What is worth growing? What does the harvest really cost? How could a farm and kitchen plan the next meal together? Read the operating experience, proposed business models, and technical examples behind Streamline’s neighborhood food network.
For what the software does today, see Streamline OS.
A vision for neighborhoods that plan food together: meal choices inform planting, purchasing, and kitchen work, with ingredients or prepared meals close to home.
Follow revenue from ingredient and meal purchases through crop costs, partner food, kitchen work, delivery, and capital. A four-acre scenario exposes the assumptions that decide the result.
Take one tomato-pruning task to a second farm. The test is acceptable work with compatible equipment, manageable setup, and a support bill the customer can afford.
A cherry tomato can be worth eating and too costly to pick. Count the snips, paid minutes, and saleable pounds to see which harvesting improvements change the crop decision.
A food plan has to earn its place in the week. Test ingredients and prepared meals against price, portions, substitutions, delivery, and the effort a household still has to make.
Give the vendor a crop event, a missing reading, and your current workflow. Compare the inspection decision, time spent, and full cost.
Compare a greenhouse replay with your usual investigation of the same recorded events. Measure time, record retrieval, missing evidence, and the full cost of the workflow.
Five decisions for the first farm and kitchen: will households buy again, which crops belong, can the route pay, does automation help, and can another team repeat it?
A working morning, an assistant planning lunch, fresh leaves from a nearby farm, and a home cook bringing it together. An illustrative future scene followed by the operating work it requires.
Compare two growing practices for one lettuce crop. Define the yield, quality, composition, and cost measurements before collecting the first sample.
Start where growing, kitchen work, and delivery can share a site. A cost comparison shows when adding household plots helps and when travel consumes the benefit.
Run a small Python example with two temperature readings and two spatial-capture references. Follow the timeline and inspect why the records cannot be fused as one synchronized capture.
How many saleable pounds must a harvesting robot handle to pay for itself? Compare the annual bill, accepted output, supervision, and the work customers actually need.
Before acting on a greenhouse temperature reading, check the instrument, location, time, units, and calibration. A worked record shows which conclusions survive that check.
Decide whether connecting greenhouse records to locations and times improves an investigation enough to pay for the setup. Compare replay, live state, simulation, prediction, and control.
Follow a tomato-pruning job from a grower's permission to the finished inspection: who can start it, what stops it, and what must happen before work resumes.