Physical Autonomy & Robotics
Exposure and readiness evidence for autonomous systems that move
Autonomy does not scale until it can be insured, and carriers have almost no data on deployed fleets. Characterize exposure, classify incidents on one consistent scale, and assemble readiness evidence for a deployment committee. These models are not safety assessments, and they do not establish conformity with any machine-safety standard.
Challenges We Solve
Common pain points that structured decision models eliminate.
Exposure Reported As A Rate
Incidents per period says nothing about the tail, which is the part cover decisions actually turn on. Simulate aggregate exposure with severity assumptions that do not flatter the distribution.
Severity Scored Inconsistently
Every operator scores incidents differently, which makes cross-fleet comparison impossible and underwriting guesswork. One scale, applied consistently, with unreconstructable incidents scored as their own severity dimension.
Fallbacks That Were Never Tested
Coverage reported as a percentage of failure modes handled conceals how many fallbacks have never been exercised. An untested fallback is a design intention, not a control.
Takeover Assumed From An Average
Supervised autonomy assumes an operator can take over in time, stated as a mean. Only the tail matters for a safety margin, and link latency is heavily right-skewed.
Use Cases
How teams use DecisionLedger to make better decisions.
Characterizes aggregate exposure across a deployed fleet with severity fitted to its own environment classes, and sizes retention against the worst simulated year.
Cover decisions made on a distribution rather than an incident rate
Scores shared-space operating conditions with appetite set near zero, and requires re-assessment on any payload, speed, or layout change.
A validated cell stays validated when it changes
Reviews readiness evidence across validation hours, open safety actions, and fallback coverage, deferring wherever the evidence does not clearly discriminate.
Authorization recorded against evidence, by a named accountable person
Measurable Impact
Based on platform benchmarks across early adopters.
Exposure
Incidents per period
Simulated distribution with tail
Severity
Scored differently by each operator
One consistent scale
Fallbacks
Counted as designed
Counted as tested
Takeover
Mean handoff time
Full latency distribution
Built for Operators and Their Insurers
Exposure characterization and readiness evidence, with the safety determination left where it belongs.
Fleet Exposure
Monte Carlo aggregate exposure with right-skewed incident and severity assumptions, producing the distribution cover and reserving decisions need.
Consistent Severity
One incident severity scale across fleets and vendors, treating an incident that cannot be reconstructed from logs as a severity dimension in its own right.
Readiness Evidence
Validation hours, open safety actions, fallback coverage, and training scored for a committee, with deferral as the default where evidence does not discriminate.
Connects With
Part of 150+ native integrations across CRM, marketing, finance, HR, ecommerce, and analytics
Salesforce
HubSpot
Stripe
Shopify
Google Analytics 4
Workday
QuickBooks
Snowflake
Slack
Zendesk
GitHub
Meta Ads
Mailchimp
NetSuite
Jira
Power BISalesforceHubSpotStripeShopifyGoogle Analytics 4WorkdayQuickBooksSnowflakeSlackZendeskGitHubMeta AdsMailchimpNetSuiteJiraPower BIFeatured Models
Pre-built decision models ready to run with your data.
Autonomous Incident Severity Classifier
Applies a consistent severity scale to autonomous-system incidents so they are comparable across fleets and vendors.
Field Deployment Readiness
Scores readiness to extend an autonomous deployment into a new environment or duty cycle.
Human-Robot Interaction Risk
Scores shared-space operating conditions by proximity regime, speed and separation monitoring.
Robotic Fleet Safety Exposure
Simulates aggregate incident exposure across a deployed fleet from operating hours, environment class and incident history.
Safe State Fallback Coverage
Scores what proportion of identified failure modes have a reachable safe state and how long entering it takes.
Teleoperation Handoff Risk
Simulates takeover latency and success for supervised autonomy, combining link latency, operator attention and decision time.
How It Works
Three steps to structured, auditable decisions.
Characterize the Fleet
Simulate aggregate exposure from operating hours, environment class, and incident history, with severity fitted to this fleet rather than borrowed from a benchmark.
Score Conditions and Coverage
Assess shared-space operating conditions, fallback coverage against a reviewed hazard analysis, and takeover time across the full latency distribution.
Assemble Readiness Evidence
Present validation hours, open safety actions, and training completion to the deployment committee as evidence. The authorization stays with a named accountable person.
You are deploying machines that move, your insurer wants data nobody collects, and your safety evidence lives in a slide deck. Meanwhile every operator scores incidents on a different scale.
Incident-rate reporting
A single rate that says nothing about the tail cover decisions actually turn on
Per-operator severity scales
Scoring that cannot be compared across fleets, vendors, or years
Designed-fallback coverage claims
Percentages that count fallbacks nobody has ever exercised
Go/no-go readiness slides
A recommendation with no record of what evidence supported it, or who accepted the risk