Policies, renewals, and claims on one timeline.
Insurance revenue is mostly retention, and retention is mostly whether someone reached out before the renewal date got close. This pack models policies and renewals as real objects so that work is scheduled by the system rather than remembered by a person.
What lands in your workspace on day one.
Not a template gallery — objects with real relationships, a pipeline with real stages, and automations already running.
- Policytype, carrier, premium, effective/expiry dates, coverage limits
- Renewalpolicy, due date, quoted premium, status, owner
- Claimpolicy, date of loss, status, adjuster, amount
- Householdmembers, linked policies, total premium, retention risk
- Quotecoverage requested, carriers quoted, premium, decision date
Rename, reorder, or retire any stage. Add a second pipeline for a different motion.
Automations, not suggestions.
These are live the moment the pack installs. Every one is editable, and every run keeps a step-by-step trace.
Renewal runway
Renewal tasks and outreach begin 90, 60, and 30 days out with the current premium and claims history already on the record — not a week before expiry.
Cross-sell prompts
A household with auto but no home cover surfaces as a live segment, and the prompt reaches the right producer with the context attached.
Claim check-ins
An open claim triggers scheduled updates to the client, because silence during a claim is what loses the renewal a year later.
Lapse prevention
A missed payment or an unreturned renewal call escalates through channels rather than being noticed at expiry.
The problems this pack was shaped around.
Retention is a calendar problem
Every book has renewals that lapsed because nobody called in time. When the renewal is an object with a date, the outreach is an automation with a trigger — and “we got busy in March” stops being a category of lost revenue.
The household, not the policy, is the relationship
Modelling policies without the household that holds them is why cross-sell is guesswork. With both modelled and related, total premium per household, coverage gaps, and retention risk are filterable fields rather than a report someone builds by hand once a year.
A record of who said what, when
Regulated conversations need an audit trail. Every message across every channel lands on the record timeline with a timestamp and an author, including anything AI drafted or sent, and consent state is tracked per channel.
The same platform, whatever you sell.
A pack is configuration on top of one shared data model, one filter engine, and one automation engine. That is why you can change any of it without losing the parts that work.
Insurance, answered.
Can this integrate with carrier systems?
Through the API and imports. Policies can be created and updated programmatically, so a nightly sync from your agency management system keeps records current while the relationship layer lives here.
How do you handle sensitive client data?
Field-level permissions restrict who can read sensitive fields, all access is audited, and data is encrypted in transit and at rest. See the security page for specifics.
Can producers see only their own book?
Yes. Row-level permissions are enforced by the query engine, so the same views and dashboards scope automatically by producer, team, or agency.
Does the AI ever give coverage advice to clients?
Not unless you configure it to, and we’d advise against it. Most agencies use AI for first response, scheduling, document summarisation, and drafting for human approval — with anything advisory reviewed by a licensed producer.
Start with the insurance pack already installed.
Free workspace, the objects and automations above set up at onboarding, and every price published. Customise from something that works.
Free trial · no card required · every price published