G2

Aswin K.

Full-Stack Developer Intern

Computer Software

Small-Business (50 or fewer emp.)

7/31/2026

"Arkion’s AI Defect Detection Streamlines Grid Inspections and Speeds Action"

4/5

What do you like best about Arkion?

Grid inspection has historically been one of the most labour-intensive, time-consuming, and expensive parts of running a utility operation. Helicopter flyovers, manual image review, inconsistent assessments across different inspection crews — the whole process has needed rethinking for years. Arkion is the most serious attempt we've used to actually do that rethinking with AI rather than just digitising the old workflow.

The defect detection capability is where it earns its place. Built on specialised AI models trained on millions of grid images, Arkion detects component-level defects and risks faster and more accurately than manual methods from cracked insulators to vegetation encroachments, every finding is automatically classified, prioritised, and ready for review. That last part, automatically prioritised is the detail that operationally changes things. Instead of analysts working through thousands of images and making their own triage calls, the platform surfaces what needs attention first, which meaningfully compresses the time between inspection and action.

All inspection data, 2D, thermal, and LiDAR is organised and accessible in one secure platform, which sounds straightforward but represents a genuine consolidation from what we were doing before across multiple disconnected systems and formats. Having a single browser-based interface to review findings, filter by severity, and plan maintenance actions without switching between tools has simplified our internal workflow considerably.

The platform maintains a library of more than 100 trained models covering common components and defect types that can be immediately applied during onboarding which meant we weren't waiting months for custom model development before seeing value. For our standard distribution network equipment, the out-of-the-box detection coverage was broader than expected from day one.

Results can be exported as reports or pushed directly into enterprise systems via API, which matters for a utility operation where inspection findings need to flow into asset management and work order systems rather than sitting in a standalone platform. Review collected by and hosted on G2.com.

What do you dislike about Arkion?

The mixed experience reflects the gap between what the AI detection layer delivers and where the broader platform still needs to mature.

False positive management is the most consistent operational friction point. The detection models are impressive in coverage but occasionally flag findings that experienced inspectors would immediately dismiss — and at scale, even a modest false positive rate generates a meaningful volume of review overhead. The tooling for efficiently triaging, dismissing, and feeding back on false positives could be more streamlined than it currently is.

Custom model development for equipment types outside the standard library is available but the timeline and process can be slower than the pace at which operational decisions need to be made. If your network includes older, non-standard, or regionally specific equipment types, expect a gap period before detection coverage reaches the same confidence level as core components.

Custom development is reserved for unique equipment types or conditions outside the existing dataset which is a reasonable approach but means the onboarding experience varies significantly depending on how standard your infrastructure is. For utilities with heterogeneous networks built across several decades, that variability is real and worth planning for.

The reporting and analytics layer, while functional, could go deeper on trend analysis and predictive risk scoring across the asset base. Knowing a defect exists is valuable knowing which assets are trending toward failure and in what timeframe would be more valuable still, and that level of longitudinal insight isn't fully there yet.

Integration with some legacy enterprise asset management systems also requires more custom work than the API-first pitch implies. If your GIS and work management systems are older or heavily customised, budget for integration effort that the standard documentation doesn't fully account for. Review collected by and hosted on G2.com.

What problems is Arkion solving and how is that benefiting you?

Arkion's platform transforms inspection data into precise, actionable insights covering every asset, every component, and every mile of the network — moving grid operators from reactive to proactive asset management.

For a utility operator the core problem it addresses is scale and consistency. Manual inspection review doesn't scale, introduces assessor variability, and creates backlogs that delay maintenance decisions. Arkion compresses that entire pipeline data comes in, AI processes it, findings are prioritised, and the right people are looking at the right problems faster than any manual process we previously ran.

The platform achieved over 80% AI coverage of faults in distribution grids for clients, which in practice means a significant proportion of what would previously have required manual analyst review is now handled automatically with confidence freeing experienced engineers to focus on the complex edge cases and high-stakes decisions rather than routine image triage.

The shift toward condition-based maintenance rather than time-based or reactive maintenance is the longer-term operational benefit that's harder to quantify immediately but increasingly visible in how we're planning field crew deployment and capital expenditure.

Bottom line: Arkion is the most capable AI inspection platform we've evaluated for distribution grid defect detection and the underlying technology is genuinely ahead of the alternatives. The rough edges are real but they're the kind that come from a platform still maturing around a strong core, not fundamental product problems. Worth the investment if you go in with realistic expectations about integration effort and false positive management. Review collected by and hosted on G2.com.