Key Takeaways

  • Blast sequencing and timing design live inside broader blast-design suites: Orica BlastIQ and Deswik.Blast.
  • BlastIQ is a cloud platform for storing, managing, and sharing blast-related information, giving quality-control visibility over blast design and execution rules.
  • Marked “Emerging” — sequencing modules exist inside mature commercial suites, but the AI-driven optimization of sequence/timing (versus just digitizing existing manual practice) is still a developing capability.
  • BlastIQ has documented API integration with Deswik.Ops, so these tools aren’t necessarily either/or — they’re increasingly interoperable.

TL;DR

Manage blast sequencing and timing through cloud-based platforms like Orica BlastIQ, which centralizes blast-related data and enforces design/loading rules, with Deswik.Blast offering a comparable sequencing capability integrated into the broader Deswik suite.

How Do I Optimize Blast Sequencing With AI?

Blasting sequence — the order and timing in which individual blast holes detonate — has traditionally been designed by experienced blast engineers using rules of thumb, then executed with limited visibility into whether the plan was actually followed on the bench. The digitization of this workflow is what’s currently happening, and it’s the necessary substrate for any future AI-driven sequence optimization.

Orica BlastIQ is the clearest example: it’s a cloud-based platform that stores, manages, and shares all blast-related information, giving engineers systemized control over design and loading rules and improved visibility into what’s actually happening on the bench during execution. Critically, BlastIQ exposes API endpoints — mining teams have used these to connect it into their own planning and reporting systems, including Deswik.Ops, meaning it’s designed to sit inside a broader digital mine-planning stack rather than as an isolated silo.

Where this becomes AI rather than just digitization is in the sequencing/timing optimization layer that sits on top of the captured data — using historical fragmentation and movement outcomes (see our companion post on Blasting Evaluation) to inform which sequences and delay timings actually produce the desired fragmentation and dig-ability for a given rock mass. That optimization loop is real but still maturing across the industry, which is why we’ve flagged this “Emerging” rather than “Yes”: the data infrastructure is mature, the closed-loop AI optimization on top of it is not yet standard practice everywhere.

Try It With Geocluster

Connecting blast sequencing data to downstream fragmentation and dig-rate outcomes to actually close the optimization loop is a genuine applied-AI research project, not an out-of-the-box feature. The Geocluster Research Harness is built to help teams work through exactly that kind of workflow.