Workflow automation that lives in the cloud, right next to the record it changes.
Strkr runs the whole automation layer as a managed cloud service. No queue server to patch, no agent to install on a VM, no middleware tier to buy. Build a flow in the designer, publish it, and the engine runs it against live CRM data in the same tenant that holds your pipeline, your contacts, your messaging history, and your reports. Billing is per seat, not per task, so a busy quarter does not become a surprise invoice.
Hosted, patched, monitored, and already connected to your record.
A cloud-based workflow automation platform is more than a web page in front of an older engine. The real version runs on infrastructure you never see, upgrades on a schedule you never manage, scales on load you never provision, and connects to your business data without a bridge host sitting on a laptop at your office. Strkr ships all four and reads from the same database that holds the pipeline, the account book, and the messaging ledger. Build in the browser, deploy in a click, and watch the run history on the same screen where the flow is defined.
Managed infrastructure
The platform runs. You configure.
Strkr operates the queue, the worker pool, the retry layer, the audit log, and the scheduler. Nothing lives on a server inside your network, nothing needs a VPN tunnel, nothing goes down because a corporate laptop went to sleep. Patches, security updates, and database vacuums happen on the Strkr side without a ticket, a change window, or a weekend from your team.
Multi-tenant isolation
Shared platform, isolated data.
Every tenant runs on the same hardened platform with strict row-level isolation at the database layer. Your flows see only your records. Your run history is tagged with your tenant id on every row. The platform scales the shared cost of operations across every customer while keeping the data boundary clear and auditable.
No agent install
The flow runs where the data lives.
Nothing to install on a developer machine. Nothing to run on a company laptop. The flow engine is a service inside Strkr that reads and writes CRM records directly. Call an outside API, hit an inside endpoint, write a record, send a text through Strkr Messaging. All of it runs inside the same managed service with no client-side component.
Native data connection
Zero sync lag with the CRM.
The engine queries the same Postgres database that renders the pipeline. A flow that fires on a deal update sees the write milliseconds after it lands. There is no sync job, no change-data-capture middleware, and no staging table between the record and the automation. The feedback loop between change and action is as tight as the database transaction allows.
Browser-only authoring
Build it from any machine, any OS.
The designer is a web app. Open it from a laptop, a Chromebook, a tablet in a meeting. There is no desktop installer, no Chrome extension, no node runtime to match on a developer workstation. A new admin can productively build flows on their first morning without a one-day setup slog.
Elastic scale
A thousand runs a minute if the day calls for it.
The worker pool scales horizontally on the Strkr side. A Monday morning spike of inbound leads triggering routing flows does not back up a queue. The platform spins up more workers inside the shared infrastructure, bills the capacity against the shared seat price, and keeps SLAs green during traffic surges you did not plan for.
Observability built in
Every run, every step, in a log you can read.
Each flow run writes a step-by-step execution log with inputs, outputs, timing, and the record it touched. When a flow misbehaves, an admin opens the run, reads the trace, and sees exactly which step produced the wrong output. No third-party APM stack, no stdout tail on a server, no mystery.
Private per tenant
Credentials and secrets you alone see.
API keys, OAuth tokens, and third-party credentials are stored encrypted per tenant with KMS-backed envelope encryption. A flow references the secret by name. No one on the Strkr side can read your production credentials, and the audit log records every read and rotate against the credential vault.
Backups and rollback
A version history on every flow definition.
Every save to a flow produces a versioned snapshot. Roll back to any prior version in a click. Diff two versions to see what changed. The automation layer carries the same change-management discipline you would expect from source control, inside the designer, with no external repo to sync.
The flow designer and the triggers
A visual builder on top of real primitives.
The designer is a graph. Triggers on the left, actions on the right, with conditions, branches, delays, and loops wiring them together. Every node has a type, a schema, and a config panel. Nothing is a black box. The complex flows that run the business are the same shape as the simple flows a first-time admin builds on a Tuesday afternoon. One language for the whole platform means a senior admin can read any flow a junior admin produces without a translation layer.
Record triggers
Create, update, delete, status change.
A flow can start on any record event in the CRM: contact created, deal updated, account tier changed, invoice marked paid, ticket resolved. The trigger carries the full record in the payload. The flow reads the fields it cares about and routes on them. No separate change feed, no polling, no missed events during a maintenance window.
Schedule triggers
Cron patterns, business days, time zones.
Fire a flow every weekday at 9 AM in each rep's local time zone. Fire a flow on the first business day of the month. Fire a flow every ten minutes during business hours. The scheduler understands business calendars, holidays, and user-level time zones, so a nightly job for a global team actually hits each region at the right local hour.
Webhook triggers
An inbound HTTP endpoint per flow.
Need an external system to kick off a flow? Enable the webhook trigger and the flow gets a unique signed URL. POST a JSON payload to the URL and the flow starts with that payload in context. The signature validates the sender. The audit log records every inbound call. The webhook ships without a middle tier.
Form triggers
A Strkr form submission kicks the flow.
A Strkr Lead Capture form submission can start a flow directly. The payload carries the form fields, the hidden UTM parameters, the referrer, and the device signal. Route the lead, enrich the record, send a confirmation email, and create a task on the owner in one definition.
Message triggers
Inbound SMS or email starts the automation.
An inbound SMS reply through Strkr Messaging, an inbound email on a shared mailbox, or an inbound call recording landing on an account can all serve as flow triggers. The platform treats conversation events as first-class citizens, not an afterthought, so a reply or a missed call runs the right follow-up without a human noticing.
Manual triggers
A button on the record, in the UI.
Some flows are better run on demand. The designer can expose a flow as a button on a record page, scoped by permission. A rep clicks "Prep for QBR" on an account and the flow runs. No separate app launcher, no second URL to remember, no gap between the record and the action.
Branching logic
If-else, switch, filter, with real schema help.
Branches read the current flow state. Pick a field with the picker (schema-aware), pick a comparator, enter a value. The designer validates the comparison at build time so a type mismatch does not slip into production. Nested branches, switch statements with many cases, and early-return filters all compose cleanly.
Loops over collections
Iterate a query, run a sub-flow per row.
Pull a list of accounts matching a filter and run a sub-flow for each one. The loop node parallelizes workers up to a configured concurrency so a 500-account loop does not take an hour. Each iteration gets its own context, its own retry budget, and its own log entry.
Delays and SLA timers
Wait five minutes, or wait until Monday 9 AM.
A delay node pauses the flow for a fixed interval, a business-hour duration, or a specific calendar moment. SLA timers measure elapsed time against a target and fire an escalation branch when the timer expires. The engine persists the paused state so a nine-day delay survives a platform restart without a dropped step.
The action library on day one
Enough primitives to build the motion in an afternoon.
A cloud automation platform earns its seat the day an admin finishes a flow without opening a feature request. Strkr ships a broad action library out of the box: CRM reads and writes, messaging, email, documents, HTTP calls, assistant prompts, task creation, approvals, and dashboard writes. The actions all share a configuration pattern, so the second action a new admin configures feels familiar. Composite actions like "Create lead and route it" come packaged for the common motions, and custom composite actions can be saved by an admin for reuse across the team.
CRM write
Create, update, upsert, delete.
Write to any standard or custom object. The action reads the schema at build time so the field picker shows exactly the columns on the object. Field-level permissions apply, so a flow running under a scoped service identity cannot write a field the identity does not have permission to touch.
Send message
SMS, MMS, or email, logged to the record.
Send a text through Strkr Messaging or an email through the native email layer. The action resolves the recipient, respects opt-out and consent state, logs the send on the related account or contact, and surfaces the message id for later lookup. One action, one audit entry, same compliance posture as every other outbound.
HTTP call
Hit any external API, with retries and timeout.
A general HTTP action calls any REST endpoint with authentication from the credential vault. Configure method, headers, body, retry policy, and timeout. Parse the response in the next node and route on fields. The call is logged with its request, its response, and its status for forensic debugging later.
Assistant prompt
Call Strkr AI with structured output.
An assistant action calls Strkr AI with a prompt, a record context, and a required output schema. The response comes back as structured JSON ready for the next node to consume. Draft an email, extract fields from a document, classify a reply, score a conversation. The assistant is a first-class citizen inside the flow graph, not a separate chat window.
Create task
A to-do assigned to a user, with a due date.
Fire a task on a user or a team, with a due date, a priority, and a link back to the record that triggered the flow. The user sees the task in their inbox and the task carries the context the flow had. No separate task tool, no second notification channel, no shared inbox to crawl.
Approval gate
A human approves before the flow continues.
An approval node pauses the flow and sends the configured approver a request with the context and the decision options. The flow resumes on the approval response. Timeouts, delegation, and multi-approver quorum are all handled by the node without a separate approvals product.
Document generation
Produce a PDF or Word file from a template.
Pass data from the flow into a Strkr document template and generate the file. Store the output on the related record. Email the file as an attachment. Pipe it into an e-signature workflow. The flow becomes the fastest path from record state to signed paper, without an export, a drive, or a secondary tool.
Dashboard write
Push a metric to a shared dashboard.
Flows can write to custom metric endpoints that back a dashboard tile. A nightly flow computing a composite number can land it on the leadership dashboard without a BI job. The tile shows the fresh value with the last-updated timestamp and the flow that wrote it.
Saved composites
Group common actions into a reusable step.
An admin saves a cluster of nodes as a composite action. "Route inbound lead" might package resolve-company, write-lead, assign-owner, send-ack-email, and create-task. The composite shows up in the action library and other flows reuse it. Standardize the motion once, propagate it everywhere.
Reliability, retries, and run history
The boring operational layer that keeps the platform honest.
Automation that fires 98 percent of the time is worse than no automation at all, because the two percent misses erode trust in a quiet way nobody notices until the quarter ends. The cloud platform earns its keep by executing every eligible event exactly once, by retrying transient failures without duplicating writes, by surfacing permanent failures on a visible queue, and by letting an admin replay a run from any step with the same payload. Strkr builds the reliability layer as a first-class product surface, not an invisible back end.
Exactly-once execution
The write happens, and only once.
The engine deduplicates triggers at the ingestion layer and persists idempotency keys on side-effecting steps. A flow that fires on a deal update does not double-run if the update event retries. A webhook replay does not create two contacts. The exactly-once guarantee is a platform contract, not a developer convention to remember.
Automatic retries
Transient faults recover without a human.
HTTP calls, assistant prompts, and external integrations all retry with exponential backoff on recoverable failures. The retry policy is configurable per action: max attempts, backoff curve, timeout. A rate-limited external API recovers on its own without a flow run going to the dead-letter queue.
Dead-letter queue
A visible home for the runs that still failed.
When retries exhaust, the run lands in the dead-letter queue with the step, the error, the inputs, and the time. An admin inspects the DLQ once a day, fixes the root cause, and replays the runs with one click. Nothing fails silently, nothing accumulates in a log nobody reads.
Step-level replay
Resume from the broken node, not from the top.
Replay a failed run from any step, with the inputs the step saw the first time or edited inputs for a corrected re-run. A ten-step flow that failed on step eight does not have to re-run the first seven side effects. The platform respects the write boundary of the earlier steps.
Run history retention
Every run, every step, available for lookback.
The platform keeps run history for ninety days on standard tiers and longer on enterprise. The retention includes inputs, outputs, errors, and timing per step. A post-incident review three months later can walk the exact state the automation saw, not reconstruct it from logs.
Concurrency controls
Fair-share by tenant, by flow, by action.
The engine caps concurrency per flow and per tenant to prevent a runaway loop from starving the rest of the platform. Admins see the concurrency limit on each flow and can request a lift. The fair-share policy is why one tenant's busy quarter does not slow down another tenant's nightly scoring job.
Scheduled maintenance
Planned work on a published calendar.
Platform maintenance is announced on the status page and planned during low-traffic windows. The queue drains gracefully, in-flight runs finish, and new triggers queue safely until the window closes. No automation is dropped during a maintenance window and no admin needs to pause flows manually.
Health metrics
A dashboard on the engine itself.
The platform exposes an operational dashboard: trigger latency, step throughput, error rate, retry rate, DLQ size. Admins see at a glance whether the engine is healthy. SLAs are published and tracked publicly for enterprise tiers.
Change-safe deploy
A new version replaces old without breaking runs in flight.
Publish a new version of a flow and in-flight runs finish on the version they started on. New triggers pick up the new version. Rollback restores the prior version instantly. No partial runs, no mid-flight schema shifts, no half-upgraded state.
Security, compliance, and the trust posture
Encryption, isolation, and the paperwork a procurement team expects.
A cloud automation platform earns the right to touch regulated data by publishing its security program and documenting its compliance posture. Strkr runs on hardened AWS infrastructure with full encryption in transit and at rest, per-tenant isolation at the database layer, scoped service identities on every integration, SOC 2 on the implementation track, and a HIPAA BAA available on request for teams handling protected health information. The posture matters most for revenue teams in healthcare, financial services, and regulated SaaS, where a credit-metered external automation vendor is often not even a candidate for the shortlist.
Encryption in transit
TLS 1.2+ on every hop, no exceptions.
All client traffic to Strkr runs over TLS 1.2 or higher. Inbound webhooks require HTTPS. Outbound HTTP calls default to TLS verification on. The platform rejects downgrade requests. There is no plaintext channel for data to leak into on the way between the browser and the engine.
Encryption at rest
Database, backups, and object storage, all encrypted.
The Postgres database, the automated backups, and the object storage for attachments all encrypt at rest with AES-256. Keys are managed through AWS KMS with per-tenant envelope keys for sensitive secret storage. Rotation happens on the platform cadence without a customer action.
Row-level tenant isolation
Your data never shares a row with another tenant.
Every row in the Strkr database carries a tenant id. Every query is scoped by tenant at the application layer and enforced by row-level security at the database layer. A code mistake that drops the application scope still cannot cross the tenant boundary because the database refuses the query.
Scoped service identities
Flows run as a bounded principal.
Each flow runs under a service identity scoped to the permissions the flow needs. A flow that only reads deals cannot write accounts. A flow that writes messages cannot change user records. The least-privilege posture at the service layer stops a mistaken flow from touching more than its narrow surface.
SOC 2 track
Program running, controls documented.
Strkr is on the SOC 2 Type II implementation track. The control framework is in place, the policies are published, and the audit window is active. Teams in regulated environments can request the latest attestation progress and the policy pack from the trust center.
HIPAA BAA available
A signed BAA for teams in healthcare contexts.
For teams building workflows that touch protected health information, Strkr executes a Business Associate Agreement on request. The HIPAA posture covers the hosted platform, the messaging layer, the assistant calls, and the data retention policies. The regulated engagement is a contract flow, not a separate product.
Audit log for every change
Who did what, when, from where.
Every flow definition change, every credential rotation, every manual run, every DLQ replay is logged with the actor, the timestamp, the IP range, and the before/after state. The audit log is searchable in the UI and exportable for compliance reviews. Nothing changes without a line in the log.
Credential vault
KMS-encrypted secrets referenced by name.
API keys, OAuth tokens, and other credentials live in a per-tenant vault with KMS-backed encryption. Flows reference credentials by name. Reads land in the audit log. Rotation is a UI action with no code change. The vault is the only place credentials live and the only path by which flows access them.
Open integration posture
Strkr does not depend on partner marketplace revenue.
The integration layer is first-party. Every connector ships as native code written by the Strkr team, not a Zapier recipe or a partner-built middle tier. Reduced attack surface, one audit boundary, one bill, one support thread. Open API, open documentation, open source adapters for custom work.
Cloud workflow automation on the same seat as the CRM.
The automation engine, the designer, the credential vault, the run history, the retry layer, and the DLQ all ship on every paid tier. Strkr AI, Strkr Messaging, and the native integration catalog are included with no add-on SKU. Open the designer on a real motion and have the first flow in production before lunch.
What makes cloud-based workflow automation different from on-premise or self-hosted tools?
A cloud-based platform runs on infrastructure the vendor operates, patches, scales, and monitors. Teams build flows in a browser, deploy with a click, and never touch a server, a queue host, a worker VM, or an agent install. On-premise tools require a dedicated host, a database, a backup plan, a monitoring stack, and an upgrade cadence that costs operator time. Strkr runs the entire automation layer as a managed service, with multi-tenant isolation at the database layer, encryption in transit and at rest, and SLA-tracked uptime. The practical difference is that an admin can go from an idea to a published, monitored, auditable flow in minutes, without filing a ticket for infrastructure.
How does Strkr handle reliability if an external API or integration fails during a flow run?
The engine retries transient failures automatically with exponential backoff, with a configurable max-attempt and timeout per action. HTTP calls, assistant prompts, and external integrations default to sensible retry policies that recover from rate limits and transient faults without human intervention. When retries exhaust, the run lands in a visible dead-letter queue with the exact step, inputs, and error. An admin reads the DLQ, fixes the root cause, and replays runs step by step from the broken node, not from the top of the flow. Exactly-once semantics on side-effecting steps prevent duplicate writes when the retry path recovers mid-execution.
Is there a per-task charge or a credit meter on the automation layer?
No. The automation engine is included on every paid Strkr tier at no additional charge. Build as many flows as the team needs, trigger them as often as the business runs, and the invoice is still the seat count the pricing page shows. There is no per-task meter, no per-run credit, and no premium action tier. Strkr AI inside the flow engine also runs on the seat price, with no credit meter on prompts. The predictability lets a team scale automation without a procurement conversation every quarter.
What security and compliance posture does the platform ship with?
Strkr runs on hardened AWS infrastructure with TLS 1.2+ encryption in transit, AES-256 encryption at rest, and row-level tenant isolation at the database layer. Each flow runs under a scoped service identity. Credentials live in a per-tenant vault with KMS-backed encryption and are referenced by name, not value. SOC 2 Type II is on the active implementation track with the full control framework in place. A HIPAA Business Associate Agreement is available on request for teams building flows that touch protected health information. Every change to a flow, every credential rotation, and every manual run writes to an audit log that is searchable in the UI and exportable for compliance reviews.
Can we integrate with external systems without using Zapier or a middleware tier?
Yes. Strkr ships a first-party integration catalog written by the Strkr team, plus a general HTTP action that can call any REST API with authentication from the credential vault. The webhook trigger exposes an inbound signed URL per flow for external systems to call. The open API gives developer-level access to every object in the tenant. No third-party automation layer is required between the CRM and the external system, which keeps the integration audit boundary at one vendor, reduces attack surface, and avoids a parallel bill for middleware that duplicates what the native engine already does.
How do we migrate existing automations off another tool without a six-month cutover?
The designer reads the shape of most upstream automations: triggers on record events, scheduled jobs, HTTP calls to external APIs, and conditional branches. Teams typically copy a flow at a time, test the new version against a sandbox tenant, and switch the production trigger once the Strkr version produces the same outcome as the legacy version. Flows can be versioned and rolled back, so the migration proceeds in small units with a safety net at every step. For teams moving from a self-hosted engine, the lack of infrastructure to run on the Strkr side collapses the cutover window and the new flows start producing audit entries from day one.
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