What does robotics data mean for insurers?
For insurers, robotics data is evidence. Underwriting an autonomous fleet or a warehouse robot deployment requires proof of how the system performs across the scenarios that drive claims, captured with records that show where, when, and how the data was produced. The same capture discipline also trains the AI that inspects property damage from drone imagery and 3D scans.
Claims data is already shaping how autonomy is judged. A Swiss Re study of Waymo's 25.3 million fully autonomous miles found an 88% reduction in property damage claims and a 92% reduction in bodily injury claims compared with human drivers.
What that means: insurers now have a template for evaluating autonomy with evidence. Most robotics companies don't have Waymo's mileage, so they need a different kind of evidence: structured scenario data an underwriter can trust.
Who we work with in insurance
Insurers and MGAs covering autonomy
Carriers and specialty underwriters writing coverage for autonomous vehicles, AMR fleets, and robot deployments who need independent performance evidence.
Insurtech teams building inspection AI
Companies training models to assess roof, facade, and property damage from drone imagery and 3D scans.
Robotics companies seeking coverage
Teams that need to show underwriters structured evidence of safety and reliability before a policy is written.
Risk engineering groups
Teams that assess sites and facilities and want repeatable 3D and aerial capture instead of one-off walkthroughs.
| Use case | What must be shown or learned | Data we collect | Services |
|---|---|---|---|
| Underwriting autonomous fleets | Performance in the scenarios that drive claims | Scenario-targeted evaluation sets with held-out cases | Edge case |
| Property claims inspection AI | Hail, wind, and water damage across roof types | Drone imagery and 3D scans across materials, damage levels, and light | Aerial, point cloud |
| Risk engineering | Site layout, hazards, change over time | Repeatable aerial and 3D site capture registered to control points | Point cloud, aerial |
| Incident data review | What the system sensed and did | Synchronized multimodal logs with calibration records | Multimodal |
Evidence-Grade Data: five tests an underwriter can apply
Gamasome framework
Most robotics datasets are built to train models. Insurance needs data that can also withstand scrutiny. Evidence-Grade Data is the standard we use when data has to support a decision about risk.
If you can't show how the data was made, it isn't evidence. It's an anecdote with a file size.
Provenance
Every file records sensor, calibration, time, location, and operator, so any result can be traced back to how it was captured.
Scenario coverage
Evaluation data is organized by the scenarios that drive losses, not by convenience, with coverage reported per scenario.
Independence
Evaluation sets are held out from training and, where needed, collected separately from the team that built the system.
Reproducibility
Scenarios are documented well enough to be rerun on the next software version, so performance can be compared over time.
Claims realism
Inspection datasets include the damage types, materials, and conditions adjusters actually see, not just clean examples.
What evidence for an underwriter looks like
Composite scenario: coverage for an AMR fleet (details generalized)
- Situation
- A robotics company deploying AMRs in retail backrooms needs liability coverage for a multi-site rollout.
- Problem
- The underwriter asked for evidence of how the robots behave around people and in cluttered aisles. The company had demo videos and uptime stats, but nothing structured.
- Solution
- Build a scenario-based evaluation set: people stepping into paths, blocked aisles, low light, and dropped items, recorded across three pilot sites, with provenance on every run and a held-out site.
- Outcome
- A performance report organized by scenario, backed by traceable data, that the underwriter can compare against the next software version.
For the underwriter, the value is a repeatable way to price robot risk. For the robotics company, it's a faster path to coverage.
Common mistakes with robotics evidence
Treating demo videos as evidence
Curated clips show what went right. Underwriters need to know what happens in the cases that go wrong.
Evaluating on training data
If the test scenarios were in the training set, the results say little about real-world risk.
Clean-only inspection data
Damage models trained on clear-sky, fresh-damage images miss weathered roofs and subtle hail hits.
No version tracking
Performance changes with every software release. Evidence has to be tied to the version it describes.
Structured test reports come from our validation and testing service. For vehicle autonomy specifics, see our automotive and trucking pages.
Robotics data for insurance FAQs
How can robotics data help insurers underwrite autonomy?
Scenario-based evaluation data shows how an autonomous system performs in the situations that drive claims, with provenance records that let an underwriter trust and compare results across software versions.
What is evidence-grade data?
It's data with traceable provenance, organized by risk-relevant scenarios, held out from training, reproducible on new versions, and realistic enough to reflect actual claims conditions.
Do you collect data for property damage inspection AI?
Yes. We capture drone imagery and 3D scans across roof and facade materials, damage types, and lighting, planned around the resolution the model needs.
Can robotics companies use this to get insurance coverage?
Yes. A structured, scenario-based performance report backed by traceable data gives underwriters something concrete to evaluate, which can shorten the path to coverage.
Is your evaluation independent from the robot developer?
Evaluation sets can be collected separately from training data and from the team that built the system. The level of independence is agreed during scoping.
What's the evidence behind autonomy reducing claims?
A Swiss Re study of Waymo's 25.3 million autonomous miles found an 88% reduction in property damage claims and a 92% reduction in bodily injury claims compared with human drivers.