The best starting point for AI is a recurring task that takes too much time: reconciling invoices, cleaning spreadsheets, checking operations or researching prospects. Volckano combines AI, data engineering and business-rule automation to turn those tasks into reviewable workflows.
These examples show where efficiency gains can come from, what we deliver and how to measure the result in your operation.
1. Finance: reconcile shipments and prepare invoices
Project example: a Canadian UPS authorized dealer processes billing files with about 8,000 rows and more than 200 columns per cycle. Volckano built a workflow that matches shipment records to rates and fuel surcharges, handles adjustments and taxes, and produces reconciliation sheets and customer CSV/PDF invoices. Unmatched shipments go to a human review queue. The system has been in daily use since 2024.
Efficiency and cost opportunity: replace repeated spreadsheet matching and invoice preparation with a consistent process, so staff can focus on exceptions. Measure time per billing cycle, manual corrections and unresolved discrepancies.
Explore finance automation or our data analysis and finance automation service.
2. Operations: turn inconsistent records into usable data
Project examples: Volckano has cleaned and standardized last-mile delivery records for analysis and prepared more than 20 years of sales and inventory data for a Canadian building-materials distributor. The work starts with inconsistent addresses, product fields and business definitions.
What we deliver: repeatable data preparation, explicit calculation rules and analysis-ready datasets. This reduces repeated cleanup before each report and gives teams a consistent basis for delivery analysis or procurement planning. Measure preparation hours, missing or duplicate records and the time needed to produce a report.
Explore data-cleaning pipelines and business intelligence, supported by our data analysis service.
3. Logistics: find loading-process exceptions faster
Project example: Volckano delivered vision monitoring for port and offshore-warehouse loading operations. The system recognizes work steps, compares them with the agreed procedure and marks deviations with timestamps for review.
Efficiency and cost opportunity: help supervisors locate relevant events instead of reviewing entire recordings, and identify recurring process issues before they lead to more rework. Measure review time per incident, exception-confirmation rates and repeat deviations. The workflow analyzes actions; face recognition is not used by default.
Explore operational vision monitoring and our computer vision and industrial automation service.
4. Business development: focus research on relevant buyers
Solution example: Goldrute organizes overseas buyer research using trade evidence, including customs bill-of-lading records, to help teams build a shortlist with reasons for inclusion.
Efficiency and cost opportunity: reduce repeated company searches and spend more research time on prospects that fit the product and market. Measure time to produce a reviewed shortlist, the share of candidates meeting agreed criteria and the quality of the supporting evidence.
Explore Goldrute and our export buyer intelligence service.
5. Software delivery: organize QC and acceptance evidence
Solution example: AutoQC connects agreed requirements with available test evidence and code-analysis findings. Volckano scopes the review around the materials and systems the project owner can authorize.
Efficiency and cost opportunity: reduce repeated evidence collection and make handovers easier to review. Deliverables include a requirements-to-evidence map, structured findings and verification steps. Human reviewers confirm findings, untested items remain visible, and the project owner makes the acceptance decision. AutoQC is available for a scoped demonstration or pilot.
Explore AutoQC and our platform QC and acceptance support service.
Start with one workflow and measure the change
Choose a recurring process, record its current workload and agree on success measures before implementation. A pilot can compare processing time, exception rates and review effort against that baseline. Savings depend on the process, data quality, volume and review requirements; the examples above do not imply a fixed percentage reduction.
Browse our case studies, explore AI Solutions or discuss a workflow with us.