Built for teams tired of busywork

A CRM with automation that respects the audit trail and the exit path.

Automation inside a CRM separates teams that scale from teams that re-hire ops leads every year. Strkr Flows is a native node-and-edge automation engine with inline documentation, versioning, replay, and per-tenant feature flags. Zero third-party iPaaS fees for the common patterns.

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What this audience is actually dealing with

The pains that bring buyers here.

Automation inside a CRM comes in three flavors: workflow builders (point-and-click triggers plus actions), scheduled jobs (nightly batch work), and iPaaS integration (connecting to external tools). Legacy CRMs often ship one of the three cleanly and bolt the others on. Below are the common pain patterns that show up when the automation story is thin.

Workflow archaeology

Nobody can explain why flow 47 fires at 2 AM.

Workflows accumulate across years and admins. A new ops lead opens the flow list and finds 150 flows with names like Copy of Jim's stage change v2 and no documentation. Strkr requires in-line documentation on every flow at save time, with a required business-logic field and a change-log entry per edit. The flow list stays knowable rather than becoming archaeology.

Silent failure

The round-robin flow stopped last week; nobody noticed.

A critical flow that stops firing silently is a common CRM failure mode. The sales floor finds out when a rep complains about unfair distribution. Strkr monitors flow execution with alerts on execution-count anomalies, pattern-break detection, and admin notifications when a flow has not fired in its expected window. The silent-failure gap closes to minutes rather than weeks.

No replay or dry-run

Testing a flow means breaking real data.

Many CRMs lack a dry-run mode for flows, so admins test on real records and hope the side effects are reversible. Strkr Flows ships a dry-run against test records with full logging, replay against historical events for debugging, and a staging preview tenant for structural changes. The test-driven workflow development model works for admins, not just engineers.

External iPaaS pricing

Zapier and Make bills exceed the CRM subscription.

Teams that build their automation story on external iPaaS (Zapier, Make, Workato) often end up with an iPaaS bill that exceeds the CRM subscription. Strkr Flows handles the common patterns natively (round-robin, stale alerts, cross-tool webhook, lifecycle advancement) so the external iPaaS line goes away for 80 percent of the use cases. The remaining 20 percent still runs on iPaaS; the budget balance shifts.

Automation runs with elevated privileges

A flow quietly bypasses the permissions model.

Legacy workflow engines run as a system account with broader permissions than any user, which creates a quiet privilege-escalation path. Strkr Flows can run under the triggering user's claims or a scoped service account; either way the execution is logged in the audit trail and the privilege scope is explicit rather than hidden.

No versioning

A flow edit cannot be rolled back cleanly.

When an admin edits a flow and the change breaks downstream, the typical rollback is a manual revert from memory. Strkr Flows versions every save with a diff view and one-click rollback to any prior version. The audit log captures who edited what when, and the staging preview catches most mistakes before they land in production.

What Strkr Flows actually ships

The automation primitives the ops lead gets on day one.

Strkr Flows is a native node-and-edge automation engine. The cards below are the primitives available on day one for a mid-market team. Each primitive is a flow node that composes with the others; the ops lead builds complex automation by composing simple building blocks.

Event triggers

Record change, time schedule, inbound webhook.

Flows trigger on record events (contact created, deal stage changed, task completed), scheduled time (daily at 9 AM, every Monday, hourly), inbound webhook (external system calls Strkr to fire), and conditional composition (fire when A and B both true in trailing 7 days). The trigger surface covers the shape of most sales-ops automation.

Filter and branch

Conditional logic without a scripting language.

Filter nodes gate downstream actions on field values, time windows, and record relationships. Branch nodes split flow execution into parallel paths based on conditions. Loop nodes iterate over related records. The logic surface is readable without a scripting language; complex cases that genuinely require code route to a code node with sandboxed JavaScript.

Record action

Create, update, delete records with validation.

Action nodes create, update, and delete records in the tenant. Validation runs on the action, so a flow cannot write invalid data that a user-facing form would reject. Mass-record actions respect bulk-update performance targets (sub-5-minute on 10,000 records). Delete actions log in audit and respect retention policies.

Communications action

Email, SMS, Slack, calendar invite.

Communications actions send email, SMS (via Strkr Messaging), Slack messages, and calendar invites with native integration. Email actions respect per-tenant sender configuration and deliverability posture. SMS actions respect consent and DND policies. Slack actions post to configured channels with record-link URLs. Calendar actions create invites with the right ICS format for Google Calendar and Microsoft 365.

External webhook

Call out to external systems with authentication.

Webhook actions call external HTTP endpoints with configurable authentication (bearer token, basic auth, HMAC signature). Response handling supports success-conditional branching and error-retry policies. The webhook action is the pattern that replaces 80 percent of external iPaaS usage for cross-tool handoff.

Code node

Sandboxed JavaScript for complex logic.

Code nodes run sandboxed JavaScript when the flow logic exceeds what filter and branch nodes handle readably. The sandbox prevents external network access except through the webhook action, limits execution time to prevent runaway logic, and logs the full input and output per execution. The code-node escape hatch keeps the readable flow surface available for 90 percent of cases without blocking the 10 percent that need real logic.

AI alongside the flow

Strkr AI on the records your flows touch.

Strkr AI runs on the records your flows reach (fit score, buyer-intent score, forecast risk signals, email draft inside the composer, flow description polish). A flow that triggers on closed-won can route into an approval wait step before firing the kickoff email, with the Strkr AI draft already saved on the deal for the admin to review. First-class AI step types in the flow DAG are a pre-launch build in progress; today AI composes with flows through records and the inbox composer.

Approval node

Human in the loop before an action.

Approval nodes pause flow execution and request approval from a specified user or role. The approver sees the context in-app or via email and approves or rejects. The flow resumes based on the decision. Approval nodes handle the compliance-sensitive patterns (sending communications on behalf of a departed rep, deleting records, exposing data to external systems) that should not run fully autonomously.

Error handling

Retry, dead-letter, and alert on failure.

Every flow has configurable error handling: retry policies with exponential backoff, dead-letter queues for persistent failures, and admin alerts on failure patterns. Flows do not fail silently; the ops lead sees the failure in the admin inbox within the SLA window. For critical flows the admin can configure stricter error policies.

Automation patterns teams build on day one

The 8 flows every mid-market tenant deploys first.

The flows that pay back fastest are known. The cards below are the 8 patterns that ship as starter templates on every new Strkr tenant, with the typical time saving per team.

Round-robin lead assignment

New leads routed by territory, segment, availability.

The round-robin pattern assigns new leads to reps based on territory, segment, and availability (respecting OOO and workload balance). The pattern replaces the manual-assignment pattern that most SMBs run before they install automation. Typical time saving: 2-3 hours per week for the sales manager.

Stale deal alerts

Deals untouched for N days surface in the manager view.

The stale-deal pattern flags deals that have not received an activity update in a configurable number of days (typically 7-14 days by stage). The pattern replaces the Monday pipeline-review surprise with a running list the manager addresses daily. Typical pipeline freshness lift: from 7-day average staleness to 2-day average.

Closed-won Slack post

The sales floor celebrates in real time.

The closed-won pattern posts to Slack when a deal moves to closed-won with the deal size, rep, and summary. The pattern replaces manual Slack posting (which reps often skip because they are focused on the next deal). Sales-floor morale and visibility compound; managers describe it as the single most-noticed starter flow.

Lifecycle stage advancement

Contacts graduate from MQL to SQL automatically.

The lifecycle pattern advances contacts through MQL, SQL, opportunity, customer stages based on configurable triggers (first demo booked, first proposal sent, first revenue). The pattern replaces manual stage advancement and keeps the lifecycle data consistent across the team. Marketing attribution math gets honest.

Lost-reason prompt

A deal moving to lost prompts for the reason inline.

The lost-reason pattern requires the rep to select a reason from a configured list when moving a deal to lost. The reason powers the win-loss analytics the exec team reads monthly. The pattern replaces the empty-lost-reason data problem that undermines most pipeline analytics.

Onboarding kickoff

Closed-won triggers the handoff flow.

The onboarding pattern fires on closed-won and creates the kickoff tasks, assigns the CSM, drafts the welcome email, surfaces the sales context on the new service view. The pattern replaces the ad-hoc handoff that leaks context in the sales-to-service transition.

Renewal pipeline creation

Customer accounts get renewal deals 90 days out.

The renewal pattern creates a renewal opportunity on each customer account 90 days before the contract end date, assigns the AE or CSM, and schedules the first renewal conversation. The pattern replaces the manual renewal-tracking spreadsheet that most mid-market teams run before installing automation.

Expansion signal routing

Usage spikes surface in the AE view.

The expansion pattern integrates with product analytics (Mixpanel, Amplitude, PostHog) via webhook and surfaces usage spikes on account records. The AE sees the expansion signal with the usage context attached, which gates the expansion outreach to accounts with actual signal rather than cold expansion attempts.

Cross-tool data sync

Billing data flows into the account view.

The sync pattern integrates with the billing system (Stripe, Chargebee, NetSuite) and surfaces MRR, invoice status, and payment health on the account view. The pattern replaces the manual check Stripe for payment status workflow that reps run before every renewal conversation.

The automation limits

What Strkr Flows will not do.

Strkr Flows covers the common sales-ops automation patterns deeply. For some workloads an external iPaaS or a dedicated workflow platform is the right tool. Below are the limits we want to be straight on.

Not a general-purpose iPaaS

Zapier and Make still have more connectors.

For integrations with less common third-party tools, Zapier and Make ship more pre-built connectors than Strkr Flows. For common integrations (Slack, Stripe, Gmail, Microsoft 365, Zoom, Mailchimp, Mixpanel) Strkr ships native actions. For long-tail integrations the pattern is webhook action plus a lightweight connector service, which handles the long tail at the cost of some setup work.

Not a BPM platform

Complex multi-week processes need a dedicated tool.

For true business process management (multi-stakeholder, multi-week processes with heavy state machine logic), a dedicated BPM platform (Kissflow, Pipefy, process-specific vertical tools) handles the complexity better. Strkr Flows handles sales-ops automation and the subset of adjacent patterns that fit; teams running BPM-shaped processes should pick a BPM tool and integrate with Strkr via webhook.

Not a data warehouse ETL tool

Fivetran and Hightouch own the data pipeline.

For heavy data warehouse ETL and reverse-ETL workloads, Fivetran and Hightouch are the right tools. Strkr Flows can sync records at modest volume via webhook action, but true warehouse-scale ETL is not the design target. Strkr integrates with the common warehouse tools via API so the ETL flows live where they belong.

Not a marketing automation platform

Marketo and HubSpot Marketing Hub own nurture.

For heavy marketing automation (nurture sequences at scale, lead scoring across millions of events, complex multi-channel journeys), a dedicated marketing automation platform (Marketo, HubSpot Marketing Hub, Customer.io) is the right tool. Strkr Flows handles the sales-side automation and integrates with the marketing platform via webhook for lead handoff.

Not a scheduling engine

Calendly and Chili Piper own meeting scheduling.

For meeting scheduling with buyer self-service, round-robin lead-to-rep matching, and calendar availability, Calendly and Chili Piper are purpose-built. Strkr integrates with these via API so the scheduled meeting lands as an activity on the correct deal and contact record. The scheduling flow stays in the dedicated tool.

Not a rule engine for pricing

CPQ tools own quote math.

For configure-price-quote math (product configuration, pricing rules, discount approval chains), a dedicated CPQ tool (Salesforce CPQ, Qwilr, DealHub, PandaDoc) handles the specialized rule complexity. Strkr Flows can trigger the CPQ workflow and surface the quote on the deal record; the pricing math lives in the CPQ.

Head-to-head

Strkr Flows vs Salesforce Flow for mid-market automation.

Salesforce Flow is the most-cited mid-market CRM automation tool, which makes it the natural comparison. Here is the head-to-head on dimensions that matter for the ops lead running the automation story.

Feature Strkr Salesforce Flow
Native node types Trigger, filter, branch, action, webhook, code, AI, approval, error handling Trigger, decision, action, loop, assignment, callout
In-line documentation requirement Required at save time with business-logic field Optional description
Dry-run and replay Dry-run against test records plus replay against historical events Debug run; limited historical replay
Versioning Every save versioned with diff view and one-click rollback Flow versions; roll back to prior version
Permission context Run as triggering user or scoped service account; audit logged Run as default context or specific user
External webhook Native action with authentication and retry Apex callout or Named Credential
AI drafting inside flows Strkr AI on records, approval gates in flows Einstein actions, Enterprise tier
Monitoring and alerting Execution-count anomaly detection with admin alerts Flow execution reports; manual alert setup
Pricing basis Included on every paid tier Platform limits by edition
Learning curve for ops lead Readable node-and-edge surface without scripting Flow syntax plus Apex escape hatch
How teams use Strkr

How teams run Strkr Flows today.

The patterns below are motions that are live on customer accounts right now. The thread across them: teams that invested in the automation story early saw compounding returns on ops-time savings and pipeline hygiene.

48-rep B2B SaaS, 24 active flows

Automation saved 32 ops-lead hours a week.

A 48-rep B2B SaaS team runs 24 active flows across the common patterns (round-robin, stale alerts, closed-won posts, lifecycle advancement, lost-reason prompts, renewal creation, onboarding, expansion signals). The ops lead measured 32 hours a week of avoided manual work across the sales team. The flows do not run perfectly on day one; the ops lead ran a 90-day optimization cycle to tune the trigger conditions and action parameters. Steady state is cleaner than the pre-automation baseline on every pipeline hygiene metric.

32-rep agency, outsourced ops

Outsourced ops provider manages flows across clients.

A 32-rep agency with outsourced ops uses Strkr Flows to standardize across its client base. The outsourced ops provider ships a flow pack per client type (SMB SaaS, mid-market services, agency, DTC) and tunes per client. In-line flow documentation means the provider's team can hand off between consultants without archaeology. The provider's revenue per client increased 30 percent through productized automation offerings.

24-advisor RIA, compliance-sensitive flows

Approval nodes gate every outbound communication.

A 24-advisor RIA runs flows with approval nodes gating every outbound communication to clients. The compliance officer reviews each approval request before the communication fires. The dual-record audit log captures the request, approval, and sent message for regulatory review. The approval pattern replaced a shared-mailbox review flow that was slower and less auditable.

18-rep agency, cross-tool handoff

Webhook actions replaced Zapier for 12 of 15 integrations.

An 18-rep agency had been running Zapier for 15 cross-tool integrations with the typical escalating iPaaS bill. The move to Strkr Flows migrated 12 of 15 integrations to native webhook actions. The remaining 3 (specialty long-tail integrations) stayed on Zapier. The agency's iPaaS bill dropped 85 percent and the integration posture became more auditable because the webhook actions log in Strkr audit.

60-rep mid-market, approval chain for discounts

Discount approval runs through the flow approval node.

A 60-rep mid-market team runs discount approvals through a flow approval chain. The AE submits a discount, the manager approves at the first threshold, the VP approves at the second, the CFO approves at the third. The approval chain respects role boundaries, logs every decision in audit, and feeds the quarterly discount-analysis report. The old pattern (email approvals, lost in threads) retired completely.

A CRM where automation respects the audit trail and the exit path.

Start a 14-day trial with 9 starter flows active (round-robin, stale alerts, closed-won posts, lifecycle advancement, lost-reason, onboarding, renewal, expansion, cross-tool sync). Tune to your workflow.

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Common questions

What buyers in this bucket ask most.

How does Strkr Flows compare to Zapier or Make for cross-tool integration?

Zapier and Make ship more pre-built connectors to third-party tools (thousands of integrations each) than Strkr Flows does natively. For common integrations (Slack, Stripe, Gmail, Microsoft 365, Zoom, Mailchimp, Mixpanel, major CRM destinations) Strkr Flows ships native actions. For the long tail of third-party tools, the pattern is webhook action in Strkr plus a lightweight connector service, which handles the integration at the cost of some setup work. Teams that start on Zapier typically migrate 80 percent of their flows to Strkr Flows natively and leave the long-tail 20 percent on Zapier, which cuts the iPaaS bill proportionally without blocking any specific integration.

Can non-engineers build and maintain flows in Strkr?

Yes. The flow editor is a visual node-and-edge surface that reads as a flow chart. Filter, branch, action, and webhook nodes are configurable through forms rather than code. The code node is available when the logic genuinely requires it, but 90 percent of mid-market automation fits the no-code surface. The in-line documentation requirement means every flow has an explanation that a non-engineer admin reads to understand the business logic. New ops leads reach productive flow development in 2 to 4 hours of hands-on practice; by month two most ops leads are shipping flows weekly.

What happens when a flow breaks or produces unexpected results?

Three safety nets. One: the dry-run against test records catches most logic errors before the flow runs against live data. Two: the staging preview tenant runs the flow against production-schema data before the flow ships to production. Three: the execution-anomaly detection monitors live flows and alerts the ops lead when execution counts or patterns break. If a flow does produce unexpected results, the replay feature re-runs the flow against the historical trigger events for debugging. The one-click rollback restores the prior flow version. The combined safety net catches the vast majority of issues before they produce user-visible impact.

How does Strkr handle permissions and audit for automated flows?

Flows run under one of two permission contexts: the triggering user's claims (default) or a scoped service account configured by the admin. The chosen context is logged in every execution. The audit log captures the trigger, the flow version, the execution outcome, and every record change the flow made with user attribution. The compliance officer can trace any automated change back to the flow that made it, which is the posture that satisfies regulated-industry audit requirements. Compare to legacy workflow engines that run as a system account with elevated privileges and no per-execution audit; Strkr avoids the quiet privilege-escalation path.

What is the pricing for Strkr Flows?

Strkr Flows is included on every paid Strkr tier with no per-execution or per-flow metering below the plan cap. The plan cap is sized to cover the typical mid-market workload (thousands of executions per hour across dozens of active flows); for teams that approach the cap we discuss higher-tier plans or custom contracts. The pricing posture reflects the thesis that automation is table stakes for a mid-market CRM; metering it per execution would discourage the pattern (automate everything that can be automated) that produces the ops-time savings. For teams running very high-volume flows, we document the per-tenant cap and the upgrade path.

How does Strkr AI interact with Flows cost math?

Strkr AI is an opt-in add-on module priced at 49 dollars per month flat with 5,000 Strkr AI calls included and 0.012 per call after that. Strkr AI handles complexity routing automatically so simple drafts and deeper reasoning do not pay the same cost per call. Flows run the deterministic business logic (CRUD, email send, SMS send via Strkr Messaging, Slack, webhooks, waits, approval gates), and Strkr AI runs on the records those flows touch (fit score, intent score, forecast risk signals, inbox draft). First-class AI step types inside the flow DAG are a pre-launch build in progress. The cost posture (predictable module, visible usage line) is the differentiator for teams that want automation plus AI without a budget surprise.

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