Why it matters
Without it, mislabeling, lost context, backlogs in post-excavation assessment, and delays in archive deposition risk disjointed records and non-compliant HER submissions.
From design to safety
Design
AI plans the workflow schema
The system ingests the written scheme of investigation, context sheets, and finds types to model a washing-to-archiving workflow, calibrating labels and registers with explicit tag conventions and cross-reference rules. It maps batch IDs to trench sections, sets up barcode-linked context references, and integrates with the HER entry workflow to anticipate deposition requirements.
- finds registers
- bag labels
- finds trays
Build
Setup signals for field processing
It reads site setup data, trench sections, early context sheets, and the digital site plan to align washing stations, marking protocols, and traceable bagging paths with standardized checklists and batch IDs. It links washing throughput targets to flux meters and ensures spill containment data feeds into environmental logs.
- finds trays
- washing equipment
- marksing tools
Operation
Real-time processing and log updating
During daily operations, it monitors washing times, marking accuracy, and bag label integrity, updating the finds register, cross-referencing context entries, and triggering quality checks against the archive deposition plan. It records operator IDs, machine temperatures, rinse cycle counts, and deviations for audit trails.
- washing equipment
- bag labels
- finds registers
Maintenance
Calibration and archive readiness checks
Regular calibration of scanners and scales, verification of label legibility, and synchronization of the finds store inventories with the archive plan, plus periodic mock audits and dry runs. It logs calibration dates, drift measurements, and replacement part cycles for all equipment.
- finds stores
- bag labels
- finds registers
Safety
Risk checks for handling and storage
The AI flags potential mislabeling or contamination risks, ensuring secured handling, proper welfare setup, and alignment with heritage consent conditions and site security protocols. It records PPE compliance, cold-chain status for organic samples, and storage room temperature histories.
- finds stores
- washing equipment
- finds trays
Part by part
- 1
finds trays
- Watch for
- Wear patterns on tray lips, risk of cross-contamination between samples.
- AI action
- AI detects misplacements and prompts re-sorting before washing.
- 2
washing
- Watch for
- Residue build-up causing cross-contamination or incomplete rinse.
- AI action
- AI schedules batch rinses and logs actual wash cycles.
- 3
marking tools
- Watch for
- Faded or illegible markings on containers or labels.
- AI action
- AI prompts re-marking and updates coding in the context sheets.
- 4
bag labels
- Watch for
- Label unreadability or mismatch with finds register.
- AI action
- AI reprints labels and updates the Digital Bag Ledger.
- 5
finds registers
- Watch for
- Inconsistent cross-referencing between tray, bag, and entry number.
- AI action
- AI flags inconsistencies and generates an evidence-backed reconciliation report.
- 6
finds stores
- Watch for
- Access control lapses or inventory drift.
- AI action
- AI flags shelf audit needs and triggers stock-tick updates.
Worked example
Development-led site clears 12.4 ha; finds artifacts from Iron Age through late Roman
Illustrative scenario with sample data.
A trench-based evaluation expands into a full excavation; AI-guided finds processing ensures accurate washing, marking, and archiving.
Signal in
siteCode: RCH-018; period: Iron Age-Late Roman; trays: 18; labels: 18; registerEntries: 72; storageBins: 6; batchIDs: B12, B13, B14; operator: S05
System out
bag labels updated; finds register reconciled; archive deposition scheduled; traceability report generated; context maps updated in the GIS and linked to HER entry.
Human sign-off: Project manager approves the archive deposition plan, verifies compliance with HER and CIfA standards, and signs off the deposition checklist after curator review.
How to roll it out
Phase 1
Weeks 1 to 4: Baseline
Establish standard operating procedures, baseline tag formats, and initial training data for washing and labeling across all trench sections.
Phase 2
Weeks 5 to 12: Integration
Integrate digital bag ledger with archive system, implement live logging from field workflows, and pilot batch verification routines.
Phase 3
Weeks 13 onward: Optimization
Refine AI triggers, error thresholds, and reporting cadence with project team feedback, plus periodic audits of the archive deposition process.
Where people stay in charge
- Decisions on archival deposition and classifications must be signed by the registered finds supervisor or post-excavation manager in accordance with HER approvals.
- Only qualified personnel may approve changes to the written scheme of investigation and context recording practices.
- All safety-critical decisions exceeding automated thresholds must be reviewed by the site safety officer and archived.
Questions owners and crews ask
How does AI help prevent mislabeling of finds during washing and marking?
The system cross-checks tray IDs, batch numbers, and label codes against the finds register, raising alerts for mismatches and prompting corrective action before storage.
What data records are created for each find after washing?
A digital trail captures tray ID, washing cycle, marking code, bag ID, and cross-references to context sheets and the context map.
Who approves the final archive deposition record?
The post-excavation manager, in consultation with the curator and HER officer, signs off the deposition package.
How does this workflow integrate with the archive store?
The AI exports an archive-ready package including the finds ledger, context map, and deposition plan to the finds store system and HER submissions.