Why it matters
Without it, mislabeling, origin disputes, and incomplete certifications can occur; quality control gaps risk PDO compliance, and traceability failures threaten market access and consumer trust.
From design to safety
Design
AI-assisted traceability design
Reads planned grove blocks, harvest contours, known cultivar traits, and batch-precise lab requirements to map end-to-end data flow, establish digital records for each source, and enforce ISO-compliant metadata schema with unique geolocated identifiers for each block and harvest event.
- grove block sensor
- bottle code printer
- closure cap
Build
Data pipelines set and validated
Configures data interfaces from irrigation telemetry, leaf-area indices, and harvest data to mill and bottling records, validating tags, codes, and alignment with the planned estate schema, including GIS layer alignment and offline data caching.
- grove block sensor
- harvest lot tag
- mill batch tag
Operation
Real-time provenance tracking in workflow
Continuously ingests sensor, meter, tasting, and oil-chemical data; assigns unique IDs to blocks, lots, tanks, and bottles; flags deviations for immediate intervention by operators and QA teams; cross-checks with lab results.
- harvest lot tag
- tank sampling valve
- bottle code printer
Maintenance
Data integrity and calibration monitoring
Compares incoming readings with calibration schedules, maintains code integrity and label readability, implements versioned data lineage, and logs historical corrections for full audit readiness across campaigns.
- mill batch tag
- tank sampling valve
- closure cap
Safety
Compliance and food-safety assurance
Ensures chain-of-custody is verifiable for IOC standards, PDO eligibility, and lab-safety requirements with sign-off by certified personnel, including external certification body confirmation and annual audit trail review.
- bottle code printer
- closure cap
- tank sampling valve
Part by part
- 1
grove block sensor
- Watch for
- Sensor drift beyond set tolerances indicating misaligned block data or sensor fouling.
- AI action
- Flag data drift to manager and trigger block reassessment workflow, schedule calibration.
- 2
harvest lot tag
- Watch for
- Tag misreads or missing harvest lot IDs during unloading or transfer.
- AI action
- Reconcile IDs and pause affected transfers for verification and re-tagging if needed.
- 3
mill batch tag
- Watch for
- Batch codes mismatch between mill output and upstream harvest data.
- AI action
- Trigger batch reconciliation and reprint labels if needed, alert QA when mismatch persists.
- 4
tank sampling valve
- Watch for
- Inconsistent sampling times or wrong tank IDs compared to batch records.
- AI action
- Pause transfer to bottling and trigger QA review, schedule re-sampling.
- 5
bottle code printer
- Watch for
- Failed print quality or unreadable bottle codes.
- AI action
- Queue reprint and reapply codes before fill stage, log printer fault.
- 6
closure cap
- Watch for
- Cap defect or mis-seal detected after capping.
- AI action
- Initiate seal integrity check and segregate affected bottles, report to QA.
Worked example
Catching a bottle code misread before palletization
Illustrative scenario with sample data.
A bottle code printer shows inconsistent printing on a small batch just before bottling; potential mislabel risk detected during QC check. The system correlates bottle IDs with corresponding harvest lots and mill batches for traceability.
Signal in
printQuality=poor, batchID=B-2045, sealStatus=OK, bottleCount=500, lineID=Line-3, blockID=Block-12, harvestDate=2026-10-21, labResult=polyphenols-UT, K232=0.08, K270=0.02, maturityIndex=0.65, PDOStatus=Pending
System out
alertCode=CODE_MISMATCH, action=pauseLine, QAReview=required, nextBatch=B-2046, associatedBlock=Block-12, millBatch=B-2045, certificateStatus=UnderReview
Human sign-off: Mill master approves line pause and QA to confirm code integrity and block linkage before resuming bottling; certification lead signs off after reconciliation.
How to roll it out
Phase 1
Weeks 1 to 2
Baseline data model for grove blocks and harvest lots, assign initial bottle code schema, initialize GIS-linked metadata.
Phase 2
Weeks 3 to 6
Integrate mill batch and tank data, validate end-to-end traceability with sample runs, implement auto-reconciliation rules.
Phase 3
Weeks 7 to 12
Full pilot across one estate sector, certify labeling and PDO documentation readiness, initiate external audit prep and corrective action plan.
Where people stay in charge
- Decisions on traceability exclusions or data edits must be made by the quality manager and approved by the certifying body when applicable.
- All alterations to core identifiers (block, lot, batch, bottle codes) require dual-signature approval from the estate manager and the QA lead.
- Each data breach or suspected mislabeling must be reported to the certification auditor within 24 hours and resolved before release.
Questions owners and crews ask
What data is required to establish tree-to-bottle traceability?
Essential data include grove block IDs, harvest lots, mill batches, tank IDs, bottle codes, dates, and corresponding quality results from lab analyses. The model also references PDO certificates and IOC trade standards.
How does AI detect bottle mislabeling or origin errors?
AI cross-checks code integrity across blocks, lots, mills, tanks, and bottles against certification records and lab outcomes, flagging mismatches for human review and initiating reprint workflows.
Who signs off on traceability records for PDO and IOC?
The certified quality manager signs off; the estate manager and certification body oversee integrity and regulatory compliance, with annual surveillance audits.
What happens if a bottle code cannot be read at bottling?
The system triggers a reprint workflow, pauses the line, and queues bottles for manual verification and rerun with validated codes, documenting the incident in the batch log.