Decision automation that explains itself to the operations team.
Routing a lead, approving a refund, escalating a ticket, releasing an order, flagging a transaction. The Strkr Flow Decide node reads the record, scores the decision with the drivers shown, respects the policy pack, and lands the audit evidence in one log. Open API, every tier, no credit meter.
What a real decision automation engine owes the ops team
Nine jobs the operations lead actually asks for.
Decision automation gets sold as a magic black box that eats a backlog of judgement calls and emits perfect outputs. The ops lead running a real operations floor needs something less glamorous and significantly more useful: a configurable engine that reads the record, applies the policy, scores the case with the drivers shown, routes to the right actor, and leaves an audit trail that stands up in a quarterly review. The nine jobs below are the honest list of what an ops team pulls from a decision engine on a Monday morning. Strkr Flow ships all nine as native primitives.
Rules-based routing
Deterministic policy, no model drift.
A policy says tickets over a 24-hour SLA route to the senior queue, refunds over 500 dollars route to a manager, and leads from an enterprise ICP route to the strategic team. These are deterministic rules; the engine applies them without probabilistic surprise. The ops lead edits the rule in a readable canvas, not a config file nobody owns.
Scored decisions
A number with the top three drivers.
A credit decision, a fraud flag, a renewal risk flag, a priority score, or a lead-fit score comes out as a number plus the three strongest drivers in plain English. The operator reads the drivers and audits the decision. Scoring without explanation is scoring that nobody trusts; Strkr emits both every call.
Policy verification
Every decision checked against the policy pack.
A refund approval that would exceed the daily cap, a lead routing that would violate the territory rule, a transaction that triggers a KYC check. The Verify step runs against configured policies before the decision commits. The policy pack lives in the tenant and the audit log carries each verification result.
Human in the loop
Low-confidence routes to a human reviewer.
A decision below a configurable confidence threshold routes to a human review queue rather than firing autonomously. The threshold is per decision type and per policy. The reviewer sees the record, the recommended decision, the drivers, and the policy verification results in one surface and clicks approve, override, or escalate.
Audit evidence
Every decision, every input, every outcome logged.
The audit log carries the full decision event: the input record, the policy pack version, the model or rule version, the recommended decision, any human override, the final committed decision, and the elapsed time. A compliance reviewer walking a quarter of decisions walks one query.
Explainable scoring
Drivers shown in business language.
The drivers behind a score are rendered in business language the ops lead can act on, not model coefficients. For example: late payment history last 90 days, average deal size dropped 23 percent, two support tickets escalated. The ops lead learns the model by reading the drivers, not by taking a vendor training.
A-B challenger
Shadow run new rules before cutover.
A new policy or a tuned scoring model runs in shadow against the live decisions for a configurable window before cutover. The compare view shows which cases would have decided differently and why. The ops lead cutovers when the shadow has run long enough to earn confidence, not on a vendor-dictated release schedule.
SLA tracking
Time-to-decision measured per decision type.
A decision that sits in a human review queue for 48 hours blows the SLA. The engine tracks time-to-decision per decision type, per actor, and per policy. The dashboard surfaces the slowest decision paths so the ops lead can rebalance queues or add automation at the right step.
Open API hooks
Decisions callable from any Strkr Flow trigger.
A Decide node is callable from any workflow trigger: a form submission, a webhook from a third-party, a scheduled sweep, a product event, an inbound email. The decision engine is a composable primitive, not a separate product with its own API shape. The ops team wires it into every workflow that needs a judgement call.
The operations use cases that benefit most
Six domains where decision automation stops a backlog.
The decision engine proves its value in domains where the volume is high, the policy is well-defined, and the human time cost is measurable. The six domains below are the ones where a representative mid-market operations team working on Strkr Flow put the engine into production first and saw a measurable drop in queue size or cycle time inside the first 60 days. The numbers below come from a composite of six 2026 customers across the segments listed.
Lead routing
From a 400-row list to a ranked twelve-stack.
The before state was a RevOps analyst running a weekly round-robin spreadsheet and a lead router that fired on a shallow rule set. The after state is a Decide node reading ICP fit, territory, engagement signal, and current rep capacity. Lead-to-first-touch dropped from 11 hours to 90 minutes on average across one week of production data.
Ticket triage
Severity assigned at ingest, not after escalation.
The before state had a shared inbox and a human triager assigning severity on arrival. The after state is a Decide node reading the ticket text, the account tier, the SLA calendar, and the recent incident load. Mis-triage dropped from 14 percent to 2 percent of tickets; mean time to first response dropped by 38 percent.
Refund approvals
Within-policy refunds commit without a manager click.
The before state had every refund request landing in a manager queue. The after state has within-policy refunds (below cap, within window, approved reason code) commit automatically through the Decide node. Out-of-policy refunds route to the manager with the policy violation named. The manager queue shrank by 71 percent.
Fraud and risk flags
Suspicious transactions scored at ingest.
The before state had a daily batch review of flagged transactions. The after state is each transaction scored at ingest with the drivers shown. High-score transactions pause for human review; low-score transactions commit. The fraud team shifted from reactive review to proactive investigation.
Renewal risk flags
At-risk accounts surfaced 120 days before renewal.
The before state had a quarterly CSM pulse where risk accounts were flagged subjectively. The after state is a Decide node running weekly, reading usage trend, support ticket pattern, exec sponsor change, and NPS. The CSM team pulls into the at-risk list 120 days before the renewal date rather than 30.
Order release
Credit holds applied automatically where policy requires.
The before state was a daily finance review of orders that triggered credit holds. The after state is the Decide node applying holds inline against the credit policy. Orders that pass commit through to fulfillment; orders that fail hold for the finance team with the policy citation on the record.
How Strkr compares to the dedicated decision-engine market
The three vendor types ops teams usually evaluate.
The decision automation market splits into three product shapes: enterprise business rules management systems (BRMS) built for underwriting and insurance, modern decision-engine SaaS tools positioned at risk and fraud teams, and workflow platforms with decision steps bolted on. Each has strengths and tradeoffs. the comparison below helps the ops lead frame the choice.
Versus BRMS
The procurement is self-serve, not a six-figure engagement.
Enterprise BRMS tools (IBM ODM, Red Hat Decision Manager, FICO Blaze) are powerful at the top of the market and typically carry six-figure license costs and a professional services engagement to deploy. Strkr Flow at the mid-market scope ships the first decision workflow in two weeks on self-serve pricing. The gap closes for the common ops use cases.
Versus decision-engine SaaS
Native CRM integration, no sync boundary.
Dedicated decision-engine SaaS (SAS Decision Manager, Pega Decision Hub) typically runs beside a separate CRM and system of record. The sync boundary is where decisions get stale or wrong. Strkr Flow reads the live record directly because the decision engine lives in the same platform as the CRM.
Versus workflow with bolt-on
The decision step is first-class, not a hack.
Many workflow platforms (Zapier, Make, Power Automate) have a basic conditional step and claim it as decision automation. The step tends to be shallow (if/else on a single field) and lacks scoring, policy verification, audit evidence, and A/B shadow. Strkr Flow treats the decision as a first-class primitive with the full ops toolkit around it.
Versus internal build
The engine stops being an engineering ticket backlog.
The common pattern of an internal decision service built by one platform engineer and maintained with low priority eventually becomes a bottleneck. The ops lead cannot change a routing rule without a sprint. Strkr Flow puts the rule editor in the hands of the ops lead directly, which is a different operating posture.
Versus ML platform
The explainability is first-class, not an add-on.
Dedicated ML platforms (Databricks, Vertex AI, SageMaker) have strong model training and inference but weaker tooling for ops explainability, policy verification, and audit evidence at the decision boundary. Strkr Flow is positioned at the operational-decision boundary, not the model-training boundary. Teams typically pair the two: train the model on an ML platform, serve the decision through Strkr Flow with the ops controls around it.
Pricing posture
Flat per-user, no per-decision meter.
Many decision-engine vendors meter on decisions per month. The ops team adopting the engine thinks hard about volume because every incremental decision has a line-item cost. Strkr Flow prices on seats; the decision volume is unbounded. The ops lead rolls the engine out across every workflow without a procurement conversation each time.
Reporting posture
Dashboards share a surface with pipeline and ops metrics.
The decisions dashboard lives inside the same Strkr dashboard builder that the sales ops lead and the CS ops lead use for pipeline and ticket metrics. One reporting surface across the ops organization is a dramatic simplification over three vendor dashboards.
Governance posture
Policy, audit, and HITL on one tenant.
The policy pack, the audit log, the human-in-the-loop queues, and the model shadow runs all live in one tenant with one permission model. A compliance officer running a quarterly review walks one surface rather than reconciling across vendor dashboards. The posture matters most for regulated verticals.
Deployment timeline
First decision workflow in two weeks for mid-market.
A mid-market ops team moving a first use case onto Strkr Flow (typically lead routing, ticket triage, or refund approval) ships in two weeks: one week of configuration with the ops lead, one week of shadow running before cutover. The timeline is reproducible across the current customer base.
How decision automation connects to the rest of Strkr
The engine as one node on the broader revenue surface.
Decision automation inside Strkr is deliberately not a standalone product. The engine is a Decide node on the Strkr Flow canvas, which means it reads the same record the CRM, the marketing surface, and the Projects module all work. The relationships below are the ones a mid-market operations team exercises most once the engine is in production.
Decide to CRM
Routed leads land on the live account record.
A lead routed by a Decide node lands on the account record with the routing rationale captured. The AE opens the lead and reads the drivers that caused the routing decision. No sync delay, no stale rule, no vendor boundary.
Decide to forecast
Renewal risk flags roll into the forecast model.
A renewal risk flag from the Decide node feeds the forecast model as a signal. The CRO reading the Monday forecast sees the at-risk accounts in the same table as the pipeline math.
Decide to marketing
Segment eligibility respects decision outcomes.
A contact in the human review queue for a fraud flag auto-suppresses from marketing nurture journeys until the review resolves. The suppression is a single rule rather than a weekly list push.
Decide to Projects
Policy changes tracked as projects.
A policy change for the refund decision engine is a project in the Strkr Projects module with sign-off from finance, compliance, and ops. The change ships through governance rather than as a config edit nobody logged.
Decide to Docs
Policy packs documented in the wiki.
The business policy behind each decision rule lives in Strkr Docs with version control and cross-links to the workflow. The compliance team walks policy, workflow, and audit log on one platform rather than reconciling three vendor stacks.
Decide to open API
External systems call the decision engine directly.
A legacy order management system, a billing engine, or a product application can call the Strkr decision engine over REST with a decision payload and receive a scored response plus the policy verification result. The decision engine extends beyond the Strkr tenant through the open API.
Decision automation that reads for the ops lead, not just the vendor pitch deck.
Strkr Flow Decide node, policy verification, explainable scoring, human-in-the-loop queues, audit log, open API. Every tier, no credit meter. First decision workflow in two weeks for mid-market ops teams.
What is decision automation and how does it differ from workflow automation?
Workflow automation moves a record from one state to another through a defined sequence of steps. Decision automation inserts a judgement call at a step: route this lead to this rep, approve this refund below the policy cap, flag this transaction as suspicious, escalate this ticket to a senior queue. The decision engine reads the record, applies the policy, scores the case with drivers shown, and routes the outcome to the next workflow step. Strkr Flow ships both as native primitives: the Decide node for the judgement call and the full workflow canvas for the sequence of steps around it.
How does Strkr handle explainability in a decision engine?
Every decision emits three artifacts: the recommended outcome (approve, route to queue X, apply hold, flag for review), the confidence score (0 to 100), and the top three drivers in business language that the operations lead can read without model training. For example, a renewal risk flag might emit the drivers: usage trend dropped 23 percent in last 60 days, executive sponsor changed last quarter, two support tickets escalated to severity 2 in last 30 days. The explainability is first-class, not a tooltip; the drivers are in the audit log alongside the decision outcome, which is what regulators and auditors ask for.
Can we run a new decision rule in shadow before cutover?
Yes. The A/B challenger pattern is native. A new policy or tuned scoring model runs in shadow against live decisions for a configurable window (days to weeks). The compare view surfaces every case that would have decided differently under the new rule with the drivers, the old decision, and the proposed decision shown. The ops lead cutovers to the new rule when the shadow run has earned enough confidence rather than on a vendor-dictated release schedule. The pattern is particularly useful for high-stakes decisions like credit holds or fraud flags where a bad cutover is expensive.
What governance controls apply to decision automation?
Six controls apply by default. Policy packs are versioned in the tenant with change history. Policy verification runs on every decision before commit. Human-in-the-loop queues route low-confidence or high-stakes decisions to a human reviewer. The audit log captures the input record, the policy version, the model or rule version, the recommended decision, any human override, the final decision, and the elapsed time. Residency pinning holds the request, response, and logs inside a chosen region. Permission grants to edit the policy pack are logged and require a second-admin approval for sensitive decision types.
Does the decision engine work with external systems through an API?
Yes. Every Decide node is callable from the open REST API with a decision payload (record data, context, optional policy override), and returns the scored decision, the drivers, and the policy verification result. A legacy order management system, a billing engine, or a product application that needs a decision outcome calls the Strkr endpoint and consumes the response. Webhook events fire on decision commit, human review assignment, and policy update. The engine does not require data to live inside the Strkr record to serve the decision; the integration pattern is open.
How does the decision engine price compared to dedicated decision-engine vendors?
Strkr Flow is included on every paid Strkr seat with no per-decision meter and no AI add-on. The ops team deploys the engine across every workflow that needs a judgement call without a procurement conversation each time. Dedicated decision-engine vendors typically meter on decisions per month or carry enterprise license tiers in the six-figure range. For mid-market ops teams running 50,000 to 2 million decisions per month, the Strkr seat-based model lands at a dramatically lower all-in annual total than the metered alternatives. Enterprise-tier contracts for decision volume above 10 million per month are available with custom infrastructure terms.
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