Feature · Customer Service Automation

Service tickets that route, respond, and resolve themselves faster.

Strkr customer service automation covers intake across email and web form, routing by rule, SLA clock tracking, Strkr AI reply drafts tuned to your knowledge base, escalation on breach, and CSAT capture after resolution. Agents handle more tickets with less typing.

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Intake

Four channels, one case record.

Service requests arrive through multiple channels. Email to the support mailbox, web form on the help portal, inbound phone call to the service line, SMS to the support number. Each channel feeds into one case record on the matching account. Agents work from the same inbox regardless of how the request arrived.

Email intake

Shared mailbox to case record.

Email to support@tenant maps to the service queue. Each new thread creates a case with the sender as the contact, the subject as the case title, the body as the first case comment. Reply emails on the same thread update the existing case; attachment handling preserves files on the case. The pattern replaces the free-form shared-mailbox chaos with structured case tracking.

Web form

Portal submissions with required fields.

Customer-facing help portals submit through a web form that captures required fields: category, product, severity, description. The form validates on submit and refuses submissions missing required context. The pattern improves the first-case data compared to free-text email intake; agents triage faster when the inbound form carries structure.

Phone intake

Inbound call creates a case.

Inbound phone calls on the support line auto-create a case with the caller as the contact (via caller ID match) and the call recording link attached. Agents type the resolution notes during or after the call; the case holds the full history. The pattern avoids the common gap where phone-handled issues never land on the account record.

SMS intake

Text replies become case threads.

SMS inbound to the support number threads to the matching contact and creates or updates a case. Agents reply via the Strkr thread; the SMS routes outbound through Strkr Messaging. The pattern suits consumer-service motions where customers prefer texting. 10DLC compliance applies; opt-outs route to opt-out suppression.

Chat intake

Live chat sessions become cases on handoff.

Web chat sessions (through an integrated chat provider or the native Strkr chat widget) can hand off to case on request. Live chat covers the immediate response; case covers the follow-through if resolution needs more than one session. The handoff preserves the chat transcript on the case for context.

API intake

External system creates cases.

External systems (uptime monitoring, security alerting, product usage) create cases through the Strkr open API (OpenAPI 3.1 at the trust center). A monitoring alert about customer-reported downtime creates a case automatically with the incident details; the on-call team sees the case before customer calls arrive. The pattern pre-empts the reactive support cycle.

Routing

Rules that put cases on the right agent.

Case routing on Strkr runs through configured rules. The rules evaluate on case creation and on case update; routing can send the case to a queue, assign to a agent, or trigger an escalation to a specialized team. The pattern replaces the manual triage inbox that most support teams start with.

Rule evaluation

Priority-ordered conditions.

Routing rules evaluate in priority order; the first matching rule wins. A rule like if product is Checkout AND severity is Critical, route to the payments on-call rotation fires before a lower-priority rule like if product is Checkout, route to the checkout queue. The ordering keeps the policy tight and the before-general pattern explicit.

Queue vs agent

Pool the case or assign it directly.

Rules route to a queue (any agent in the queue can pick up) or directly to an agent (specialist assignment, named-account handling). Queue routing fits high-volume motions; direct assignment fits named-account service where a agent owns the customer. Both patterns coexist in one rule set.

Round-robin

Fair distribution within a queue.

Queue routing supports round-robin to spread cases evenly across agents on duty. Agents on PTO or marked unavailable skip in the rotation. The pattern avoids the common senior agents get all the hard cases bias that happens when triage runs by hand.

Capacity-aware

Route around overloaded agents.

Routing can consider current agent workload: an agent with 15 open cases skips in the rotation while an agent with 3 open cases picks up the next assignment. The pattern keeps workload balanced within a queue even when case complexity varies. Capacity thresholds are tenant-configurable per role.

Skills-based

Match the case to agent expertise.

Agents carry skill tags (payments, checkout, mobile, enterprise). Cases carry category tags that map to required skills. Routing matches the case category to agents with the required skill. The pattern suits specialized teams; a case about a product feature lands on an agent who knows the feature.

VIP routing

Named accounts to specific agents.

High-value or sensitive accounts carry a VIP flag that overrides standard routing. A case from a VIP account routes to the account owner directly, bypassing the queue. The pattern maintains relationship continuity on strategic accounts while the standard motion handles the broader volume.

SLA clocks

First response and resolution time.

Service SLAs set the expectation on first-response time and resolution time per severity. The SLA clock runs server-side with pause support for Pending Customer states. Breaches surface before they happen (yellow at 75 percent of budget) rather than after. Reports roll up SLA attainment per agent, per queue, and per tier.

Policy by severity

Different clocks for different severities.

SLA policy varies by severity: Critical cases carry 30 minute first response and 4 hour resolution; High carry 2 hours and 24 hours; Normal carry 8 hours and 72 hours; Low carry 24 hours and 7 days. The policy is tenant-configurable; different support tiers (free, standard, enterprise) can carry different policies on the same severity.

Business hours

Clocks respect business hours.

SLA clocks run during tenant-configured business hours per queue. A case submitted at 11 PM on Friday for a queue running Monday-to-Friday 9-to-5 does not accrue breach time overnight; the clock resumes Monday morning. The pattern matches how teams actually staff support rather than demanding 24-7 response as the default.

Pause states

Pending customer pauses the clock.

Cases in Pending Customer state pause the SLA clock. An agent responds within SLA, the customer takes four days to reply, and the clock resumes on the customer reply. The pattern avoids penalizing agents for customer-side delays. The pause state is explicit; the clock does not pause on arbitrary case activity.

Warning threshold

Yellow before red.

The clock surfaces a yellow warning at 75 percent of budget. Agents see the warning before the breach happens; the pattern enables intervention rather than post-hoc breach analysis. A manager seeing yellow across a queue reinforces staffing; the alternative of waiting for red is a measurably worse motion.

Escalation on breach

Breached cases notify management.

A case that breaches SLA triggers an automatic escalation: the manager gets a Slack ping, the case flags on the manager dashboard, Strkr AI drafts an acknowledgment response to the customer. The pattern ensures breaches do not quietly rot; something happens that moves the case forward.

Reporting

Attainment and distribution views.

SLA reports roll up first-response and resolution attainment per queue, per agent, per severity, per week. Teams track attainment against goal (typically 95 percent for first response, 90 percent for resolution) and identify queues or agents where coaching matters. The pattern makes service performance measurable rather than vibes-based.

Strkr AI reply drafts

Response drafting from the knowledge base.

Agents on service queues spend significant time crafting responses to common questions. The Strkr AI reply draft reads the case content and the knowledge base, drafts a response, and shows the agent the draft with source citations. The agent accepts, edits, or discards. The pattern cuts median response time while preserving human review.

Draft on open

A suggested response ready to send.

Opening a new case triggers Strkr AI to read the case content, query the knowledge base, and draft a response. The draft lands in the reply box with source citations. The agent reviews, edits if needed, and sends. For common questions with clear knowledge base answers, the agent sends in under 30 seconds; the alternative is 3 to 5 minutes of manual crafting.

Knowledge base grounding

Responses cite specific articles.

Draft responses include source citations to specific knowledge base articles. Agents verify the AI used relevant sources; customers see links to the referenced articles in the final response. The pattern builds customer trust in the response (clear sources) and reduces AI hallucination risk (agents catch mismatched sources quickly).

Tone matching

Response voice matches the tenant.

The AI draft uses the tenant-defined response tone: formal or casual, verbose or terse, technical or plain-language. The tone is configured once in the admin surface; every draft respects the voice. The pattern maintains brand consistency across agents without requiring each agent to internalize the voice guide.

Multilingual

Draft in the customer language.

The AI detects the customer language from the case content and drafts the response in the matching language. Agents handling multilingual queues (common in global service teams) accept drafts in languages they themselves do not speak natively, with the AI handling translation accuracy. The pattern suits teams supporting 20-plus languages without hiring 20 native-speaking agents per language.

Macro expansion

Shortcuts combine with AI drafting.

Agents who prefer manual control use macros (canned responses with placeholder fields) alongside AI drafts. A macro like shipping-delay-apology expands with the order details filled in. The two patterns coexist; agents who like the AI use it, agents who prefer manual macros keep that workflow. The pattern avoids forcing one working style.

Learning loop

Edits refine the next draft.

Agent edits on AI drafts feed back into the model tuning for the tenant. Repeated edits in the same direction (shorter responses, more technical detail, specific terminology) adjust the drafting style over time. The pattern improves draft quality progressively; the first week drafts need more editing than the fourth week drafts.

Resolution and retention

From case close to NPS to retention.

Case resolution is not the end of the service motion. Resolved cases trigger CSAT capture, root-cause analysis for critical cases, and feedback integration into the knowledge base. The post-resolution flow turns individual case outcomes into organizational learning.

CSAT capture

Automated survey on resolution.

A resolved case triggers a CSAT survey email to the customer: how satisfied with the resolution, how satisfied with the agent, open-ended feedback. Responses write to the case and roll up on the agent scorecard. The pattern provides direct customer feedback without a separate quarterly NPS exercise. Response rates run 15 to 25 percent on service CSAT.

Agent scorecards

Volume, speed, satisfaction, reopens.

Agent scorecards aggregate case volume handled, average resolution time, SLA attainment, CSAT score, reopen rate. Managers coach from the scorecard during 1:1s. The pattern makes service coaching data-driven rather than impressionistic. Scorecards update daily; managers see trend over quarters.

Reopen tracking

Cases that come back get flagged.

A case closed and then reopened within 7 days flags for review. The flag indicates incomplete initial resolution or customer confusion. Teams measure reopen rate per agent and per category; patterns in reopens drive knowledge base updates and training. The pattern distinguishes resolved cases from closed cases.

Knowledge base feedback

Resolved cases suggest articles.

Resolved cases that match no existing knowledge base article (or match a thin article) surface as content-gap suggestions. Content managers review the queue weekly and author new articles. The pattern turns support activity into living content; the knowledge base grows based on actual customer questions rather than internal assumptions.

Escalation pattern

Common escalations drive training.

Cases that escalated to a specialist get tagged with the escalation reason. Monthly review of escalation reasons surfaces training opportunities (frontline agents could handle 70 percent of these if trained on topic X) or product opportunities (we are getting the same question 40 times a week, let us fix the underlying issue). The pattern connects service to product.

Compliance posture

HIPAA, SOC 2, retention policy.

Service runs under the standard Strkr compliance posture: HIPAA with BAA for healthcare tenants, SOC 2 Type II (in progress through 2026-Q4) for the audit infrastructure, tenant-controlled retention (default 2 years for cases, longer for regulated verticals), per-tenant KMS encryption at rest. The posture is uniform across the record; service does not require separate compliance review.

See a sample ticket route, draft, resolve, and survey.

Fifteen minute walkthrough on a demo tenant: intake a sample email, route through a rule, draft an AI response, resolve, and capture the CSAT. Bring a sample ticket from your current tool.

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Common questions

What buyers ask about this feature.

How does Strkr customer service compare to Zendesk or Freshdesk?

Zendesk and Freshdesk are mature dedicated help desk platforms with rich feature sets. The structural difference for Strkr customer service is that the case module lives inside the same record system as the CRM, Marketing, and Projects modules. An agent viewing a customer case sees the account record, the active sales opportunities, the marketing engagement, and the project state in one view. Teams running Zendesk alongside a CRM typically maintain a sync lane between the two; Strkr eliminates the sync by sharing the record. For teams with heavy ITSM workflow needs (change management, asset tracking, incident response), a specialized ITSM tool may still fit better; for product-focused customer service, Strkr covers the full motion.

Can we connect our existing knowledge base to the AI reply drafts?

Yes. The AI draft engine ingests knowledge base content from the native Strkr knowledge base, from external sources (Zendesk Guide, Freshdesk Freshchat, Confluence, Notion, SharePoint) via API, or from a crawled public help center URL. Ingestion runs on a scheduled cadence; new articles become available for AI drafting inside the sync window. The AI respects source permissions; articles marked internal-only do not get cited in customer-facing drafts.

How do we handle multi-brand service queues on one tenant?

Multi-brand tenants run separate service queues per brand, each with its own branded response templates, SLA policy, and support mailbox. A tenant running three brands maintains three queue sets; agents can be cross-trained and assigned to multiple queues or specialized to one. Customer-facing communications respect the brand on the queue; the pattern suits agencies, holding companies, and multi-product teams. Reports slice per brand or roll up across brands at the executive level.

What happens when a case needs to escalate to an engineering team?

Service cases that need engineering escalation can create a linked Project issue in the Projects module with the case context carried forward. The engineering team works the issue in their own queue; the service agent sees the issue state on the case and updates the customer accordingly. The pattern avoids the common support-engineering handoff where context gets lost; both sides work from the same linked records.

How does the system handle after-hours support coverage?

After-hours policy is configurable per queue. Cases arriving outside business hours either queue for next-business-day handling, trigger on-call rotation (with Slack ping to the on-call agent), or auto-respond with an expected-response-time message depending on severity. The pattern suits teams running 9-to-5 standard support with on-call rotations for critical issues. Teams running 24-7 service configure business hours as 24-7 and route to the shift queue in rotation.

Can we use SMS or chat as the primary customer-facing channel?

Yes. The channel choice is per-queue. Teams running primarily SMS-based consumer service configure their main queue around SMS inbound; email becomes the fallback. Teams running primarily chat configure around the chat widget. The case record format is channel-agnostic; the agent experience is similar regardless of whether the customer prefers email, SMS, or chat. The multi-channel posture matters for consumer-service motions where channel preference varies widely.

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