A contact center runs 20 to 400 agents, each handling 40 to 200 contacts per shift. The CRM has to hold the queue context, capture the outcome, score the conversation, and feed the coaching loop. Strkr ships that stack with Strkr Messaging for SMS and MMS, with voice features on the roadmap for 2027-Q1.
Contact-center operations are a volume game played at a quality bar. The CRM has to hold enough context for the agent to be useful, capture enough outcome data for the operations manager to tune routing, and surface enough coaching signal for the QA team to raise the floor. The pain points below are what Strkr's onboarding team hears from inbound, outbound, and blended contact centers across sales, support, and collections.
Most contact-center CRMs ship a screen pop that shows contact name and phone number and nothing about why the person is calling or what to do next. Strkr's inbound screen pop includes the last three touches (channel, outcome, note), the open flag (billing issue, cancellation, upsell opportunity), the AI-drafted next-best-action, and the suggested quote or offer. The agent opens the call ready to resolve.
An inbound agent handling 120 calls per shift has 90 seconds of wrap-up between calls to log the outcome. Multi-field dropdown trees that take 60 seconds to navigate kill both throughput and data quality. Strkr's outcome capture surface uses a flat picklist with keyboard shortcuts, voice-to-text notes, and Strkr AI drafting the summary from the conversation (where recording is available via future voice integration).
A 60-agent contact center with a two-person QA team samples maybe 120 calls per week against 7,200 calls per week handled. The sample is too small to drive real coaching change. Strkr's AI QA scorer handles 100 percent of recorded conversations against the firm's rubric (available when voice recording ships in 2027-Q1), with human review on flagged conversations. Coaching conversations get grounded in real behavior at scale.
Contact centers in regulated verticals carry disclosure requirements (FDCPA for collections, HIPAA for healthcare, TCPA for outbound). Strkr's compliance surface tracks required disclosures per call type, flags conversations that may have missed a required disclosure, and retains audit evidence per retention rules. The compliance team gets a weekly review queue instead of a monthly discovery of a bad call.
Blended contact centers run agents who flip between outbound dialing and inbound call handling as queue depth changes. Most CRMs cannot model the blended queue cleanly. Strkr ships queue-aware agent assignment that routes outbound call attempts and inbound answers through the same agent capacity view, with real-time utilization visible to the shift supervisor.
A 24-hour contact center runs three shifts, each handling the same book of contacts. The handoff between shifts has to carry open flags, promised callbacks, and in-flight issues cleanly. Strkr's contact card surfaces every open flag, the responsible agent per flag, and the handoff notes. The incoming agent on the next shift sees exactly what the departing agent left.
A default CRM schema does not fit contact-center work. Strkr ships a tunable contact-center schema with Contact, Conversation, Outcome, QA Score, Agent, Shift, Queue, and Compliance Flag as first-class objects. The ops manager can rename or extend anything. The default lands close to where most centers end up after two months of tuning.
A contact in Strkr carries the full history across every channel (voice, SMS via Strkr Messaging, email, chat where enabled), the open flags, the preferred contact window, consent context (STOP, HELP, opt-in evidence), and the next-best-action suggested by Strkr AI. The contact card is what the agent sees on screen pop.
A conversation is one unit of agent-contact interaction (one call, one SMS thread, one email exchange, one chat session). The object captures channel, agent, duration, outcome, notes, and the compliance disclosures delivered. Multiple conversations tie to the same contact card to form the full relationship history.
Each conversation closes with an outcome from a configurable picklist (resolved, callback scheduled, escalated, abandoned, voicemail, no answer, do-not-call added, sale completed, cancellation processed). The outcome drives the next workflow step and feeds the agent performance and routing optimization reports.
A QA score ties to a conversation and captures the rubric items (greeting, discovery, product recommendation, close, disclosure, hold time, tone) with pass or fail or partial credit per item. The score surface the top-coaching opportunities for each agent and feeds the weekly coaching conversation grounded in real behavior.
An agent in Strkr carries skill tags (Spanish language, billing specialist, retention trained, outbound certified), performance tier based on rolling QA score and resolution rate, shift assignment, and current status (available, on call, in wrap-up, on break, offline). The agent record drives queue routing and shift-handoff visibility.
Queues model the routing structure (inbound sales queue, inbound support queue, outbound dialer queue, retention team queue, Spanish-language queue). Each queue carries routing rules (round robin, skill match, longest idle agent, priority weight), service-level targets, and the overflow logic for when the primary queue is full.
A compliance flag attaches to a contact or to a conversation (do-not-call added, FDCPA mini-Miranda required, HIPAA authorization on file, TCPA consent captured). The agent sees the flags on screen pop. The compliance team gets a weekly review queue of flagged conversations and conversations that may have missed a required flag.
A contact center needs AI that drafts in the brand voice, respects the compliance rules, and feeds the coaching loop. Strkr AI does the work with no vendor-brand chrome in the output. The jobs below are what shows up in every contact-center deployment inside the first three weeks.
When the queue routes a call to an agent, Strkr AI drafts a 60-second brief covering the contact's last three touches, open flags, likely reason for calling, and suggested opening approach. The agent opens the call ready to resolve instead of asking the contact to restate their history.
For contact centers with voice recording in place (through the current Strkr Messaging stack for SMS and MMS, voice recording ships 2027-Q1), Strkr AI drafts the wrap-up summary with resolution, open items, follow-up commitments, and the outcome picklist suggested. The agent reviews and submits. The 90-second wrap-up drops to 20 seconds.
Strkr AI scores a conversation against the firm's QA rubric (available on recorded conversations when voice ships). Human QA reviews the AI score, agrees or adjusts, and the result lands on the agent's coaching report. 100 percent of conversations get scored instead of a two-percent sample.
Before the weekly coaching one-on-one, Strkr AI drafts an agenda for the supervisor with the agent's top three coaching opportunities, two conversations that illustrate each, and suggested coaching language. The supervisor spends 10 minutes preparing instead of an hour and the conversation lands with specific examples.
For outbound campaigns Strkr AI drafts the opening-line personalization grounded in the contact's history (prior product purchased, recent service interaction, demographic match to the campaign). The agent sees the personalized opening on the call card and uses it or adjusts.
When a call escalates to a supervisor or specialist team Strkr AI drafts the handoff summary with the issue restatement, what has been tried, what the contact wants, and the suggested path forward. The escalation lands clean instead of forcing the contact to restate the issue to the next agent.
The agent desktop is the surface the contact-center ops manager cares about most because it directly drives throughput and agent satisfaction. Strkr's agent desktop collapses the 11 screens of a typical contact-center stack into one surface with the queue status, the contact card, the outcome capture, and the coaching feedback in a single view.
The agent desktop shows the current queue status (depth, service level, wait time), the current contact card with the full history and the AI-drafted brief, the active channel (voice placeholder, SMS, email), and the outcome capture surface on one screen. The agent stops Alt-Tabbing between four tools.
Every agent action has a keyboard shortcut (resolve, callback schedule, transfer, add note, add do-not-call, flag for QA review). The 90-second wrap-up drops to 30 seconds because the agent never leaves the keyboard. New-hire training time drops by 40 percent because fewer clicks means fewer things to learn.
For contact centers with voice recording in place (ships 2027-Q1), Strkr AI watches the conversation in real time and surfaces coaching nudges to the agent (missed disclosure, off-rubric opening, long hold time). The supervisor sees the same nudges and can intervene selectively. Coaching happens in the moment instead of in next week's one-on-one.
For regulated contact centers Strkr's compliance guardrails show a prompt at the right moment in the conversation (FDCPA mini-Miranda at call open for collections, HIPAA authorization check for healthcare, TCPA consent check for outbound). The agent acknowledges the disclosure delivery. The audit log retains evidence.
The agent desktop surfaces a live script and FAQ panel grounded in the current conversation context (contact's product, likely issue, campaign). The agent finds the right answer in two seconds instead of searching three internal knowledge bases.
At shift end Strkr AI drafts a shift-handoff summary covering open callbacks, in-flight escalations, promised commitments, and queue status to pass to the next shift supervisor. The handoff happens in five minutes instead of fifteen and no commitments drop through the shift transition.
The supervisor and operations manager need a floor view that shows queue health, agent utilization, service-level attainment, and coaching priorities in near real time. Strkr's cockpit refreshes every 10 seconds for queue data and every minute for aggregate performance metrics.
The queue health strip shows current depth, longest wait, abandonment rate, and service-level attainment for every active queue. Red flags surface when a queue crosses a service-level threshold. The supervisor flexes agents between queues to rebalance without opening a routing config.
The agent utilization grid shows every agent with their current status (available, on call, in wrap-up, on break, in training, offline) with time-in-status. The supervisor spots agents stuck in wrap-up and jumps in to help without opening a slack conversation.
Service-level trends show attainment versus target over 24 hours, 7 days, and 30 days per queue. The ops manager spots the time-of-day gaps (Monday morning spike, Thursday afternoon dip) and tunes shift staffing grounded in actual pattern data.
The coaching priority queue ranks agents by QA score trend, outcome quality, and escalation rate. The supervisor sees the top three agents who need coaching attention this week with the conversations that illustrate the opportunities. The coaching cycle gets focused instead of random.
The compliance review queue surfaces conversations flagged for missing disclosures, off-script moments, or regulatory risk. The compliance officer reviews each flagged conversation, confirms or clears the flag, and feeds the pattern back to the training team.
For contact centers that run outbound campaigns and inbound campaign-attributed calls, Strkr tracks campaign performance (contact rate, conversion rate, cost per outcome). The marketing team and the ops team see the same numbers and have the same conversation about what works.
Most contact centers run a stack of a dedicated contact-center platform (NICE, Genesys, Five9) plus a generic CRM (Salesforce Service Cloud, Zendesk) plus a QA tool. The comparison below is Strkr's contact-center posture versus that legacy stack.
| Feature | Strkr | Legacy contact-center stack |
|---|---|---|
| Queue-aware screen pop with AI brief | Native with contextual AI draft. | Basic screen pop, no AI brief. |
| Outcome capture with keyboard-first | Flat picklist with hotkeys. | Multi-field dropdown tree. |
| AI QA scoring on 100 percent of conversations | Ships with voice recording in 2027-Q1. | Sample-based with limited scoring. |
| Blended queue agent assignment | Unified capacity view. | Separate outbound and inbound tools. |
| Compliance guardrails with disclosure prompts | In-moment prompts with audit evidence. | Post-call compliance review only. |
| Shift handoff summary | AI-drafted with open commitments. | Manual spreadsheet or email. |
| Native SMS and MMS via Strkr Messaging | Included channel. | Third-party add-on. |
| Pricing basis | Flat per seat with Strkr Messaging usage. | Per-agent-hour plus platform fee plus seat fee. |
| Deployment time | Three to four weeks for a 60-agent center. | Three to six months typical. |
| Integration surface | Open API and webhook spec. | Closed platform with limited API. |
The playbooks below are patterns Strkr's onboarding team sees on contact-center deployments across inbound, outbound, and blended operations.
A 42-agent inbound sales center for a consumer finance product ran Salesforce Service Cloud plus a dialer plus a QA tool across three screens per agent. Average handle time ran 11 minutes. Strkr onboarding consolidated the agent desktop into one surface. Handle time dropped to 7 minutes 20 seconds in the first month because the agent stopped Alt-Tabbing. Conversion rate held at the previous level because the context did not disappear.
A 90-agent outbound shop running fundraising campaigns for nonprofit clients needed TCPA compliance evidence on every outbound dial. Strkr onboarding configured the TCPA consent tracking, the DNC scrubbing against state and federal lists, and the campaign performance reporting. The compliance review queue caught 11 potential TCPA exposures in the first quarter that previous spot-check QA had missed.
A 180-agent collections operation needed FDCPA compliance (mini-Miranda disclosure, prohibited practices avoidance, dispute handling) on every call. Strkr onboarding configured the mini-Miranda prompt at call open, the dispute flow (ceases contact until written validation), and the audit log retention set to seven years. The compliance officer stopped manually reviewing every dispute case because the workflow enforced the required steps.
A 60-agent healthcare support center for a mid-size insurer needed HIPAA compliance on every contact. Strkr onboarding configured HIPAA authorization checks on screen pop, role-scoped PHI visibility, and the BAA covered the data processing. Agent satisfaction improved because the compliance friction was built into the workflow instead of being a separate step to remember.
A 28-agent blended ops team for a SaaS vendor flipped between outbound renewal calls and inbound support calls based on queue depth. Strkr onboarding configured the blended queue capacity view and the automatic shift-flex rules (when inbound queue exceeds depth 20, outbound dialers pause and agents flex to inbound). Inbound SLA attainment improved from 72 percent to 94 percent without hiring.
Book a 30 minute call. We will walk through the agent desktop, the outcome capture, the QA scoring, and the compliance guardrails with your actual queue structure and campaign mix.
Strkr integrates with legacy contact-center platforms through their published APIs and webhook interfaces. For most mid-size contact centers (under 200 agents) Strkr's native stack (CRM plus Strkr Messaging for SMS and MMS, with voice features coming in 2027-Q1) is the full solution. For centers over 200 agents or in regulated verticals where a voice platform is already in place, Strkr sits alongside the legacy platform as the CRM and QA scoring layer with voice events flowing through API.
Voice features (click-to-call dialer, call recording, AI call summary, real-time coaching nudges) are on the roadmap for 2027-Q1. In the meantime Strkr Messaging covers SMS and MMS end to end with native 10DLC registration, STOP and HELP keyword enforcement, and TFN verification. For contact centers that need voice today Strkr integrates with existing voice platforms through API with CRM and QA scoring as the Strkr layer.
Strkr's compliance surface tracks required disclosures per call type with disclosure prompts shown to the agent at the right moment in the conversation. For TCPA Strkr tracks express written consent per contact and scrubs against the DNC registry on every outbound attempt. For FDCPA Strkr prompts the mini-Miranda at call open and tracks dispute handling. For HIPAA Strkr tracks authorization status on contact and limits PHI visibility by role. BAA available for HIPAA-covered contact centers. SOC 2 Type II in progress with 2026-Q4 target.
Strkr prices flat per seat: $29 per user per month on Starter, $59 on Pro, $119 on Scale, with a 14-day trial. Strkr Messaging usage runs at 2 cents per SMS segment and 5 cents per MMS. The AI Enhancement module is a $49 per month add-on. Legacy contact-center platforms (NICE, Genesys, Five9) typically run $90 to $250 per agent per month plus per-minute voice costs plus professional services deployment of $200,000 to $800,000 for a mid-size center. Strkr's advantage is the flat seat pricing, the native SMS and MMS, and the open API.
For a typical 60-agent contact center Strkr onboarding runs a three to four week deployment. Week one is discovery and queue mapping. Week two is agent desktop configuration and QA rubric setup. Week three is pilot with eight agents against a shadow queue. Week four is full cutover with the legacy stack in parallel for safety. For centers over 200 agents the deployment stretches to six to eight weeks with a dedicated onboarding architect. For smaller shops (under 20 agents) the deployment runs two weeks.
Yes. Strkr ships blended queue capacity management with automatic shift-flex rules (when inbound queue exceeds a configurable depth, outbound dialers pause and agents flex to inbound). Agents who are certified for both inbound and outbound see unified capacity in the agent desktop. The ops manager tunes the flex thresholds based on real pattern data from the service-level trends view. Service-level attainment typically improves by 15 to 25 percent in the first month after cutover.
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