Valerann

1. If independent user-review coverage and broad peer benchmarking are required for procurement evaluation; this vendor has limited visibility on mainstream SaaS review platforms.

2. If procurement requires a turnkey, full ATMS replacement from a vendor with decades of module-level history; incumbent ATMS suites may present lower perceived integration risk for full-stack replacement programs.

3. If the organization lacks capacity for integration and operational change management; data-fusion platforms require structured onboarding, tuning, SOP design, and governance to maintain alert trust.

4. If the data environment is poor and cannot be improved; the utility of a data-fusion service depends on data quality and coverage.

5. If procurement requires extensive published security artifacts beyond the ISO certifications noted; buyers should request SOC 2 Type II reports, penetration test summaries, and other artifacts if these are mandatory.

Valerann is specialized infrastructure software sold primarily via procurement channels. In this research pass, no substantive product listings were found on G2 or Capterra. This is common for enterprise ITS products and means independent review coverage is limited.

Consequently, sources of user feedback in public materials are mainly:

• Customer quotes published by the vendor
• Industry press and association write-ups
• Case-study style announcements

This introduces a potential positive-source bias in available public materials; negative experiences are less likely to appear in vendor-published materials.

Because independent review platforms lack coverage, common complaints are inferred from typical ATMS/data fusion deployment pain points:

• Integration effort and complexity across heterogeneous data sources
• Alert quality that depends on data completeness and calibration
• Concerns about opaque model behavior for AI-driven detection (black-box concerns)
• Procurement friction and security review requirements

Action for procurement teams: request reference calls that include operational staff such as TMC operators and shift supervisors to validate daily usability and the false-alarm burden.

Industry press and vendor materials describe a largely remote deployment of Lanternn on a motorway in Chile, integrating cameras and operational vehicle feeds and completed in a compressed timeframe according to press coverage.

Critical evaluation:

• Remote deployment can reduce onsite travel and related costs, but integration still requires local cooperation, data access, and stable connectivity. Project timelines reported in press should be validated with implementation-level details in a procurement process.

Reporting attributed notable reductions in serious injuries and overall crashes over a 12-month period associated with a suite of road-safety measures that included Lanternn.

Critical evaluation:

• The reported safety improvements are associated with a set of measures and are not attributed solely to the software. Without a controlled study and clear attribution methodology, these results should be treated as indicative rather than causal proof of the software alone.

Valerann is a B2B intelligent transportation systems (ITS) software company focused on advanced traffic management and monitoring for road operators. Its primary product, Lanternn by Valerann, is a cloud-based data fusion and analytics platform that ingests disparate roadway data (for example, roadside ITS devices, cameras, third-party feeds, weather, historical incidents, and other sources) and produces alerts, incident detection, and risk insights intended for use in control centers. The vendor reports operational outcomes such as faster incident response and reduced serious collisions; these vendor-reported outcomes are discussed further in Case Studies and Critical Analysis.

Vendor materials describe capability areas including multi-source situational awareness through data fusion, earlier incident detection with the aim of reducing false alarms, and proactive operations features (risk profiling and predictive elements) rather than exclusively reactive response. These outcomes are plausible for implemented ATMS/data-fusion programs, but the magnitude of benefits will vary with local context, data quality, baseline maturity, and operator processes.

Target buyers:

• National and regional road authorities, toll road operators, motorway concessionaires
• Traffic Management Centers (TMCs) and control centers responsible for incident management and network performance

Positioning:

• The vendor describes Lanternn as a software layer focused on automated data fusion and alerting intended to integrate with existing ITS infrastructure. Materials indicate it is often positioned as complementary to existing ATMS deployments and as an option for roads with limited ITS coverage by leveraging third-party and non-traditional data sources.