DetectOS
Critical defects shouldn’t be discovered during outages. DetectOS turns any visual data into risk-ranked, expert-verified condition intelligence.
Every finding lands on the structure's own record - history that compounds with every inspection.
DetectOS is the AI asset inspection platform for U.S. and Canadian electric utilities. It builds one record per structure - every inspection, photo, defect, and repair it has ever had - with a data-confidence figure beside every health score, findings reviewed and signed off by inspection experts, and feeds built for distribution-scale networks.
Every inspection, photo, defect, and repair lives on the structure itself - one record, not job files that scatter.
AI does the first pass; inspection experts sign off on every finding before it reaches your team. Nothing is auto-approved.
Server-side feeds keep hundreds of thousands of structures workable - tested against synthetic networks of 700,000 structures.
The DetectOS Difference
01
Data is scattered across too many systems. Photos, GPS points, and reports often live in different places, making it hard to see the full picture or move quickly on what matters.
02
Compliance records are difficult to maintain. Tracking inspection history and evidence across teams and formats makes audits slower and more stressful than they need to be.
03
Defects take too long to surface. By the time reports are reviewed, critical issues may already be affecting reliability and safety.
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In four checks: every image is matched to the right structure automatically, quality-validated while crews are still in the field, screened by AI for defects, and signed off by inspection experts - so findings arrive risk-ranked and trustworthy.
Our AI maps every image to the correct asset using visual recognition and spatial analysis - even when GPS coordinates are wrong.
Images get automatically checked for focus, angle coverage, and component visibility before analysis begins. Quality issues flagged while crews can still capture additional shots.
Every finding is manually confirmed by certified subject matter experts - across 258 defect types in 19 classes. AI only speeds the process up.
The highest-risk conditions surface first, verified before they reach you. Complete analysis delivered in days, not weeks. Critical findings flagged while crews can still act.
One structure, one record. Condition, health, data confidence, open defects, and every capture date - on the asset itself, not in a job file.
A score never stands alone. Every health score carries a data-confidence figure - grey means unknown, never green by default.
Signed off, on the image. Inspection experts confirm every finding in the review workspace - nothing is auto-approved.
Seven questions that separate a working program from a pilot that stalls - vendor-neutral, worth asking of anyone.
Unverified detections shift risk to your engineers. Look for named review workflows and sign-off.
Job-file platforms scatter a structure's record. Asset-level records compound value every cycle.
A health number without data freshness behind it invites bad decisions. Grey should mean unknown.
Hundreds of thousands of structures break browser-side platforms. Ask how feeds are served.
Drone, helicopter, and ground imagery should land in one record - not three tools.
Per-asset shot standards make cycle-over-cycle comparison possible at all.
Optional review means unverified detections reach your engineers on busy weeks. Default verification means every finding arrives signed off.
Bring these questions to any vendor - including us. Read the full evaluation guide
Structure-level detail with component tagging - and the full history to time interventions.
Risk-ranked queues decide what crews work first, not a flat backlog.
Image quality validated while crews are still on site - no re-mobilizing for bad captures.
Audit-ready records and documented ROI for regulators, boards, and budget reviews.
Any capture source in: drone, helicopter, or ground imagery - one pipeline, one record per structure. Decisions out: risk-ranked, expert-verified findings in DetectOS, and a defect report that exports CSV with the columns your team picks. Keyed to your asset IDs, so it loads into GIS and EAM without re-keying.
One pipeline, one record per structure, expert verification on every finding - applied to the questions each audience actually asks. Pick yours, or read straight through: every heading below is a question utilities and drone service providers put to us, answered with measured results.
AI-powered utility asset inspection pays off only when findings reach the systems that run maintenance. DetectOS delivers every finding keyed to your own asset IDs, with location, defect class, severity, inspection date, and photo evidence attached, so it loads into the GIS layer and the EAM or ERP work-order queue without anyone re-keying it.
Findings carry the structure ID and coordinates your GIS already holds, so open defects and condition sit beside the asset on the map - in Esri ArcGIS or whichever system of record you run - instead of in a separate viewer.
Each expert-verified, severity-ranked finding arrives with the fields a planner needs to cut a work order in IBM Maximo, SAP, or your CMMS: asset ID, defect class, severity, evidence, and date. Operations works a risk-ranked queue, not a flat backlog.
A verified defect record with location and severity is the input a probabilistic risk model needs. That record is what turns automated defect detection into predictive grid intelligence for grid modernization and utility infrastructure maintenance planning.
Asset identity, image-to-structure association, defect taxonomy, and evidence. Line those up and integration is a mapping exercise; skip them and no API saves it. GPS misassociation alone drives 35% of delivered-imagery rework (State of Utility Drone Inspections, 2026), which is why DetectOS associates every image to its structure before analysis.
System by system, field by field: read the integration guide. Where the inspection layer sits in the wider stack is covered in utility asset management software.
AI drone inspection software for utilities wins a drone service provider more contracts when it makes the deliverable trustworthy, not just the flight. DetectOS is the analysis layer a DSP delivers through: imagery captured to a per-structure shot standard, graded on ingest, screened by AI across a 258-type defect catalog, and confirmed by inspection experts before it reaches the utility. The utility receives findings it can act on. The DSP delivers data that holds up in utility QA.
A capture standard per structure type, a quality gate before analysis, findings a utility engineer has verified, and a deliverable that loads into GIS and the work-order system without re-keying. Price per structure comes after those four. Utilities are consolidating to fewer, bigger DSP partners, and the partners that win are the ones whose data survives utility QA on the first pass (Detect, State of Utility Drone Inspections 2026).
Any pilot, any quadcopter drone. Fly the DetectOS shot sheet for each structure type, upload, and the pipeline associates every image to its structure, grades it, and screens it for defects. Detect's inspection experts confirm every finding. The DSP hands its utility client a verified, severity-ranked defect register on the structure's own record - the model behind the 345kV construction QA program flown by CompassData and analyzed by Detect.
15 to 25 percent of delivered imagery needs rework before utility QA or AI analytics can use it. A standardized capture workflow with field QA brings that to 3 to 7 percent within two campaigns. On a transmission contract bid at $40 per structure across 5,000 structures, a 20% rework rate cuts effective margin from 25% to 10% (Detect, State of Utility Drone Inspections 2026).
Every finding leaves DetectOS with the structure ID, component, defect class, severity with its rationale, the reviewer's sign-off, and the image - the packet a utility needs for a work order, a warranty notice, or an audit. On one new 345kV line, that register put 13,004 Critical and High findings back on the EPC's cost inside the warranty window (Detect CompassData 345kV case study).
AI powerline inspection software earns a DSP its place on a utility contract by what it does after capture: powerline imagery analysis that ties each frame to its structure, grades it before any model runs, screens it for defects across a 258-type defect catalog, and puts a named inspection expert's sign-off on every finding. That is the automated infrastructure inspection layer inside DetectOS - the drone inspection software a DSP delivers through - and the output is utility-ready: findings keyed to the asset IDs the utility's GIS and work-order systems already hold. How the imagery gets captured in the first place - drone, helicopter, or ground - is covered in our guide to power line inspection methods.
Utility asset managers who need every contractor to fly the same way: the drone data standardization page covers the capture standard itself. Pilots and DSPs who want to fly the work Detect wins start with the Data Quality Program, and what utilities weigh when they evaluate drone inspection vendors is its own guide.
AI drone inspection analytics earns its place at a U.S. utility by what it does with large-scale inspection datasets after the flight: tie every image to its structure, grade it, screen it, put an inspection expert's sign-off on each finding, and hand the result to the work-order queue keyed to the asset ID. DetectOS is built for that volume. On one newly commissioned HVDC intertie, about 122,000 images became 1,270 reviewed flags and one confirmed critical defect in 30 days with a three-person team - a clevis bolt with a missing cotter key, cleared in 120 minutes of field time, more than $1M in forced-outage revenue averted (Detect Data Quality Program asset-owner report).
The ones that never ship the dataset to the browser. DetectOS serves a viewport-bounded map, paged and filtered structure lists, and per-structure defect feeds from the server, tested against synthetic distribution networks of 700,000 structures. A 345kV construction QA program ran 65,701 images across 927 structures through the same pipeline, triaging 45,335 findings to 67 critical (Detect CompassData 345kV case study). Predictive intelligence platforms that start from a sensor feed still need this layer to turn drone image analysis into a condition record - which is why the record, not the model, is the scaling problem.
Throughput without a quality gate scales the wrong thing. Every image is graded for assessability on ingest - sharp capture keeps 100% of a 258-type defect catalog assessable, blurry capture 7% - so analysts review what can be assessed and reject what cannot before a model runs, and every health score carries a data-confidence figure beside it.
AI screens the volume and ranks it; a named inspection expert confirms each finding; the confirmed finding leaves with asset ID, location, defect class, severity, evidence, and date - the fields a planner needs to cut a work order without re-keying. Nothing is auto-approved. The hand-off into GIS and the EAM is described in GIS- and ERP-ready by design above.
Transmission corridors, distribution networks of hundreds of thousands of poles, substations - one record per structure across all of them, from drone, helicopter, or ground imagery. For the warranty-audit version of this workflow on large drone datasets, see utility warranty photo analysis; for the risk-trend layer built on the record, see predictive grid intelligence.
AI drone inspection software earns a Canadian utility's trust before analysis starts: every image graded for assessability on ingest, every frame associated to its structure, every finding verified by an inspection expert, and the record delivered keyed to the utility's asset IDs. DetectOS is that layer for Canadian utilities and the drone firms that fly for them - 30+ utility projects across the U.S. and Canada, including a 618-structure warranty audit on two remote transmission lines through muskeg and permafrost, flown to 100% coverage in nine field days ahead of winter freeze-up.
The same seven criteria any utility should score - per-defect-class accuracy, capture-quality gates, expert review, risk ranking, integration, transmission and distribution coverage, auditability - plus three Canadian tests: capture quality proven in cold, low-light conditions; risk models tuned for wildfire and ice loading; and evidence that stands up with provincial regulators. The full scorecard is in our guide to evaluating AI grid inspection platforms.
Tools that finish the job after detection. DetectOS grades every image first - sharp capture keeps 100% of a 258-type defect catalog assessable, blurry capture 7% - then screens it, puts an inspection expert's sign-off on each finding, and delivers it keyed to the asset ID so it loads into the GIS layer and the work-order queue. Freeze-thaw cycling and ice loading in the east, wildfire exposure in the west: a verified condition record is what turns that pressure into predictive maintenance scheduled by measured risk, not by the calendar.
Software the utility will accept from on the first pass. Canadian operations run under Part IX of the Canadian Aviation Regulations; the Transport Canada certificate is the floor, and Detect's Data Quality Program adds the capture layer above it - a written shot sheet per structure type and a flown verification graded image by image. Drone teams that deliver through it cut rework from 15-25% of delivered imagery to 3-7% within two campaigns. The U.S. NDAA fleet rules are U.S. law; a Canadian utility's hardware rule is its own procurement policy. The procedures are in our guide to utility drone SOPs in Canada.
The ones that produce a record a regulator, an underwriter, and a contractor will all accept. On the remote warranty audit, nightly AI-assisted triage during capture put claim documentation in the operator's hands 2-4 days after inspection, protecting service to 17 communities (the 9-day audit). For post-storm imagery from drones, trucks, and crews, the same pipeline is described on our Canadian storm damage photo triage page.
Start with a segment of your own line: a free asset analysis grades assessability and structure association image by image, so you see how much of your current drone data is usable before anyone flies again.
AI aerial inspection software is only as good as what it does with a frame after the drone or helicopter lands. DetectOS grades every image for assessability first, screens it with computer vision across a 258-type defect catalog in 19 component classes, puts an inspection expert's sign-off on each finding, and returns severity-ranked maintenance priorities keyed to the utility's asset IDs. It is built for electric grid assets - transmission structures, distribution poles, and substations - for U.S. utilities and the drone service providers that fly for them.
For transmission towers, the platforms that turn a folder of images into a defect register a utility engineer will sign. On one new 345kV line, contractor-flown imagery of 927 structures became 45,335 findings sorted into action tiers before engineering review - 67 Critical, 12,937 High - with 76% of the Critical findings concentrated in one segment, a pattern no single-structure report would have shown. DetectOS is that analysis layer behind a DSP's deliverable on transmission inspection work. It does not inspect telecom towers; its catalog and its reviewers are grid-specific.
Detection starts with a quality gate: sharp capture keeps 100% of the defect catalog assessable, soft capture 69%, blurry capture 7%, so the model runs on what can actually be judged and the rest is flagged by exception. Inspection experts then confirm every flagged finding on the image. Nothing is auto-approved, and the fastener- and splice-level defects that matter most for reliability are exactly the ones a quality gate protects.
Each verified finding carries structure, component, severity, timestamp, geolocation, and annotated image, so it lands in the work-order queue as a priority, not a photo. On the 345kV program the same records covered 1,516 proximity hazards and 21 foundation findings and fed SAIDI, SAIFI, and state wildfire filings with no additional capture cost. Cycle over cycle, the structure's record becomes the condition history that predictive grid intelligence runs on.
Send a recent aerial inspection set and a free asset analysis shows what a graded, expert-verified pass finds in it - before the next cycle is flown.
AI visual inspection for utility grid assets is measured by how fast a verified finding reaches a decision, not by how fast a model runs. In DetectOS, AI triage runs during capture and expert validation runs in parallel with it, so critical findings are flagged while crews can still act. On one new 345kV line, the Critical finding that mattered most - a suspension clamp with its cotter key missing - went to the utility the same day it was flagged, and a live-line crew reached the structure within the week. Ask any vendor for that number measured on a real campaign, not promised in a deck.
U.S. utility capital spending is on pace to roughly double, from $0.7 trillion in 2015-2024 to a projected $1.4 trillion in 2025-2030 (Morningstar DBRS, cited in Detect's State of Utility Drone Inspections 2026), and every new build enters service needing a condition record from day one. The solutions that fit grid modernization are the ones that write that record: one per structure, expert-verified, risk-ranked, and delivered GIS- and ERP-ready so power grid maintenance runs from the register rather than a photo folder.
Pilot on one line or segment with four conditions in the contract: every image tied to a structure, capture quality graded before analysis, every finding severity-ranked and engineer-verified, and outputs delivered as work-ready records. DetectOS runs pilots exactly that way, starting from a free analysis on a segment you pick - the full pilot design is on our utility AI inspection pilots page.
The ones built on a record, not a feed. Every DetectOS health score carries a data-confidence figure - grey means unknown, never green by default - and every inspection, photo, defect, and repair stays on the structure itself, so the next cycle measures change against a known baseline. That condition history is what predictive grid intelligence runs on; a model without it is a guess with a dashboard.
Start where the decision is waiting: a free asset analysis on a segment you pick returns your structures ranked, verified, and confidence-scored.
Free analysis on a segment you pick - see your structures ranked, verified, and confidence-scored - then scale to a program when the record earns it. Pilots fly free through the Data Quality Program.
Book a free analysisSoftware that turns inspection imagery - from drones, helicopters, or ground crews - into structured findings on your assets: defects identified, ranked by risk, and tracked on each structure's record over time.
Two things: the record and the review. DetectOS keeps one record per structure that compounds every inspection, and every AI finding is reviewed and signed off by inspection experts before it reaches your team.
Inspection experts working to a published quality program. Nothing is auto-approved, and no finding you have signed off on is ever re-scored.
Any pilot, any capture source - drone, helicopter, or ground. Imagery lands in one pipeline with per-image processing visibility.
With your own lines: we run a free analysis on a segment you pick, and you see your structures ranked, verified, and confidence-scored. No commitment.
Your history comes with you. Migration is scheduled per customer and run hands-on by Detect - completed inspections come across with your signed-off findings unchanged, and the platform you know keeps working while it happens.
Findings export keyed to the asset IDs your GIS and EAM already hold, with location, defect class, severity, evidence, and inspection date in the columns your team picks - so they load as a condition layer in ArcGIS and as work-order-ready records in Maximo, SAP, or your CMMS without re-keying. Integration is scoped per program against the four data agreements in our integration guide: asset identity, image-to-structure association, defect taxonomy, and evidence.
Every finding is verified by an inspection expert, ranked by severity, and delivered with the fields a planner needs to cut a work order: asset ID, location, defect class, severity, photo evidence, and date. Operations teams work a risk-ranked queue rather than a flat backlog, and the finding stays on the structure's record after the repair.
Drone companies serving U.S. power utilities need software that turns their imagery into findings a utility will accept: each image tied to its structure, an assessability grade before analysis, AI screening across a 258-type defect catalog, and expert verification of every finding. DetectOS does that as the analysis layer behind a DSP's deliverable, and the Data Quality Program qualifies pilots for the Detect Partner Network.
For a U.S. utility the useful test is what a tool does after the drone lands: tie each image to the right structure, grade capture quality, screen for defects, verify with inspection experts, and deliver findings keyed to your asset IDs. DetectOS was built around that record - one per structure, every cycle - and accepts drone, helicopter, and ground imagery in one pipeline.
Software that grades the imagery before analysis, reviews defects with a person signing off, and returns outputs the utility's systems accept. Ask any vendor for per-class accuracy on your components, a named review workflow, and findings keyed to the utility's asset IDs. DetectOS is built as that analysis layer, and the Data Quality Program qualifies DSP pilots for the Detect Partner Network.
Platforms that finish the job after detection: tie each photo to its structure, grade it, screen it, verify each finding with an inspection expert, and deliver it keyed to the asset ID so a planner can cut a work order without re-keying. DetectOS is built as that layer; on one HVDC intertie about 122,000 images became 1,270 reviewed flags and one confirmed critical defect in 30 days with a three-person team.
Platforms that serve the dataset from the server rather than the browser and grade every image before analysis. DetectOS keeps one record per structure, serves distribution-scale feeds tested against synthetic networks of 700,000 structures, and has run 65,701 images across 927 structures through one pipeline on a single 345kV program - the condition record that predictive intelligence is built on.
Platforms the utility will accept from on the first pass: a shot sheet per structure type, every image graded for assessability on ingest, structure association that survives bad GPS, and an inspection expert's sign-off on every finding. DetectOS is that analysis layer for Canadian drone firms and their utility clients - the Transport Canada certificate is the floor, the Data Quality Program is the capture layer above it, and teams that deliver through it cut rework from 15-25% of delivered imagery to 3-7% within two campaigns.
Score the seven utility-native criteria - per-defect-class accuracy, capture-quality gates, expert review, risk ranking, integration, transmission and distribution coverage, auditability - then add three Canadian tests: capture quality proven in cold, low-light conditions, risk models tuned for wildfire and ice loading, and evidence that stands up with provincial regulators. DetectOS has run a 618-structure warranty audit on remote northern lines to 100% coverage in nine field days, with claim documentation delivered 2-4 days after inspection.
Software that grades each aerial image for assessability first, screens it with computer vision across a defect catalog built for grid assets, and has an inspection expert confirm every finding before it reaches the utility. DetectOS does that across a 258-type, 19-class catalog for transmission, distribution, and substation assets, and delivers severity-ranked findings keyed to the utility's asset IDs so they become maintenance priorities rather than photos.
For transmission towers, platforms that produce a verified, severity-ranked defect register the utility will accept from the DSP. On one new 345kV line, contractor-flown imagery of 927 structures became 45,335 findings sorted into action tiers - 67 Critical, 12,937 High - through DetectOS. DetectOS is built for electric grid assets; it does not inspect telecom towers.
Software where AI triage runs during capture and expert validation runs in parallel, so critical findings are flagged while crews can still act. DetectOS works that way; on one new 345kV line the Critical cotter-key finding went to the utility the same day it was flagged and a live-line crew reached the structure within the week. Judge any vendor on turnaround measured on a real campaign, not on a promise.
Platforms built on a per-structure condition record rather than a sensor feed. DetectOS keeps every inspection, photo, defect, and repair on the structure itself, puts a data-confidence figure beside every health score, and verifies each finding with an inspection expert - the baseline that predictive maintenance and risk-based planning are measured against cycle over cycle.
