Feature · Intelligent Business Process Automation

Run intelligent business process automation on the CRM you already own.

Intelligent business process automation is the practice of applying AI to the end-to-end business process rather than to a single task. It is only intelligent when it reads real business context: records, timelines, prior decisions, and the live state of a deal, a ticket, or a project. Strkr runs intelligent BPA on the platform the sales, marketing, and ops teams already use: Flows for the orchestration, Strkr AI for the context-aware decisions inside each flow, the CRM for the records, and the audit log for every automated choice. One tenant, one role matrix, flat seat pricing.

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What makes BPA intelligent

The six properties that separate intelligent BPA from basic workflow.

A workflow that fires a step on a trigger is not intelligent. It becomes intelligent when the decision inside each step reads real business context: the record, the prior decisions, the sentiment of the last interaction, the risk flags on related records, the policy that governs the current action. Strkr intelligent BPA has six properties built into the runtime.

Context-aware branching

Flow reads the record before choosing a path.

A naive flow branches on a static field value. An intelligent flow reads the deal timeline, the account history, the related tickets, and the sentiment of the last email before choosing. Strkr Flows call Strkr AI with the full record context, read the structured return, and branch on the result. The decision is informed.

Policy enforcement

Every decision checks the governance rule.

A flow routing a service request for approval reads the role matrix, checks the amount threshold, and confirms the approver is active before firing. If the rule changes, the audit log records which policy version applied to each decision. Policy is not an afterthought.

Risk reasoning

AI reads deal and flags before firing.

Strkr AI reads the risk signals on a deal (no activity, contact drift, slipped dates, tone shift) and decides whether to escalate, nurture, or hold. The reasoning is explainable: the AI returns the signals it weighed and the policy it applied. The human lead can audit the choice.

Natural language rules

Admin writes rules in plain English.

A policy like deals over $50k from strategic accounts route through senior review is a sentence on the Strkr admin page. Strkr AI parses the sentence into executable logic, writes the audit entry describing the compiled rule, and the admin reviews the compile output before publishing. Rule changes are human-readable.

Explainability

Every automated decision shows its reasons.

The audit log entry for an intelligent decision carries the inputs the AI read, the policy that governed the choice, the structured return the AI produced, and the resulting write. A compliance review on automated decisions reads the chain for any specific case, not a vendor-dashboard summary.

Human override

Reviewer can override with a reason captured.

A reviewer can override any automated decision with a reason that writes to the audit log. The override feeds a feedback loop that calibrates the policy over time. Patterns of override become visible to the policy owner; the system learns from the dissent.

Where basic BPA stops

The six limits of workflow tools without intelligence.

Classic workflow tools do a competent job on structured processes with clear branching: on this field, do that step. They stop short when the branching needs to read context, when the policy evolves faster than the workflow can be rewritten, when the audit question is a compliance review on automated decisions, or when the exception rate is too high to triage manually. Intelligent BPA is the layer that handles those cases.

Static branching

If-then on fields, nothing more.

A basic flow branches on field values: if stage equals X then do Y. It cannot read the timeline, the sentiment of the last email, or the health of the account relationships. Strkr Flows call Strkr AI from within a branch to read context and return a structured decision the flow can switch on.

Policy drift

Rules rewrite requires an engineer.

A classic flow encodes policy in diagram topology. Changing the policy means editing the flow and republishing. Strkr lets the admin write the policy in plain English on the admin page; Strkr AI compiles it into executable logic the flow calls, and the policy version writes to the audit log.

Audit silence

Flow fired, but why is anybody's guess.

A classic flow logs that it ran. It does not log why a branch was taken when the branch condition is dynamic. Strkr writes the audit entry with inputs, policy version, structured return, and resulting write. The compliance review runs on real data.

Exception overflow

Too many manual exceptions to triage.

A classic flow generates an exception on anything that does not match the main path. Teams drown in exception queues. Strkr intelligent BPA reads exception context and either resolves it (route to a owner, update a field, nurture) or escalates with explainable reasoning the human can act on in seconds.

No learning loop

Repeated overrides never change the system.

A classic flow does not know when humans consistently override its routing. Strkr's feedback loop surfaces override patterns to the policy owner so the policy evolves. The system learns from the dissent instead of forcing humans to disagree with it week after week.

Siloed records

Workflow runs on data it does not own.

A classic workflow vendor runs on records that live in another system. Latency, drift, and permissions leaks are endemic. Strkr owns the records, runs the flows on-platform, and audits every change in one tenant. The architecture is clean.

Real processes Strkr intelligent BPA runs

Eight end-to-end processes the platform handles today.

Intelligent BPA is only interesting when it covers real end-to-end processes. These eight are production patterns on Strkr: inbound lead triage, deal risk escalation, service request approval, customer renewal watch, support-to-engineering handoff, new-hire provisioning, GDPR deletion cascade, and quarterly compliance review. Each covers a flow, an AI call, a policy, and an audit trail.

Inbound lead triage

Score, route, nurture, enrich.

A form submit fires a flow that scores the lead via Strkr AI reading contact history and campaign engagement, routes to the right rep based on territory and specialty, enriches from configured data providers, and enters a nurture track if score misses the threshold. The flow writes the full chain to the audit log.

Deal risk escalation

AI reads timeline, flags, and escalates.

A weekly flow reads every commit deal, calls Strkr AI with the deal timeline and risk signals, and flags the deals that need manager coaching. The manager opens Monday's review with a ranked list, writes coaching notes, and the audit log carries the AI reasoning plus the human follow-up.

Service request approval

Multi-step approval with policy awareness.

A procurement ticket from the service desk fires a flow that reads the amount, the department, the vendor history, and the current fiscal state. Strkr AI routes to the right approval chain, the approvers decide, and the audit log carries the policy version and every approval event.

Renewal watch

Account health drives the renewal play.

A nightly flow reads every account approaching renewal, calls Strkr AI with usage, support ticket history, and primary contact engagement, and categorizes the renewal risk. High-risk accounts route to the strategic retention play. Low-risk auto-renew with a lightweight check-in email.

Support-to-engineering

Ticket becomes a Projects issue with context.

A ticket tagged for engineering fires a flow that creates a Projects issue, carries the context, and keeps the two linked. Strkr AI summarizes the ticket thread, extracts the reproduction steps, and writes them to the issue description so the engineer reads a complete story.

New-hire provisioning

HRIS event drives multi-system onboarding.

A BambooHR hire event fires a flow that builds the Freshservice provisioning ticket tree, enrolls the hire in the right identity-provider groups, schedules the laptop, and triggers the welcome nurture. Strkr AI drafts the welcome message in the hiring manager's voice.

GDPR deletion cascade

One request, every system, auditable.

A GDPR deletion request fires a flow that redacts the contact, the deal history, the campaign engagement, the project involvement, and the related integration events per the retention policy. Strkr AI writes the privacy response letter and the audit log carries the complete cascade.

Quarterly compliance review

Audit filter runs the full review.

A quarterly flow pulls admin changes, flow policy versions, AI decision logs, and access reviews for the quarter. Strkr AI summarizes the findings into a compliance review packet. The compliance owner reads a page instead of running fifteen queries.

Compliance and trust

What the intelligent-BPA runtime inherits from the platform.

Intelligent BPA that runs on sensitive business data needs the same compliance posture as the rest of the platform. Strkr runs HIPAA with BAA, SOC 2 Type II in progress through 2026-Q4, GDPR and CCPA, scoped tokens, KMS encryption, and append-only audit. The runtime inherits all of it.

HIPAA with BAA

Protected health info stays inside the tenant.

Strkr is HIPAA compliant with a BAA available on request. The intelligent BPA runtime runs inside the tenant boundary with no third-party processor in the data path. Healthcare software vendors and healthcare-adjacent teams can run the full BPA layer under one compliance review.

SOC 2 Type II

Audit in progress through 2026-Q4.

SOC 2 Type II is in progress through 2026-Q4 with the control inventory published to prospective customers under NDA. The intelligent BPA runtime inherits the controls: access reviews, change management, incident response, and logging. The audit covers Flows, Strkr AI, records, and the API.

GDPR and CCPA

Right-to-delete cascades through BPA runs.

A GDPR deletion request propagates through the records the BPA runtime touched. The audit log carries the deletion event with every affected record and the retention policy version. CCPA posture is the same. The privacy response reads directly from one audit log.

Scoped AI calls

Strkr AI reads only the records in scope.

Strkr AI runs inside the tenant with record access scoped by the role matrix. A flow running as the sales manager calls Strkr AI with records the manager can see. The AI does not reach records outside the matrix. Policy and access are coupled.

Encrypted at rest

KMS-backed storage end to end.

Flow configurations, AI prompt policies, decision logs, and record data are stored in KMS-backed encrypted storage. Encryption keys are per-tenant. Rotation is a one-click action. The posture is encrypted end to end inside the tenant boundary.

Append-only audit

Every flow run, every AI call, every override.

The audit log carries every flow run (inputs, policy version, structured return, outbound write), every AI call (prompt digest, response digest, policy version), and every human override (actor, reason, timestamp). A compliance review runs one filter query across the lot.

Run intelligent BPA on the platform you already own.

Flows plus Strkr AI plus audit plus open API. The reference processes are production ready. Keep the audit log, keep the flat seat price.

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Common questions

What buyers ask about this feature.

What is intelligent business process automation and how is it different from workflow?

Workflow is a trigger plus a step: on this field change, do this step. Intelligent BPA adds a decision layer inside each step that reads real business context (the record timeline, the account history, the policy, the risk signals) before choosing a path. Strkr runs intelligent BPA on Flows plus Strkr AI, with every decision writing an audit entry that shows the inputs, the policy version, the AI reasoning, and the resulting write.

How does Strkr AI participate inside a flow without the user context-switching?

Strkr AI runs inside the record and inside the flow runtime. A flow can call it from within a branch with the record context already loaded. The AI returns a structured decision (routing, risk tier, suggested next action) that the flow switches on. The audit log carries the prompt digest and the response digest for the compliance review. The user never context-switches to a chat tab.

Can admins change the policy that governs an intelligent flow without engineering help?

Yes. The admin writes the policy in plain English on the admin page: a rule like deals over $50k from strategic accounts route through senior review. Strkr AI compiles the sentence into executable logic, the admin reviews the compile output before publishing, and the audit log records the policy version. Policy changes are human-readable and reversible.

How does the override feedback loop work?

Human reviewers can override any automated decision with a reason captured in the audit log. The override pattern surfaces on the policy owner's dashboard: a policy whose decisions get overridden 15% of the time is a candidate for policy revision. The system does not silently learn in the background; the human reviews the pattern, decides on the policy change, and publishes a new version. Learning is explicit.

What volume of flow runs does the platform support?

The intelligent BPA runtime is event-driven with per-tenant rate budgeting and exponential backoff on 429 responses. A 500,000-run month runs at the flat seat price with no per-task meter. Teams with higher sustained throughput can request a reserved-capacity engagement. The runtime is retry-safe, idempotent on signed delivery IDs, and audit-logged on every run.

How does the audit log answer compliance questions about automated decisions?

The audit log is append-only and tamper-evident. Each flow run writes an entry with the inbound event, the branch taken, the policy version, the AI inputs and outputs (in digest form when policy requires), and the resulting writes. A compliance review on automated decisions runs a filter query by date, by flow, by policy version, or by acting user. The answer is reproducible months later.

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