end-to-end production system, not a static concept page. It handles provider integrations, normalization, trust logic, authentication, persistence, API delivery, and Linux deployment behind a public HTTPS service.
Source adapters
Connect weather, marine, aviation, environmental, satellite, and private-device APIs with bounded timeouts and explicit licensing controls.
Evidence contracts
Convert incompatible payloads into versioned observations, predictions, static context, provenance, and source-health records.
Context and risk
Fuse local evidence, terrain modifiers, fallback policy, domain rules, and disagreement into machine- and human-readable outputs.
Production delivery
Serve through Node/Express, SQLite, Nginx, PM2 or Docker with secrets isolation, health checks, logs, backups, and deployment runbooks.
Engineering proof
Built to be diagnosed, supported, and deployed.
The project demonstrates the work between “API idea” and “reliable service”: tracing failures across integrations, validating responses, managing Linux runtime state, protecting credentials, documenting boundaries, and communicating what the system knows and does not know.
API troubleshooting
Authentication, validation, rate limits, upstream errors, normalized responses, OpenAPI contracts, and reproducible test requests.
Linux / VPS operations
Nginx reverse proxying, PM2 processes, environment configuration, filesystem permissions, runtime data, smoke checks, and recovery steps.
Integration resilience
Timeouts, caching, singleflight requests, graceful degradation, source-health tracking, and transparent fallbacks when providers fail.
Technical communication
Public docs, agent-readable manifests, deployment guides, decision boundaries, provenance, and clear explanations for developers and operators.
First real-world proof
SmartSurf is where the sensory layer meets water.
Senlay grew from a practical problem: outdoor decisions depend on local reality, while models and nearby sensors can disagree. SmartSurf applies the same evidence engine to riders, boards, schools, GPS trackers, weather, marine conditions, and water-safety workflows.
Phone-first safety workflow
Sessions, GPS location, emergency contacts, local conditions, alerts, and operational history.
Tracker and sensor integration
Waterproof GPS/SOS devices and private telemetry add live rider, board, and fleet signals to environmental context.
From prototype to service
A working vertical that connects physical hardware, backend APIs, risk logic, dashboards, documentation, and production infrastructure.
Build with Senlay
Give your agent a way to check the world.
Start with the live demo, inspect the evidence contract, or connect an agent through the documented API. The free tier is designed for prototypes and integration testing.