Iris does
Reads telemetry, forecasts, prior plans, scorecards, site context, lessons, and known limits. Writes bounded climate tactics and planning hypotheses. Posts human-readable reasoning through Slack Ops.
Verdify Live Lab
Bounded AI operations you can inspect.
We built an AI-powered greenhouse because slideware is not enough. Iris plans against real weather, live sensors, crop targets, resource constraints, and physical equipment. But the AI does not own the hardware. Firmware enforces the boundary, and the public scorecard shows whether the plan worked.
Control loop
Sensors + forecast
Iris AI planner
Validated tunables
Dispatcher
ESP32 firmware
Fans / heat / fog / misters / lights
Telemetry + scorecard
Lessons for next plan
What Iris does
AI proposes. Firmware controls. Telemetry verifies.
The greenhouse is a real physical control loop: crops, sensors, forecasts, equipment, utility costs, operator tasks, and a planning agent named Iris.
Iris does not
Directly control hardware, flip relays, replace firmware authority, claim full autonomy, or hide known limits. ESP32 firmware owns physical enforcement.
Inspect the receipts
Public proof, not a static demo.
The main website curates the buyer story. The lab subdomain keeps the deeper technical evidence visible.
Live telemetry and scorecards
Climate, plans, scorecards, costs, failures, and lessons are inspectable.
Safety boundary
Why the AI does not control relays and how ESP32 firmware enforces state.
AI tunables
The exact control surface Iris may write through validated paths.
Known limits
Sensor quality, misting geometry, planner reliability, and roadmap caveats are published.
What this proves
A bounded AI planning loop can operate against real telemetry, physical constraints, scorecards, and public audit trails.
What this does not prove yet
Full autonomy, yield optimization, profit optimization, or controlled agronomic causality.
Translate the pattern
Your workflow probably does not have relays. It still has authority boundaries.
The lab is a proof pattern for any workflow where AI can help, but wrong actions matter.
| Greenhouse | Client workflow equivalent |
|---|---|
| ESP32 firmware | System of record, policy engine, permission layer, deterministic business rules. |
| Iris planner | AI agent, workflow copilot, decision-support layer. |
| Dispatcher | Validation layer, approval workflow, schema enforcement. |
| AI tunables | Allowed AI actions. |
| Firmware safety rails | Prohibited actions and fallback rules. |
| Telemetry | Audit logs, workflow events, operational data. |
| Scorecard | KPI dashboard, evaluation rubric, quality metrics. |
The lab is a proof layer, not a greenhouse-only offer.
Good fit when
You need an AI workflow where wrong actions matter.
You want to see how boundaries, validation, telemetry, and scorecards work in a real system.
Your workflow has a system of record, policy layer, approval path, or operational data source.
You need evidence before expanding AI authority.
Not a fit when
You are looking for greenhouse automation as a product.
You want full autonomy claims without caveats.
You want AI to bypass the authority layer.
You do not want telemetry, logging, or scorecards.
FAQ
Common buyer questions.
Is Verdify mainly an agriculture company?
No. The greenhouse is a public proof lab. Verdify's commercial focus is bounded AI operations for software, life sciences, CPG, cleantech, advanced manufacturing, and similar operational teams.
Does Iris directly control hardware?
No. Iris writes bounded climate tactics. The dispatcher validates writes, and ESP32 firmware owns physical enforcement.
What should buyers take from the lab?
The transferable pattern: AI proposes, firmware or an equivalent authority layer controls, telemetry records outcomes, and scorecards guide expansion.