Know the risk you are carrying. Then reduce it.
The risk you underwrote stays the risk you underwrote. You see how every agent on the book actually behaves instead of estimating it, and the exposure comes down, because the unlawful action does not execute. Telematics made motor risk writable the same way.
What the carrier gets
One installation at the insured. Assess the risk, price it correctly, reduce it.
Agents drift. New tools, new markets, a prompt change on a Tuesday, and the fleet is doing something nobody underwrote. Aegis checks each action while cover is live, so the exposure you priced is the exposure you still hold at renewal.
Not a questionnaire and not a category prior. What their agents really do, how often they come near a line, which duties they touch, at that company specifically. An actuarial file for a class that has none.
A guardrail that provably runs on every action lowers frequency, and a loss that does happen arrives with its own evidence. You are not only measuring the risk. You are shrinking it.
Telematics did this for motor insurance.
A telematics box is a real and ordinary thing: a small device the insurer requires in the car, which records how it is actually driven and reports back. Progressive's Snapshot in the United States, black-box policies in the UK, Ituran in Israel. Motor insurers did not wait for drivers to improve. They fitted the box, and it cut the losses and produced the rating data at the same time.
The box, in motor
- Fitted at the insured, required by the insurer as a condition of cover.
- Cut losses directly, because behaviour changes when it is measured.
- Turned driving into data an underwriter could rate on.
- Made cover available on risks that were declined or loaded blind.
Aegis, in agent risk
- Installed at the insured, required by you at bind on the risks that warrant it.
- Cuts losses directly, because the unlawful action does not execute.
- Turns agent behaviour into per-insured data you can rate and re-rate on.
- Makes a class writable that most of the market is currently excluding.
Before Aegis you price a guess. After it, you price the behaviour.
On day one you are still writing this class on thin data, and Aegis does not change that. What it gives you at the start is a control you can condition cover on, and an assessment of what the insured's agents can actually do. The data comes after. Once it is running you are looking at the real risk as it happens, which is when the price gets accurate and the exposure starts coming down.
Your wording, checked on every action.
A policy is written once. The agent changes every week. Aegis reads the wording you issued, compiles it into rules, and tests every action against them while the cover is live.
The insurer shall not be liable for any claim arising out of automated communications sent without a record of consent.
The insured shall give written notice of any circumstance within 72 hours of discovery.
Coverage territory: the United States and Canada.
A sublimit of $250,000 applies to regulatory proceedings.
- consent.recordrequired before any automated message, or the loss falls outside cover
- notice.window72 hours from discovery of a circumstance
- territoryoutside US and CA, the action is uncovered
- sublimit.regulatory$250,000, escalate before exposure passes it
One model update can hit your whole book at once.
Your insureds are running agents on the same few models. That makes their losses arrive together, not independently, and nobody in this market can currently measure how much of it they are holding.
The share of your written exposure running on a single model or vendor. That number exists nowhere today.
The same failure across unrelated insureds in the same window is a systemic event, not a bad week.
If one layer blocks the action at every insured, the event that reaches the world is smaller than the one that started.
Start with one book.
The useful first step is small: take a class you already write, require Aegis on the agent-exposed risks inside it, and watch what comes back for a quarter. You will know within weeks how often those agents come near a line, and whether the control holds. That is a decision made on your own data rather than on ours.