Part 1 The problemWhy teams need this
01 · THE PROBLEM
When a batch comes back below spec, the post-mortem means pulling climate logs, irrigation data and lab certificates from separate systems. It takes 60 to 90 minutes and often ends in a guess. By the time anyone knows, the next batch is already growing under the same conditions.
Part 2 How it worksWhat it does, step by step
02 · WHAT IT DOES
A pilot for an indoor cultivation facility. When a lab result comes in, it's joined to the full climate history of the room that batch grew in. The team gets a plain-English explanation of what likely drove the result, a recipe recommendation for next time, and a growing library of what works.
03 · HOW IT RUNS
Step by step, as built.
Catch the new result
A new lab result row starts the run.
Pull the room's history
Every environment log for that room across the growing cycle is loaded.
Correlate
Average VPD, temperature, humidity and CO₂ are compared against the expected range for the outcome.
Explain and recommend
A plain-English insight and a recipe recommendation go to the recipe library and to the cultivation team in Slack.
04 · TOOLS AND APPS
Built around the systems already in the process.
05 · WHEN SOMETHING BREAKS
Failure is designed in.
Standard on every build
- Schema validation before downstream writes
- Retry and error routes for external API failures
- Duplicate-safe processing and idempotent updates
- Human approval where the action carries business risk
- Execution logging for support and audit review
Part 3 The impactWhat it's worth, and how we'd build yours
06 · PROJECTED IMPACT
What it should change in the business.
Projections for a typical deployment. The calculation below shows the math, and you can put in your own numbers.
07 · ROI CALCULATION
How it pays back in your business.
The starting numbers are a hypothetical deployment sized to the projections above. Change any of them to your own volumes and costs, and the math updates underneath.
- Full-time equivalent freed
- 0.5 people
- Gross value
- $32,400
- Running cost
- −$120
- Return per $1 of running cost
- $270
The math: 60 lab results analysed × 75 min × 12 months × 90% ÷ 60 = 810 hours a year × $40/hour = $32,400. Net value = gross value − $120 running cost a year.
08 · HOW WE'D BUILD YOURS
How we'd build yours.
- Discover: map the current process, systems, volumes, owners and exceptions.
- Design: define the canonical data model, approvals, retries and system boundaries.
- Build: implement credentials, nodes, validation and observable error routes.
- Prove: run controlled data through success, duplicate and failure scenarios.
- Operate: publish runbooks, ownership and measurable service levels.
