An insurance CRM system shaped like a system of record, not a stack of silos.
An insurance operation runs on roughly fifteen systems: AMS, carrier portals, raters, claims platforms, e-signature, document storage, email, texting, telephony, finance, and the CRM supposed to tie it together. Strkr is the system layer underneath, a unified data model with account, policy, submission, and service-request as first-class objects, an open REST and webhook API that reaches the AMS and the carrier feeds, deterministic record merging that stops the duplicate-contact mess, and audit-ready event logs on every write.
Insurance operations directors and agency IT leads buy a new CRM every three or four years because the current one quietly degraded from a tool to a liability. The pattern is predictable. The CRM was bought for sales. The AMS kept the policy data. The service queue migrated to a shared inbox. Integrations were built by a departed consultant in a long-dead ETL tool. Three years in nobody can answer the question of where the authoritative account record lives. The pains below are the structural ones, the ones that drive IT leads to tear out a seven-figure stack and start again. They are not sales motion complaints. They are system-of-record complaints, and they get worse under scale because every new carrier appointment and every new team adds another seam the CRM did not plan for. If any of the six below look like your Monday morning, keep reading. The sections that follow show how Strkr collapses the data model, the integration surface, the record merging, and the audit discipline back into one architecture.
Policy data in six places
The policy lives in the AMS, the quote lives in the rater, the service note lives in Outlook.
A single commercial account has policies in the AMS, carrier quotes in the comparative rater, declarations pages in a shared drive, claim notes in the carrier portal, and service requests in a producer inbox. The CRM holds a contact record and a logo. When the account manager prepares for a stewardship meeting the picture gets rebuilt from six screens, and every quarter the rebuild misses something that becomes an uncomfortable client conversation.
No single system of record
When data disagrees, nobody knows which system wins.
The AMS says premium is 48,200. The CRM says it is 47,900. The commission statement says 48,500. The reporting pipeline picked one of them at random and the broker-of-record letter went out with the wrong number. The CRM was supposed to be the system of record for the account relationship, but the policy facts live elsewhere and nobody wrote down which direction the sync flows.
Brittle point-to-point integrations
The AMS to CRM sync broke twice this quarter and nobody noticed for a week.
A consultant built the AMS to CRM sync in 2024 with a cron job and a scratch database. It runs at 2 AM and nobody looks at the error log. When the AMS schema changed the sync kept running but stopped writing the X-date field. Three service renewals renewed late before anyone caught the drift. The CRM does not have an open API, so the fix was another consultant engagement and another layer of ETL glue on top of the existing one.
Duplicate records across lines
The same client has four contact records and three account records.
A commercial client bought personal auto for the owner, group benefits for the staff, cyber for the business, and workers comp for the operations. Each line of business got entered as a separate account by whichever producer wrote it. The CRM does not merge records deterministically, so the service team sends three separate renewal letters to the same email in April. Clients notice the chaos even when the agency does not.
Reporting that lies
The retention number on the board deck does not match the operations number.
The CRM dashboard says retention is 91 percent. The operations spreadsheet says 86 percent. The commission statement implies 88 percent. The gap comes from different definitions of what counts as a retained policy, different handling of mid-term cancellations, and different treatment of carrier-driven non-renewals. The CRM reporting layer picked one definition and never let it be refined. The leadership meeting debates the number instead of debating what to do about it.
System upgrades painful
Every CRM upgrade is a six-month project with a professional-services bill.
The current CRM shipped a major version in Q2 and the agency scheduled the upgrade for Q4 because the custom fields, the workflow logic, the integrations, and the saved views all have to be rebuilt against the new schema. The vendor professional-services bill was in the mid-five-figures. Any change to the data model, no matter how trivial, triggers the same multi-month cycle. The CRM has become unupgradeable in practice.
The data model
First-class insurance objects, not CRM contacts with a note field.
A CRM shaped for insurance has to carry the real primitives of the business as first-class objects, not as free-text custom fields bolted onto a generic contact record. Strkr ships with account, contact, policy, submission, carrier, service-request, claim-incident, commission-split, and document as native typed objects, each with its own fields, relationships, permission matrix, and timeline. The relationships are typed too, so a policy knows which account it belongs to, which carrier wrote it, which producer earned the credit, which CSR services it, and which service requests reference it. Every change to any field on any object writes to an append-only event log with timestamp, author, before/after values, and source system, so reporting can rebuild history and audit can answer the question of what the record looked like on any day in the past.
Account object
A single account carries every line of business the client holds.
The account object is the stable identity for the client, with hierarchy support for parent and child relationships (holding company and operating entities, family households, employer and employee populations) and typed fields for industry, segment, revenue band, address, and the client-service team assignment. Every policy, submission, service request, and claim links back to the single account. Reports roll up at any level of the hierarchy without a nightly batch job.
Policy object
Every in-force policy with carrier, premium, X-date, and commission split.
The policy object holds carrier, product line, policy number, effective date, expiration date (X-date), annualized premium, commission rate, split structure, named insureds, listed assets, declarations page attachment, and endorsement history. The policy auto-generates from the winning carrier quote at bind and carries the full lineage back to the submission, so twelve months later the renewal cycle starts with the original coverage comparison in reach.
Submission object
New business submissions with child quotes per carrier.
The submission object represents a quote-shopping cycle, with the client, the line of business, the segment, the writing producer, and the expected effective date as the parent record and the carrier quotes as child records. Each child quote carries carrier, premium, coverage limits, deductibles, binder-date requirement, and the raw quote PDF. At bind the winning quote flips to a policy record without re-keying a field.
Service request object
A native queue for inbound work tied to policy and account.
The service request object captures policy changes, certificate requests, ID cards, billing questions, claim incidents, cancellations, and endorsement requests with policy linkage, request type, priority, SLA timer, assignee, status, and resolution notes. The queue is a native object with routing rules, aging dashboards, and the same timeline discipline every other object carries.
Carrier object
Carriers as first-class with appointment status and appetite notes.
The carrier object tracks every market the agency works with: appointment status by line of business and state, direct-bill versus agency-bill flag, commission rate schedule, underwriter contacts, appetite notes updated by producers, and the current renewal retention rate. Policies and submissions link to the carrier, so carrier concentration and carrier-driven loss analysis roll up without a spreadsheet.
Claim incident object
Claims linked to the policy with status, reserves, and resolution.
The claim incident object captures date of loss, cause of loss, reporting date, carrier claim number, adjuster contact, reserves, payments to date, and resolution. Claims link to the policy and the account, so the account record carries the full loss history and the renewal review surfaces the loss ratio before the X-date conversation.
Event log
Append-only event log on every write, every object.
Every create, update, and delete on every object writes to an event log with timestamp, author, before and after values, source system, and request ID. The event log drives the audit trail, the reporting pipeline, the integration retry logic, and the point-in-time reporting. The agency can rebuild the state of any record on any historical date without a snapshot backup.
Integration surface
Open API, real webhooks, documented event schema.
An insurance CRM that cannot be integrated with is a liability. Strkr publishes a full REST API with OAuth2 and scoped tokens, a webhook subscription system with retry and dead-letter handling, and an event schema stable enough to drive downstream pipelines. The AMS integrations ship as native connectors with field-level mapping and conflict resolution. The comparative rater integrations ingest quote data directly as child quote records on submissions. The claims platforms push claim incidents through the webhook API. The document storage integrations route declarations pages, ACORD forms, and loss runs into the right policy and account records. Nothing in the integration surface requires a consulting engagement to light up.
REST API
Every object readable and writable through a documented REST API.
The REST API covers every object the UI covers, with the same permission matrix enforced at the token scope level. Developers at the agency, at the broker-of-record enterprise client, or at the integration partner read and write accounts, policies, submissions, service requests, claims, and documents without a vendor gate. Rate limits are published, pagination is cursor-based, and OpenAPI specification is downloadable.
Webhooks and events
Subscribe to any event, consume the typed payload.
Subscribe to events on any object: policy created, submission bound, service request opened or resolved, X-date approaching, commission statement loaded, claim incident reported. The webhook payload is typed, versioned, and documented. Retries follow exponential backoff, failed deliveries land in a dead-letter queue surfaced in the admin UI, and the full delivery history is auditable.
AMS connectors
Native connectors for AMS360, Applied Epic, HawkSoft, QQ Catalyst.
Native connectors sync accounts, policies, commissions, and documents between Strkr and the agency AMS with field-level mapping and conflict resolution rules. Agencies can run Strkr as the CRM layer with AMS as the policy module during a phased transition, or replace the AMS when the agency confirms Strkr covers their policy management needs. The sync is observable, with dashboards for last-run status, error rates, and conflict resolution outcomes.
Rater ingestion
EZLynx, PL Rating, Turborater quotes land as child records.
Comparative rater integrations pull quote data directly as child quote records on the submission. The producer runs the rater once, Strkr captures every carrier result with premium, coverage, and the raw quote PDF, and the submission detail has the full carrier-shopping history without a copy-paste step. Renewal cycle twelve months later starts with the full carrier comparison already on the record.
Carrier download
IVANS and direct carrier feeds for policy and claim data.
The carrier download integration ingests policy data (effective date, expiration date, premium, coverage, insured parties) and claim data (date of loss, status, payments) directly from the carrier through IVANS or direct API where available. The CRM reflects the authoritative carrier state without manual re-keying, and the sync is scoped to the agency book without pulling non-agency policies into the record.
Document storage
Declarations pages, ACORD forms, loss runs tied to records.
Document storage integrations with SharePoint, Google Drive, Dropbox, and native Strkr storage route declarations pages, ACORD application forms, loss runs, policy wording, and carrier audits to the right policy and account records. Documents are versioned, searchable by metadata, and surface on the account and policy timelines in chronological order with the author and source attached.
ETL and BI export
Push every event to Snowflake, BigQuery, or Redshift.
The event log can be exported continuously to Snowflake, BigQuery, Redshift, or a self-hosted warehouse through a managed sink, so the agency BI stack reads from a single durable source instead of a nightly CRM export. Dimensional modeling, retention reporting, and board-deck analytics run against the warehouse at the frequency the BI team sets, without hitting the production CRM.
Record merging and data quality
Deterministic merging, not fuzzy duplicate guesses.
Insurance operations accumulate duplicate records faster than any other industry because the same client enters the system through personal auto, commercial package, group benefits, workers comp, and cyber as separate submissions written by different producers over multiple years. A CRM that handles duplicates with a fuzzy-match wizard asking the user to pick which record wins is a CRM that will have ten thousand duplicates by year three. Strkr handles record merging deterministically, with explicit rules, explicit lineage, and an undo path for every merge. The data model treats account as the stable identity and lets policy, contact, and service history migrate between accounts with full audit history, so the agency can clean up legacy duplicates without losing a single historical fact.
Match keys
Explicit match keys per object, documented and configurable.
The admin defines match keys per object (tax ID plus state for commercial accounts, SSN last-four plus date of birth for personal accounts, policy number plus carrier for policies) and the system enforces them. New records that match an existing record on the key either merge automatically or surface a review queue with the match detail. The matching logic is deterministic and testable, not a black-box AI guess.
Merge with history
Merge accounts without losing a single historical field.
When two accounts merge, every policy, every submission, every service request, every claim, every document, and every timeline entry migrates to the surviving account with the full history intact. The event log records the merge as a reversible operation, and the agency can undo the merge with a single admin action if the merge turns out to be wrong.
Review queue
Likely duplicates surface in a queue, not a modal pop-up.
Likely-duplicate pairs that do not meet the automatic merge threshold surface in a review queue with the match detail (which fields matched, which did not, the record age, the policy count) and a one-click merge or dismiss action. The queue is a surface the data-quality team works through weekly, not a disruptive pop-up that interrupts the producer at bind time.
Lineage
Every merged record keeps its lineage visible.
The surviving account carries the lineage of every record that merged into it with the merge date, the merging user, and the source record ID. The lineage is visible on the account detail and in the API, so downstream systems that reference a merged record ID still resolve to the correct current account without a 404.
Bulk cleanup
Clean up 20,000 legacy duplicates in a weekend.
When the agency moves to Strkr with a messy legacy CRM, the data-quality team runs the matching engine against the imported records, reviews the proposed merges in bulk, and clears a decade of duplicates in a weekend. The merges are reversible, the audit trail is intact, and the agency starts the first operational Monday with a clean account book.
Contact deduplication
Household and employee populations collapse to a single record.
Contacts within an account household (family members on personal lines, employees on group benefits) deduplicate within the account scope so the same person does not appear six times with slightly different capitalisations of a name. The contact record carries the roles the person plays across the account (named insured on auto, dependent on health, primary signatory on cyber) without creating a separate contact per role.
Admin, security, and audit
Self-serve admin with real security primitives.
The IT lead at an insurance operation does not want to raise a vendor professional-services ticket every time the agency adds a custom field or changes a workflow. Strkr admin is self-serve for every surface the operations team touches, with the permission matrix enforced at every layer. The security primitives are real, not marketing claims. SSO integration, role-based access control, field-level permissions, record-level permissions, IP allowlists, session policies, and append-only audit logs ship on every paid tier. HIPAA compliant with BAA available, SOC 2 Type II in progress through 2026-Q4, GDPR and CCPA compliant. The audit log is queryable, exportable, and tamper-evident, so the quarterly compliance review is a filter and a date range, not a vendor data-request engagement.
Self-serve admin
Custom fields, Layouts, Flows, saved views without a vendor ticket.
Agency admins add custom fields, build Layouts, write Flows, configure saved views, and define service request types without filing a vendor professional-services ticket. The change takes effect the hour it is needed with an audit trail on who changed what. The quarterly vendor release cadence that legacy CRMs impose does not exist in Strkr.
Role-based access
Permissions scoped to role, team, and record ownership.
The permission matrix scopes read, write, and admin rights by role, team, and record ownership. A producer sees their own accounts and the shared agency book, a CSR sees the service queue for their assigned segment, a principal sees the full agency roll-up. Field-level permissions hide sensitive fields (SSN, bank account, loss reserves) from roles that do not need them, with no custom code required.
SSO and MFA
SAML and OIDC SSO with enforced MFA.
SAML and OIDC single sign-on integrates with Okta, Azure AD, Google Workspace, and Duo. MFA is enforceable at the tenant level and the role level, with session policies for timeout and concurrent-session limits. IP allowlists restrict access to agency office networks or VPN ranges where the IT lead requires it.
Audit log
Every write auditable with timestamp, author, and payload.
The audit log captures every create, update, delete, read of sensitive fields, login, and admin action with timestamp, author, source IP, user agent, and payload. Logs are tamper-evident and exportable to the agency SIEM. The quarterly compliance review runs the audit filter instead of filing a vendor data-request engagement.
Encryption and residency
Encryption at rest and in transit, with data residency control.
Data is encrypted at rest with managed keys and in transit over TLS 1.3. Residency is configurable per tenant, with US and EU regions available. Tenant-provided keys for additional envelope encryption are available on enterprise tiers. The security posture is published, auditable, and reviewed under the SOC 2 Type II engagement in progress through 2026-Q4.
Compliance posture
HIPAA, SOC 2, GDPR, and CCPA covered.
HIPAA compliant with BAA available for group benefits agencies handling protected health information. SOC 2 Type II in progress through 2026-Q4 for the SaaS controls environment. GDPR compliant for European client data with data subject request tooling in the admin UI. CCPA compliant with consumer request handling and opt-out signalling. The compliance team maintains a security portal with the current attestations.
Scale and performance
Shape the system for an agency, a regional broker, or an enterprise.
Strkr runs the same architecture for a four-producer agency and a 400-producer regional broker, with the same data model, API, and admin surface. The system scales horizontally with no architectural rewrite between the single-office installation and the multi-office multi-tenant installation. The performance targets are published: subsecond record load for accounts with fewer than 500 linked policies, under three-second load for the largest tenants, and 99.9 percent monthly availability under the enterprise SLA. The operations are observable: every slow query, every integration failure, every stuck Flow surfaces in the admin UI with a resolution path.
Horizontal scale
Multi-office brokers run on the same architecture as four-producer agencies.
The data model, the API, the admin surface, and the Flows engine are identical across the smallest tenant and the largest tenant. A broker growing from 15 to 150 producers over three years does not re-platform. Office-level roll-ups and office-level permissions ship as native primitives, not custom-code bolt-ons.
Multi-tenant isolation
Tenant data isolated at the storage layer, not just the query layer.
Every tenant is isolated at the storage layer with per-tenant encryption keys and query-time scope enforcement. Noisy-neighbor incidents in one tenant do not affect another. The multi-tenant architecture passes the SOC 2 Type II controls on tenant isolation and the GDPR requirements on cross-tenant data access prohibition.
Observability
Slow queries, failed integrations, stuck Flows surface in the admin UI.
The admin observability dashboard shows the slowest queries of the last 24 hours, the integration delivery statuses, the Flow execution histogram, and the queue backlog of service requests and renewal tasks. The IT lead sees the system health in one view instead of filing a vendor support ticket to ask why a Flow stopped firing.
Load tests
Published performance targets and continuous load testing.
Performance targets are published: subsecond record load for accounts with fewer than 500 linked policies, under three-second load for the largest tenants, under 500ms API p99 latency on common endpoints. Continuous load testing in the production environment verifies the targets, and regressions trigger alerts before customers notice.
Enterprise SLA
99.9 percent monthly availability with incident credit.
The enterprise SLA commits to 99.9 percent monthly availability for the core CRM surface, with 99.5 percent for integrations and 99.9 percent for the API. Downtime below the target triggers automatic service credits per the SLA schedule. The status page shows current and historical availability per surface.
Disaster recovery
Point-in-time restore, cross-region backups, documented RPO and RTO.
Point-in-time restore to any second in the last 35 days ships on every tier. Cross-region backups land in a geographically separate region with a tested restore procedure. The documented recovery point objective is under one minute, the recovery time objective is under one hour. The disaster recovery test runs quarterly and the result lands in the security portal.
Head-to-head
Strkr system layer vs legacy AMS plus manual integrations.
Legacy AMS vendors shipped in an era when integrations meant a nightly batch file and a filing cabinet of ACORD forms. The architecture is still that architecture, with a modern UI painted over it. Point-to-point integrations between the AMS, the rater, the carrier portals, the e-signature tool, and the CRM accumulate until nobody at the agency can draw the diagram without a whiteboard and a coffee. Strkr collapses the system layer into one architecture with first-class insurance objects, a documented API, native integrations, and audit discipline that was baked in from the first commit, not bolted on in the fifth year.
Feature
Strkr
Legacy AMS + manual integrations
System of record
Single architecture, policy and account on one platform
AMS for policy, CRM for contact, nothing for truth
HIPAA, SOC 2 Type II in progress 2026-Q4, GDPR, CCPA
Varies, often extra module cost
Upgrade cycle
Continuous deploy, no version migrations
Major-version upgrade projects every 12 to 18 months
BI and warehouse export
Continuous event stream to Snowflake, BigQuery, Redshift
Nightly CSV dump, schema drift between runs
How teams use Strkr
How insurance operations teams run Strkr as the system layer.
The patterns below show up at insurance operations teams moving from a legacy AMS plus CRM plus consultant stack to Strkr as the unified system layer. The common thread is architectural discipline, with insurance primitives as first-class objects, documented integrations, and audit log on every surface. Each playbook describes a real migration motion.
Regional broker migration
150-producer regional broker moves off AMS plus Salesforce stack.
A 150-producer regional broker running Applied Epic for AMS and Salesforce for CRM migrated to Strkr as the system layer. Data model mapping took six weeks, data cleanup and merge ran another four weeks, and the production cutover ran over a Thursday night in a single eight-hour window. The agency landed on one system with one bill, one admin surface, and 42,000 cleaned-up accounts replacing 61,000 duplicates from the legacy stack.
Phased AMS replacement
Agency keeps AMS for policy module during 18-month transition.
A 25-producer multi-line agency moved to Strkr as the CRM and workflow layer first and kept AMS360 for the policy module for an 18-month transition. The AMS connector synced policies and commissions bidirectionally with field-level mapping. Over the 18 months the agency validated Strkr policy management covered the AMS features they actually used, decommissioned AMS360, and consolidated to the single Strkr system layer.
Carrier download rebuild
Operations director replaces consultant-built IVANS sync with native connector.
An operations director inherited a consultant-built IVANS carrier download that ran at 2 AM and silently dropped the X-date field three months before the audit. Switching to the Strkr native IVANS connector reduced the sync failures to zero over the first quarter, surfaced the drift history in the admin observability dashboard, and freed the consultant retainer budget for the agency to spend on actual improvements.
BI pipeline consolidation
Broker consolidates nightly CSV extracts into continuous event stream.
A regional broker running a BI pipeline that pulled nightly CSV extracts from the CRM, the AMS, and the commission system into Snowflake switched to the Strkr event stream export. The pipeline moved from nightly batch to continuous near-real-time, dimensional modeling simplified against the typed Strkr event schema, and the board deck analytics ran on data that was less than 10 minutes old instead of a day stale.
Compliance audit response
HIPAA audit answered from the Strkr audit log in a day.
A group benefits agency facing a HIPAA audit on protected health information handling answered the audit from the Strkr audit log in a single day. The audit scope asked for a six-month trail of every read, write, and admin action on PHI-flagged fields. The agency filtered the audit log by date range, field type, and action type and produced the full packet. The BAA with Strkr covered the business-associate relationship. The audit closed with no findings.
See the insurance CRM system shaped as a real system of record.
Start a 14-day trial with the full system layer enabled: first-class account, policy, submission, service-request, carrier, and claim objects, open REST API with OpenAPI specification, webhook event system with retries and dead-letter queue, native AMS and rater integrations, deterministic record merging with reversible merge history, self-serve admin for custom fields and Flows, and tamper-evident audit log on every write. HIPAA compliant with BAA available, SOC 2 Type II in progress through 2026-Q4, GDPR and CCPA compliant. The pricing page lays out the per-seat line in full.
What does Strkr mean by a system of record for insurance?
A system of record is the authoritative source for a given fact about a given entity. In insurance that means the agency can point to one place and say this is where the policy premium lives, this is where the X-date lives, this is where the commission split lives, and the other systems reflect that source. Strkr acts as the system of record for the agency relationship layer: accounts, policies, submissions, service requests, claims, commission splits, and documents. Where the agency keeps a separate AMS for policy management, Strkr integrates bidirectionally with field-level mapping and conflict resolution so the authoritative source is explicit for every field, not inferred by whichever system wrote last.
How does the Strkr API handle rate limits and pagination?
The Strkr REST API publishes rate limits per token (standard tier starts at 300 requests per minute, enterprise tier scales to 3,000). Pagination is cursor-based with a stable sort order so clients can resume long-running syncs without reprocessing records. Bulk endpoints handle batch reads and writes with up to 500 records per call. The OpenAPI specification is downloadable, the sandbox tenant is available on request, and the webhook subscription system handles push notifications for clients that need near-real-time rather than polling. All of this is covered by the enterprise SLA on API availability at 99.9 percent.
How does Strkr handle schema evolution when the agency adds custom fields?
Custom fields are first-class in Strkr. The admin adds a custom field through the admin UI with a typed field (text, number, picklist, lookup, date, boolean, rich text) and the field is immediately available in the API, the webhook payload, the Layouts, the Flows, the saved views, and the warehouse export. The agency does not re-key the data model, re-build integrations, or request a vendor release. The event log records the schema change and the integrations that subscribe to the object see the new field in the next payload with a documented field metadata reference.
What does the AMS migration path look like?
The migration from a legacy AMS runs in two shapes depending on the agency goal. First shape, phased replacement: the agency keeps the AMS for the policy module during a 12 to 18 month transition and runs Strkr as the CRM and workflow layer with the AMS connector handling bidirectional sync on policies, commissions, and documents. Second shape, full replacement: the agency moves all policy management to Strkr in a single cutover window, typically six to twelve weeks of data mapping and merge work followed by a weekend cutover. The right shape depends on which AMS features the agency actively uses and whether the group has the appetite for a single-cutover motion.
How does Strkr handle data residency for European or Canadian clients?
Residency is configurable per tenant. US tenants land in US regions, European tenants land in EU regions with GDPR-compliant data handling, Canadian tenants have access to Canadian residency on request for the enterprise tier. Cross-region replication is explicit and auditable. The tenant data isolation guarantees that data stored in one region never crosses regions without an explicit admin opt-in. The data processing addendum covers the sub-processor list and the breach notification obligations in the applicable regulatory framework.
Can Strkr support an enterprise broker with multiple business units?
Yes. Multi-office and multi-business-unit brokers run on the same Strkr architecture as single-office agencies, with office-level and business-unit-level permissions, roll-ups, and segmentation as native primitives. The permission matrix scopes access by office or business unit, dashboards roll up at any level of the hierarchy, and inter-office record visibility is explicit, not accidental. The enterprise tier adds SSO integration, IP allowlists, custom SLA terms, and dedicated technical account management for the operations team.
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