Read drone, satellite and ground imagery for anomalies, geolocate each one and rank the walk — the cause is confirmed in the field, and any treatment belongs to an agronomist and a certified applicator.
Take imagery from the flight, the satellite pass, a ground rig or a phone, with its date, sensor and capture conditions.
02
Take the field boundary, the crop, the variety, the planting date and the operations already recorded for that field.
Reason
03
Flag anomalies — weed pressure, lesions, discolouration, stand gaps, lodging and water stress — and size each one.
04
Geolocate every flag to a point and a zone a scout can find, not to a coloured area of a map.
05
Compare with the previous capture of the same field and separate crop change from capture change.
Decide
06
Mark imagery whose light, cloud, blur or angle makes the read unreliable, instead of scoring it as though it were clean.
07
Route a degraded capture, or a crop, cultivar or stage the model saw little of, to the agronomist before anyone walks.
Out
08
Produce a scouting report — what, where, how large, how certain, and the look-alikes the scout has to rule out.
09
Retain the imagery, the conditions, the model version and what the scout found when they got to the flag.
→Product statement
The agent flags and locates. What the symptom is, and whether anything is applied, stay with an agronomist and a certified applicator.
Example workflow
One capture, end to end
AgentHuman
1Imagery receivedDrone flight, satellite pass, ground rig or a phone photo, with the sensor, altitude and conditions
2Field context gatheredBoundary, crop, variety, planting date, growth stage and the operations already on record
3Anomalies flagged and locatedWeed pressure, lesions, discolouration, stand gaps, lodging and water stress, each placed and sized
4Change and capture quality checkedCompared with the last capture of the same field, with sensor, sun-angle and cloud differences separated out
No human action required
Stages 1 to 4 run without a person in the loop — flagging, locating and the change check finish before anyone is asked to walk anything. A capture the agent cannot read is returned as unreadable, not scored.
5DecisionSplits on capture quality and how far the case sits from what the model has seen
Readable capture, familiar case
Enters the walking order with its look-alikes.
Degraded capture or unfamiliar crop
Goes to the agronomist before anyone walks.
Agronomist or scout
Walks to the located flag, splits stems, digs roots or pulls a tissue sample where that is what settles it, and records the cause.
Confirm · Reclassify · Send to the lab
Walked — handed back▼
6Scouting report issuedWritten to the farm record only where write access and policy allow; no product, rate or prescription is created
7Outcome evaluatedAgreement with the walk, look-alike confusion, geolocation error and misses by crop, cultivar and stage
Corrections
A flag the scout reclassifies on the ground is counted in the evaluation.
What should not run autonomously
Human approval stays in control
Outside the boundary — human approval required8 items
Naming the cause of a symptom as confirmed.
Turning a flag into a product, rate or prescription.
Deciding an economic threshold has been crossed.
Declaring a field clear of a pest or disease.
Automation boundaryAgent acts unaided
✓Flag anomalies in the imagery and geolocate each one to a findable point.
✓Compare with the previous capture and separate crop change from capture change.
✓Mark a capture unreadable and say what made it unreadable.
✓Rank the flags into a walking order with the look-alikes named against each.
Write actions run only inside the approval boundaries agreed in implementation. The report is a walking list, not a plan.
Sending a scout into a restricted-entry interval.
Closing a flag without anyone walking it.
Reporting a regulated or notifiable pest onward.
Changing capture rules, class lists or thresholds.
Example output
One flag, annotated
Everything the agent returns is attached to the capture it came from and the place it points to.
Scouting output · single flagged anomalyIllustrative example
Capture
Crop and stage
Field, date
What was seen
Confidence
Cause
Drone flight, overcast, midday
Soybean, beginning pod
Field 12, this morning
Interveinal chlorosis, patchy
88%
Settled on the walk, not here
As receivedThe imagery as captured, with the sensor, the altitude and the light at the time.
Evidence usedPrevious capture, same fieldGrowth-stage recordPattern and extent
Why this is not a diagnosisBrown stem rot, sudden death syndrome and fungicide injury all look like this from above.
ActionConfirmReclassifySend to the lab
What the score decidesConfidence decides where this sits in the walking order, not what the problem is.
Value
Where AI adds value
The same four claims, placed at the point in the workflow where each one applies.
Where the value landsValue 01 – 04
Every captureFrom the flight, the satellite pass or the ground rig
03Imagery & field record
Apply the field's own record
Use the boundary, the crop, the variety, the planting date, the growth stage and the operations already recorded for that field.
01Approved path
Start the walk where something is
Anomalies are found, sized and located before anyone leaves the truck, so scouting time goes to the acres that have something on them.
02Human review
Send the doubtful reads to a person
A degraded capture, an unfamiliar cultivar or a symptom whose look-alikes the imagery cannot separate reaches the agronomist before it reaches a walking list.
04Build an evidence trail
Retain the imagery, the capture conditions, the model version, the flag and its location, and what the scout found on the ground — on both paths.
Integrations
Typical integrations
Five system groups connect to the same agent. Which of them are in scope is decided in discovery.
Agreement with the walk is not spread evenly across captures
A disagreement rate that reads acceptably across a season of flights can sit almost entirely in the imagery taken in poor light and in the cultivars and stages the model saw least of. Nestack reports performance by slice, not only in total.
Slice performance — reported separately, not only in aggregateIllustrative example
Slice
Failure rate
Lift
Lift vs. threshold
Status
Low sun, cloud and blur
6.4%
3.4×
Review
Cultivars with thin data
5.1%
2.7×
Review
Early growth stages
3.6%
1.9×
Watch
Repeat fields, clear midday
1.9%
1.0×
Normal
Bar: walk-disagreement rate lift vs. clear-midday baseline · scale 0–4.0× · tick marks the 2.0× review threshold2 of 4 slices over threshold
Evidence-linked improvement
What the scout found is the only ground truth there is
A flag is not resolved when the model is retrained. It is resolved when someone walked to it and wrote down what it actually was.
Improvement cycle · five stagesSwitchback — the path turns at Improve and returns at Learn
01Detect
Walk agreement, look-alike confusion and geolocation error are tracked per crop and stage.
02Diagnose
The miss is placed at the capture, the detector, the class chosen or the georeferencing.
03Improve
A class list, capture rule or model release goes out on agronomy sign-off, with its version recorded.
04Verify
Captures the scout already walked are scored again, the overturned flags among them.
05Learn
What the scout wrote becomes the label for that capture, and the look-alike joins the confusion set.
Learn → DetectThe return edge. Ground truth arrives at walking pace, so a cohort is only cleared once the season has put enough confirmed cases in it.
Typical build scope
Twelve workstreams across six weeks
The build scope read against the delivery timeline. Week structure follows the six-week plan — discovery, imagery, detection and location, evaluation, integration, then production validation and handover.
WorkstreamWeek 1Week 2Week 3Week 4Week 5Week 6
01Workflow discovery and automation-boundary definition.
02Imagery source and sensor assessment.
03Boundary, crop and growth-stage mapping.
04Georeferencing and capture-quality checks.
05Anomaly detection and symptom classification.
06Look-alike sets and out-of-range declaration.
07Change against the previous capture.
08Walking order and scout handoff.
09Walk-agreement and confusion evaluation.
10Geolocation and degraded-capture tests.
11Farm-system and scouting-app integration.
12Observability, deployment and Agent Care handover.
12 workstreams · 6 weeks · bar shows the weeks a workstream is active — several run in parallelFinal scope and sequence confirmed in discovery
Engagement tiers
What each tier includes
Rows are the capabilities named in each tier's scope. Higher tiers include everything below them.
Capability✓ in scope · — not at this tierPilotOne crop, one imagery sourceProductionProduction farm-system integrationAdvancedMulti-crop / multi-sensor fleets
Introduced at Pilot
Anomaly flagging and geolocation✓✓✓
Capture-quality marking✓✓✓
Look-alikes named on every flag✓✓✓
Cause confirmed by a person in the field✓✓✓
Baseline evaluation✓✓✓
Introduced at Production
Change comparison between captures—✓✓
Walking order and scout handoff—✓✓
Farm-system and scouting-app integration—✓✓
Observability and evaluation—✓✓
Introduced at Advanced
Multi-crop and multi-sensor coverage——✓
Multi-site and enterprise controls——✓
Build priceFrom $5,000From $8,000Custom quote
Final build priceConfirmed after discovery based on imagery sources and sensors, farm-system integrations, crop and problem range, evaluation depth, approval controls and deployment requirements.
Separate from buildBuild pricing is separate from recurring Agent Care, which covers managed monitoring, evaluations, incidents and verified improvements after launch.
What we need from you
What you bring, and what we build with it
Each input maps to a piece of build scope and a week in the delivery timeline.
You bringWe build with it
01A season of imagery from the sources you actually fly→Imagery source, sensor and capture-standard assessmentWeek 1
02Your crop, variety and planting records→Field boundary, crop and growth-stage data mappingWeek 1
03Field boundaries and the geometry you trust→Ingestion, georeferencing and capture-quality checksWeek 2
04The problems you scout for, in the crops you grow→Anomaly detection, symptom classes and look-alike setsWeek 3
05Scouting notes where someone walked and wrote down the cause→Walk-agreement and class-confusion evaluationWeek 4
06Flags that turned out to be something else, and the near misses→Regression cases, look-alike sets and failure-mode testingWeek 4
07Named agronomists and scouts to walk the pilot fields→Scout handoff, then supervised walks and validationWeeks 5–6
Nothing else is requiredDeployment, documentation and Agent Care handover are ours.
Delivery timeline
Four phases across six weeks
Phases are drawn over the weeks they actually occupy. Week 5 carries both the geolocation testing and the first walks made from a live report.
PhaseW1W2W3W4W5W6
DiscoveryW1
BuildW2 – W3
EvaluateW4 – W5
Pilot & LaunchW5 – W6
Week focusW1Workflow discovery, imagery sources and the automation boundaryW2Field geometry, georeferencing and capture-quality checksW3Anomaly detection, symptom classes and look-alike setsW4Walk-agreement, class-confusion and geolocation testingW5Farm-system integration, report format and supervised walksW6Scouts walk from live reports, then Agent Care starts
Reading the bandWeek 4 measures the agent against fields your own scouts already walked. Nothing enters a scouting round before that comparison exists.
At the end of W6Reports have run alongside your existing scouting and been walked by the agronomists who read them, then Agent Care takes over monitoring.
DurationSix-week plan shown · typical delivery 4–6 weeks depending on scope confirmed in discovery.
Next step · Agriculture AI agent
Build a scouting agent around the fields you already walk.
Show us a season of imagery, the field records behind it and the scouting notes from the walks that followed. We'll fly one field alongside your scouts and score what came back against what they found standing in the crop.
