Dangerous Goods / Hazmat Compliance Software buyer guide
Review the wider capability model, products, evaluation criteria and procurement questions for this category.
Search, classify or validate dangerous goods using UN/NA identifiers, proper shipping names, hazard classes, packing groups or equivalent transport-classification data.
Recorded product facts are shown separately from buyer-specific recommendation scores. “Not yet verified” means evidence is incomplete; it does not mean the product lacks the capability.
| Product | Status | Confidence | Implementation | Evidence | Limitations / notes | Last verified |
|---|---|---|---|---|---|---|
D DGOffice.net DGM-SDG | Supported | 99% | Not recorded | 1 source | DGOffice maintains dangerous-goods classification data and DGM identifiers linked to UN numbers and classification variants. | Sep 19, 2026 |
HV Hazcheck Validate NCB Hazcheck | Supported | 99% | Not recorded | 1 source | Dangerous Goods Search covers entries in the IMDG Dangerous Goods List, ADR Dangerous Goods List and 49 CFR 172.101 table with linked packing and special-provision details. | Sep 19, 2026 |
S ShipHazmat Bureau of Dangerous Goods | Supported | 97% | Not recorded | 1 source | Current ShipHazmat guidance describes built-in hazard-class selection using UN numbers, proper shipping names and packing groups. | Sep 19, 2026 |
LE Lisam ExESS Lisam | Partially Supported | 95% | Not recorded | 1 source | ExESS calculates transport classification in SDS Section 14, but a dedicated shipment-level UN lookup/validation workflow is not inferred. | Sep 19, 2026 |
LD Labelmaster DGIS Labelmaster | Not Yet Verified | 0% | Not recorded | 0 sources | No limitation recorded. | Not recorded |
Use this capability as one requirement inside the wider category evidence model. The links below are research and comparison paths, not a product recommendation.
Review the wider capability model, products, evaluation criteria and procurement questions for this category.
Understand TechSelectAI coverage, source verification and research gaps before interpreting individual capability records.
See how support status, confidence, freshness and Unknown ≠ Unsupported are handled.
Supported, conditional, unknown and not-supported states are kept distinct so missing evidence is not treated as a negative fact.
Confidence reflects the strength of the recorded evidence for this capability, not overall product quality.
A product can strongly support this capability and still be a poor overall fit due to deployment, integrations, security, region, budget or other must-haves.
TechSelectAI can evaluate this capability together with your other must-haves, integrations, deployment constraints, security requirements, region and budget.
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