Built for revenue teams running AI plus workflow automation

Intelligent sales automation that pairs Strkr AI with deterministic Flows.

Point AI tools score leads in a vacuum, legacy workflow builders route records without judgment, and the gap between them is where deals get stuck. Strkr pairs Strkr AI with Flows inside one CRM so scoring, routing, drafting, and review share the same record and the same audit trail.

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What this audience is actually dealing with

The pains that bring buyers here.

Intelligent sales automation sounds like a solved problem until a revenue leader actually tries to stitch it together. The AI tools score leads with no CRM state. The workflow builder routes records without judgment. The AI drafter writes emails that reference account data the drafter never read. The LLM vendor charges per token with no cost ceiling. The audit log, when it exists, lives in a different system from the record. And nobody on the revenue team can explain why a lead got routed to a rep on a morning. The six pains below show up on every evaluation call we run with revenue leaders who have already tried the point-AI stack and come back looking for one surface. If any of them look familiar, the rest of this page shows how Strkr pairs the AI primitives (scoring, next-best-action, drafting, summarization) with Flows (deterministic routing and stage logic) inside one workspace with one audit trail and one flat per-seat bill.

Rules-only automation

Round-robin routing does not read deal context.

A legacy workflow builder routes new inbound leads with a round-robin rule or a static territory match. The rule fires on form fields the moment the lead lands and ignores the richer context (prior account activity, product fit, segment, firmographic match, buying signal from the website) that a human SDR manager would weigh in 15 seconds. Strkr runs Strkr AI scoring on the inbound lead first (fit score, intent score, segment match), then the Flow routes using the score plus the deterministic territory rule, so the right rep gets the right lead on the first touch.

AI tool disconnected from CRM

The AI scorer reads a snapshot and never updates.

A point-AI lead scorer syncs the CRM once a day, scores on yesterday data, and the sales team acts on a score that already drifted. Worse, the score lives in a dashboard outside the CRM and the rep has to open two screens to see it. Strkr AI runs on live CRM state (every call logged, every email replied, every stage move) and the score updates on the record the moment the signal arrives. The rep sees a current score on the lead card and the pipeline board, not a dashboard snapshot from yesterday morning.

No human-in-the-loop review

The AI drafter sends emails nobody read first.

The AI drafter writes an email based on a prompt and a few CRM fields, autosends it to the prospect, and the rep sees the result after the fact. For low-stakes touches that works. For a renewal nudge to a strategic account that looks fine on form and misreads the relationship, that becomes a Monday morning apology call. Strkr AI drafts land as a review task on the owning rep by default, with the rep approving, editing, or regenerating before send. The automation runs on autosend only where the rep explicitly flipped it on, and the audit log captures who approved what.

Fragile when data shape drifts

A new custom field breaks the whole scoring pipeline.

A revenue leader adds a new product-interest custom field to capture a new segment. The point-AI tool was trained on the old schema, the workflow builder references the old field in six rules, and the sync job silently drops records that have the new field populated. Strkr Layouts, Fields, Flows, and Strkr AI share the same metadata layer, so adding a field updates every surface at once. The admin surface shows which Flows and AI prompts reference the field before you archive it, and no silent breakage happens two weeks after launch.

No visibility into AI decisions

Nobody can explain why that lead got routed to that rep.

A deal escalates and the sales manager asks why this specific lead went to this specific rep three weeks ago. The point-AI vendor shows a model confidence score with no reasoning trace. The workflow builder shows the rule that fired but not the inputs the rule read. The sales operations lead spends an afternoon reconstructing the moment from logs that live in three systems. Strkr logs every AI decision (score, model, inputs, output, confidence) and every Flow run (trigger, inputs, branch taken, actions executed) against the record. The manager answers the question in 30 seconds from the record audit tab.

Per-token cost unpredictable

The LLM bill doubled last month and nobody can say why.

The AI tool bills per token with a monthly cap that nobody set properly. Usage spiked when the sales team ran a prospecting push, the bill arrived with a 2.3x surprise, and the finance partner asked an uncomfortable question in the Friday review. Strkr AI ships inside the flat per-seat plan with a monthly inclusion for scoring, drafting, summarization, and next-best-action requests. Heavy users on a usage-based add-on see a daily running total in the admin surface with soft and hard caps, so the Friday finance review stops producing surprises.

Strkr AI primitives

What Strkr AI actually does across the revenue motion.

Strkr AI is not a generic chatbot bolted onto a sidebar. It is a set of five concrete primitives (lead scoring, next-best-action, email drafting, call and meeting summarization, forecast risk tagging) that run on live CRM state, surface inside the records revenue teams already work from, and leave an audit trail on every decision. Each primitive ships on the paid tiers with a monthly inclusion for standard usage, and the admin surface controls which primitives run on which objects and which users. The six cards below describe the primitives as revenue teams actually use them, with concrete examples from the SDR, AE, CSM, and sales manager workflows.

Lead scoring

Fit and intent scores on every new lead within seconds.

A new lead lands from the form, the vendor fair, or an outbound sequence. Strkr AI scores fit (firmographic match against the ICP) and intent (behavioral signal from the website, prior touches, and segment) and writes both scores to the lead record within seconds. The SDR board shows the scores inline on the lead card so the SDR works the 85+ fit scores first and the 40 fit scores roll to the nurture flow. The scoring model trains on the tenant historical conversion data and recalibrates monthly.

Next-best-action

The AE sees the one concrete next move on every open deal.

A sales rep opens a stale deal and sees a Strkr AI next-best-action chip at the top of the record (schedule a demo, send a technical case study, loop in the champion contact, invite the economic buyer). The suggestion is grounded in the deal stage, the last touch, the account activity, and the pattern of similar deals that closed in the tenant history. The rep clicks the suggestion and the appropriate task, email draft, or meeting invite drafts into place.

Email drafting

First-draft outbound and follow-up emails grounded in the record.

The rep clicks Draft on the record and Strkr AI composes a first-draft email grounded in the account, the opportunity, the last three touches, and the stage. The draft lands in the compose surface for the rep to edit, personalize, and send, with a regenerate option for a different angle. The default posture is draft and review, with autosend available on specific Flow steps where the rep flipped the switch on explicitly.

Call and meeting summaries

Every call and meeting summarized with action items and sentiment.

Strkr AI ingests the call recording or meeting transcript, extracts the key moments, writes a short narrative summary, lists action items with owners, flags sentiment, and posts the result to the activity timeline. The rep moves on to the next conversation with the summary already on the record, the manager reviews the one-liner on the Monday roll, and the next participant in the deal walks into the following call with full context.

Forecast risk tagging

Open deals get risk flags grounded in the signal pattern.

Every open deal in the current forecast period runs through a weekly Strkr AI risk pass. Deals with slipping stages, cold activity patterns, missing champion contacts, or stalled next-step dates get tagged with a risk reason that posts to the pipeline review. The sales manager walks into the Monday forecast call with the risk list pre-sorted and spends the hour on the gaps instead of asking every rep the same stall questions.

Pipeline hygiene prompts

The AI nudges reps on specific gaps instead of nagging on everything.

Strkr AI runs a nightly hygiene pass on each rep pipeline and surfaces specific prompts (close date is 60 days stale on 7 deals, no next step on 4 opportunities, champion missing on 3 strategic accounts). The prompts land as a short morning task list, not a weekly email the rep ignores. The hygiene improves because the ask is specific and small, not a Monday manager rant.

Flows for deterministic logic

The workflow engine that handles the rules the AI should not decide.

Not every revenue decision belongs to an AI. Territory assignments, SLA timers, contract renewal cascades, approval chains, and compliance holds are deterministic rules that should fire the same way every time with no model drift. Strkr Flows handle the deterministic layer with a visual builder, 50+ triggers, atomic transitions, and the ability to call Strkr AI as a step inside a Flow when the right answer needs judgment. The pairing works because Flows own the deterministic path, Strkr AI owns the judgment call, and the admin surface shows the whole picture on one canvas.

Lead routing

Fit score plus territory rule equals the right rep on the first touch.

A Flow fires on new lead creation, waits for the Strkr AI fit score to post (usually within seconds), reads the score and the territory rule from the lead, and assigns to the right SDR with an SLA timer. The score and the rule both live in the Flow run log, so the SDR manager sees exactly why this lead went to this rep on this morning. Round-robin is still available for teams that want it, scoped under a conditional branch.

Stage entry criteria

A deal cannot move forward without the record it needs.

A Flow guards each pipeline stage transition with entry criteria (champion identified, economic buyer tagged, technical validation done, security review complete). Flows block the move with an inline prompt listing the missing items and the rep fills the gap before the stage change writes. The forecast picture stays honest because deals that look late-stage actually have the record a late-stage deal needs.

Approval chains

Discount approval routes to the right manager automatically.

A deal with a discount above the policy threshold triggers a Flow that routes to the sales manager, then the VP, then the CFO depending on the discount percentage and the ARR size. Each approver sees the deal snapshot, the discount reason, and the margin impact. Approval or rejection writes to the deal timeline and the Flow releases the deal to close or returns it to the rep with the reason.

Renewal cascade

Customer renewals cascade on fixed timing with CSM touchpoints.

A Flow fires at 180, 120, 90, 60, and 30 days before contract renewal with a different structured task at each milestone. The CSM runs the health review at 180, the AE joins at 120, the proposal drafts at 90, the executive touch happens at 60, and the sign-off task fires at 30. The flow runs the same way across the full book so the Q4 renewal spike never surprises the team.

AI as a Flow step

Call the AI from inside a Flow when the step needs judgment.

A Flow step can call Strkr AI as an action (summarize the last three touches, draft a follow-up email, suggest the next best action, flag anomalies in the deal data). The AI output becomes a value the next Flow step reads, so the deterministic workflow and the judgment call compose cleanly. The pattern avoids the common failure where AI tools and workflow builders cannot talk to each other.

Audit log per Flow run

Every run shows trigger, inputs, branch taken, actions executed.

Each Flow run writes an audit record with the trigger event, the input values, the branches it took, the actions it executed, and the final state. Flows that call Strkr AI capture the AI prompt, the model used, and the AI output alongside. The sales operations lead debugs a run in 90 seconds from the record audit tab instead of reconstructing the sequence from scattered logs.

Hybrid rules and AI patterns

Where the two layers actually combine in production.

The pattern revenue teams run on Strkr is not AI everything and not rules everything. It is a layered motion where Flows handle the deterministic plumbing and Strkr AI steps in for the judgment calls. The cards below describe six concrete hybrid patterns real teams run, with the deterministic rule, the AI judgment, and the handoff explained. Each pattern ships as a template on the Flow library and can be forked, customized, and deployed on the tenant within an afternoon by a sales operations lead with no engineering ticket.

Score-and-route

AI scores, Flow routes, SLA timer fires, SDR gets the right lead.

New lead lands, Strkr AI scores fit and intent, Flow reads the scores and the territory rule, routes to the SDR, fires a 10-minute first-touch SLA task, and if the SDR does not touch in 15 minutes the Flow escalates to the SDR manager. The deterministic SLA sits on top of the judgment-based score, and the whole thing runs without the SDR manager building a spreadsheet of overrides.

Draft-and-approve

AI drafts the follow-up, Flow routes to the AE for approval.

A pipeline stage change triggers a Flow, the Flow calls Strkr AI to draft a stage-appropriate follow-up email, the draft lands in the AE compose surface as a review task, and the AE approves, edits, or regenerates. On autosend-flagged steps the email sends directly with the AE still named as sender. The pattern cuts first-draft time to zero while keeping the AE in the loop on strategic touches.

Summarize-and-escalate

AI summarizes the call, Flow escalates if sentiment is negative.

A call recording lands, Strkr AI writes the summary and tags the sentiment, Flow reads the sentiment tag, and if the result is negative or at-risk the Flow fires an escalation to the AE manager with the summary pre-attached. Positive and neutral calls flow through normally. The escalation is specific and timely instead of a weekly roll where the problem calls are three days old.

Forecast-and-flag

AI tags risk, Flow surfaces the flag on the Monday review view.

The weekly Strkr AI forecast risk pass tags open deals with risk reasons, Flow aggregates the tags into the Monday forecast review saved view with the risk reason and the gap, and the sales manager walks into the forecast call with the pre-sorted list. The AE gets the risk list by email the night before so the Monday meeting runs on actual coaching not fact-finding.

Hygiene-and-nudge

AI finds the gaps, Flow sends the morning task list.

The nightly Strkr AI hygiene pass surfaces specific pipeline gaps per rep (stale close dates, missing next steps, missing champion contacts), Flow packages the gaps into a short morning task list that lands at 7:30am local time, and the rep clears the list in 10 minutes before the day starts. Managers see the aggregate hygiene score trending weekly without having to run a monthly audit.

Draft-and-send-on-rule

AI drafts the welcome email, Flow autosends on brand-new customers.

A new customer flips to Closed Won, Flow calls Strkr AI to draft a welcome email grounded in the deal context, the Flow autosends because the step is flagged autosend and the policy for welcome emails is low risk. The CSM gets the full copy on the record and the customer gets a timely, specific welcome instead of the generic template every vendor uses.

Head-to-head

Strkr intelligent sales automation vs point AI tools plus legacy workflow builder.

The common competitive stack for intelligent sales automation is one point-AI tool for lead scoring, a separate point-AI tool for email drafting, a workflow builder (Workato, Tray.io, Make, or the native CRM workflow engine), a separate call-summary tool, and the CRM underneath it all. Five vendors, five contracts, five admin surfaces, five audit logs. Strkr collapses the surface by shipping the AI primitives inside the CRM and pairing them with Flows under one workspace, one bill, one audit trail. The comparison below runs the stack line by line.

Feature Strkr Point AI tools + legacy workflow builder
Number of vendor contracts 1 (Strkr includes AI and Flows) 4 to 6 (AI scorer, drafter, call tool, workflow engine, CRM)
Lead scoring data source Live CRM state, scores update as signals arrive Daily sync, scores drift until next refresh
AI output review Draft and review by default, autosend opt-in per step Varies per vendor, often autosend with no review
Workflow and AI composition AI as a native Flow step, inputs and outputs flow through Webhook glue between tools, fragile and slow
Audit log One timeline covers AI decisions and Flow runs on each record Logs scattered across vendor dashboards
Admin schema changes Add a field, every Flow and AI surface sees it the same instant Update each vendor separately, risk silent breakage
AI pricing model Flat per-seat with monthly inclusion, no token surprises Per-token or per-call, bills swing month over month
Call summary location Native on the activity timeline with action items Separate tool, link posted to CRM
Forecast risk flags Native AI pass on open deals every week Separate forecasting tool or manual review
Routing determinism Flow handles rules, AI handles scores, both logged Depends on workflow engine, often opaque
Human-in-the-loop controls Per-step toggle for autosend, review by default Varies, often all-or-nothing per tool
Pricing model Flat per-seat covers AI, Flows, CRM, and audit Per-seat per tool, plus usage fees, plus implementation
How teams use Strkr

How revenue teams run intelligent sales automation on Strkr.

The patterns below show up across inbound-heavy SMB teams, mixed outbound and inbound mid-market teams, enterprise named-account teams, PLG motions with sales-assist, and customer success renewal teams. Each playbook pairs Strkr AI judgment calls with Flow determinism, with the triggers, inputs, and handoffs named. These are real motions a Strkr customer runs today, not hypothetical demos.

Inbound SMB SDR team

Score, route, SLA, draft, approve on every form fill.

A 6-SDR inbound team scores every new form fill with Strkr AI (fit plus intent), Flow routes to the SDR by territory and segment, fires a 10-minute SLA task, and the SDR gets a Strkr AI-drafted first-touch email ready to review. On high-intent 85+ leads the Flow adds a Slack ping to the SDR phone. The SDR clears the queue in 20 minutes a morning, the SDR manager sees the SLA pass rate on the dashboard, and the inbound conversion rate climbed 24 points in the first quarter after switch.

Outbound and inbound mid-market team

Hybrid motion with risk flags, next-best-action, approval chain.

A 12-rep mid-market team runs outbound against named accounts and inbound through the website. Strkr AI tags forecast risk weekly on open deals, Flows surface the risk list on the Monday review, and the AE walks in with a next-best-action chip on each at-risk deal. Discount approvals above 15 percent route through the sales manager, above 25 percent through the VP, with margin impact pre-calculated. The forecast accuracy moved from 72 percent to 89 percent in two quarters.

Enterprise named-account team

Strategic accounts with human-in-the-loop AI drafts.

An 8-rep enterprise team sells to 400 named accounts with 6 to 12 personas per account. Strkr AI drafts persona-specific outreach grounded in the account intelligence and recent activity, Flow routes drafts as review tasks to the owning AE, the AE approves or edits before send. Call summaries post to the account timeline with action items, next-best-action chips suggest the one concrete step on each open deal, and the quarterly business review packet pulls Strkr AI summary paragraphs for the account health section.

PLG company with sales assist

Product-qualified signals score leads, Flows handoff to sales.

A PLG company with 2,000 new sign-ups a month runs Strkr AI scoring on product-usage signals from the webhook feed (feature activation, team size, invite behavior, enterprise domain). Flows handoff the top 5 percent of PQLs to a sales-assist rep with a product-usage summary attached, the rep gets a Strkr AI-drafted outreach grounded in the features the prospect activated, and the conversion from PQL to opportunity moved 3.1x. The remaining 95 percent stay in self-serve with no human touch.

Customer success renewal team

Risk, draft, cascade, approve across the renewal calendar.

A 10-CSM team manages 800 customer renewals across quarterly cohorts. Strkr AI tags health risk on each account weekly, Flows cascade the renewal review at 180, 120, 90, 60, 30 days before contract end, and the CSM gets Strkr AI-drafted health check emails at each milestone. At-risk renewals auto-escalate to the CSM manager with a summary of the risk reasons. Gross renewal rate moved from 86 percent to 93 percent over the first three cohorts after the cascade stabilized.

See intelligent sales automation that actually pairs AI with Flows.

Start a 14-day trial with Strkr AI and Flows enabled on the full tenant. The admin surface walks a new tenant through the first lead score, the first Flow, the first draft, and the first audit review in under 30 minutes. One workspace, one bill, one audit trail for the full intelligent sales automation stack. The pricing page lays out the per-seat line and the AI inclusion limits before the trial starts.

See pricing How Flows work
Common questions

What buyers in this bucket ask most.

What is intelligent sales automation and why combine AI with workflows?

Intelligent sales automation is the pattern where a revenue team pairs deterministic workflow rules (routing by territory, approval chains, SLA timers, renewal cascades) with AI-driven judgment calls (fit and intent scoring, next-best-action suggestions, email drafting, call summarization, forecast risk flagging). Rules handle the parts that must fire the same way every time with full audit. AI handles the parts that need judgment on live data. Running them in separate tools creates fragile webhook glue, scattered audit logs, and admin surfaces that disagree. Running them in one CRM like Strkr means the AI output can feed a Flow step directly, the Flow can call the AI as an action, and the audit log is one timeline per record.

How is Strkr AI priced and how do I avoid surprise bills?

Strkr AI ships inside the paid tiers with a monthly inclusion for standard usage (scoring, drafting, summarization, next-best-action, hygiene prompts). The inclusion covers typical revenue team usage patterns on the tier. Heavy users on a usage-based add-on see a daily running total in the admin surface with soft and hard caps the admin sets per tenant and per user. Soft caps send a warning, hard caps pause further AI calls until the admin raises the cap or the next billing period starts. The admin surface shows the breakdown by primitive and by user, so the pattern of usage is visible before the invoice arrives.

Can I keep humans in the loop on AI-drafted emails and actions?

Yes, and that is the default posture. Strkr AI drafts for emails land as a review task on the owning rep by default, with the rep approving, editing, or regenerating before send. Autosend is available on specific Flow steps the admin or the rep explicitly flipped on, so the typical pattern is draft-and-review on strategic touches and autosend on low-risk templates (welcome emails, booking confirmations, nurture sequences). The audit log captures every draft, every edit, every approval, every autosend, with timestamps and actor, so the pattern of oversight is auditable.

How does Strkr AI stay accurate when my data model changes?

Strkr Layouts, Fields, Flows, and Strkr AI share the same metadata layer. When an admin adds a custom field, renames an existing field, or changes a picklist, every surface that reads the field sees the change at the same instant. The admin surface shows which Flows and AI prompts reference the field before you archive it, so you can confirm nothing silent breaks. Strkr AI scoring models retrain monthly on the current schema and the current conversion data, so model drift from schema drift stays bounded. The audit trail logs schema changes alongside AI and Flow runs, so a post-mortem on a scoring regression has the full picture.

What does the audit log actually record for AI decisions?

Every Strkr AI decision writes an audit record with the primitive called (score, draft, summarize, next-best-action), the inputs read (which record fields and which activity history), the model and version used, the output produced, the confidence score where applicable, the timestamp, and the actor who triggered the call. For automated primitives (nightly hygiene, weekly risk pass) the actor is the Flow or the system schedule. The audit record attaches to the record the AI acted on, so a sales manager asking why a lead was scored a way on a morning sees the full trace in 30 seconds from the record audit tab.

Does Strkr meet enterprise security and compliance requirements?

Strkr is HIPAA compliant with a BAA available for covered revenue teams in healthcare, SOC 2 Type II is in progress through 2026 Q4, and the platform meets GDPR and CCPA requirements for personal data handling. The open API lets security teams audit the AI calls in real time, pull audit logs for compliance review, and enforce retention policies at the tenant level. AI prompts and outputs are logged in the audit trail with the same retention controls as other activity data. Enterprise contracts cover custom data residency, SSO, SCIM provisioning, and tenant-specific key management for teams that need those controls.

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