B2B SaaS, dev tools, MSPs, AI-native startups. We build the support, sales-ops, and content workflows that your AE team and engineering team would build if they had a week to spare. They don't. We do.
32 named workflow automations across SaaS, dev tools, IT services, and AI-native shapes
Each card opens a playbook built for a firm shaped like yours — with real workflows and a number on the outcome.
$1M-$50M ARR B2B SaaS. Product-led or sales-assisted. Lifecycle + support + customer success is the operational stack.
Recovers $200-500k/yr in saved ARR + accelerates product-led conversion 15-25% at a $10M ARR SaaS.
API-first product or dev tool. Adoption + community + technical support is the lifecycle. Developer experience determines retention.
Saves $200-400k/yr in scaled DevRel cost + measurable developer satisfaction lift.
Managed service provider serving 20-200 SMB or mid-market clients. Ticket volume + remote management + recurring revenue + technical project work.
$250-500k/yr in capacity + measurable client retention impact.
Early-stage AI-native company building model-powered products. Small team, big inference cost, rapid iteration. Cost discipline + eval pipelines + customer feedback loops are existential.
Compounds in product velocity + retention.
You know what's broken. Here are the parts the system can run without you.
What works at $1M ARR breaks at $5M. Personalized lifecycle + targeted CS + product-led activation are exactly what AI is built for.
Volume scales linearly with revenue; team scales every fundraise. The ratio breaks. AI handles 50-60% of routine tickets; humans focus on the actual hard ones.
CS is supposed to drive expansion but spends days reviving deactivated trials. AI handles the predictable; CS does the strategic.
Every ops process is a Notion doc that someone wrote in 2023. AI runs the actual workflows + keeps docs in sync.
AI-native: inference can spike 30% from a regression overnight. Without anomaly detection, you find out at month-end billing.
Support, sales calls, Discord, NPS, social, GitHub. Without consolidation, the loudest customer wins. AI consolidates and ranks by frequency + severity.
Named workflows, not categories. Expand a group to see exactly what each one does and where it slots into your stack.
From signup to expansion, automated against actual product behavior.
Trial-to-paid lifecycle orchestrator
LiveNew trials trigger personalized onboarding sequence based on use case signals; activation events trigger upgrade nudges; deactivation triggers reactivation.
Activation event triggers
LivePer-product key activation events (first integration / first invite / first export) trigger contextual in-app + email follow-up.
Renewal motion drafter
Live60 days pre-renewal: drafts account-specific renewal motion (executive summary, usage trends, ROI estimate, expansion opportunities) for CS review.
Sales-call insights extractor
LiveSales call transcripts auto-parsed into structured insights (objections, competitive mentions, feature requests, pricing concerns, decision criteria); piped to CRM + product + revenue ops.
Outbound sequence personalizer
Ready on engagementDrafts personalized outbound sequences from prospect signals (LinkedIn activity, company news, hiring patterns); SDR reviews and approves.
Auto-resolution, intelligent escalation, proactive CS.
Support ticket auto-resolution
LiveTickets auto-classified; 50-60% auto-resolved with brand-voice replies; complex tickets escalate with full customer + product context.
Technical ticket draft response
LiveTechnical tickets get drafted response from documentation + prior tickets + relevant code; engineering reviews before send.
Churn signal detector
LiveContinuously monitors usage + support + key-user signals; aggregates into churn risk score; top-decile triggers CS + exec outreach.
Onboarding milestone tracker
LiveTracks every customer through structured onboarding milestones; surfaces stuck accounts to CS within 24h of stall.
Community question auto-responder
LiveDiscord/Slack/GitHub questions auto-classified + auto-answered for known patterns; novel surfaces to team.
Engineering productivity + reliability + cost discipline.
Inference cost anomaly detector
LiveAI-native: monitors per-model + per-tenant inference cost continuously; alerts on >X% deviation within hours.
Eval pipeline failure alerter
LiveAI-native: eval failures surface immediately with diff against prior runs + suspected commit.
Documentation auto-updater
Ready on engagementDev tools: monitors API surface for changes; flags affected documentation pages; drafts updated copy for maintainer review.
Usage anomaly alerter
LivePer-customer + per-feature usage anomalies (sudden spike or drop) surfaced for engineering + CS review.
Deploy + incident postmortem assembler
Ready on engagementAfter incident or deploy, drafts postmortem from monitoring data + Slack timeline + ticket impact; queued for engineering review.
Internal workflows that scale with the company.
Cross-channel feedback consolidator
LiveFeedback from support / Intercom / Discord / sales calls / NPS / GitHub auto-classified and aggregated into structured product feedback database.
Internal knowledge search
LiveAI-searchable knowledge base across docs, decisions, architecture, and prior tickets, so engineers don't ask the same question 3 months apart.
Hiring pipeline coordinator
LiveInbound applications screened + ranked + scheduled; interview feedback consolidated; offer drafts generated.
Internal status assistant
Ready on engagementEngineering / sales / CS daily standup auto-drafted from PR activity + ticket activity + meeting notes; surfaces blockers.
The MSP operational stack.
Ticket auto-triage + L1 resolution
LiveMSP: tickets auto-classified by tier + technology + urgency; L1 attempted auto-resolution before tech engagement.
NOC alert correlation
LiveMSP: monitors RMM/SIEM alerts continuously; correlates related alerts to suppress noise; surfaces real incidents with priority.
QBR auto-assembler
LiveMSP: 10-day pre-QBR cron pulls all client data and assembles QBR deck in MSP standard format; AM reviews and edits.
RMM script orchestrator
LiveMSP: common ticket types trigger pre-approved RMM scripts (clear cache, restart service, fix DNS) automatically with audit log.
Client onboarding workflow
LiveMSP: new client signed → structured onboarding (network discovery, RMM agent deploy, security baseline, documentation, kickoff meeting).
We ship in phases, each with a measurable success criterion before the next phase begins.
Weeks 1-4
By end of Phase 1, support response time drops 80%+, trial conversion lifts 10-20%, and at-risk accounts surface 30+ days before churn.
Weeks 5-12
By end of Phase 2, community handled at 100x scale by 2-person DevRel and technical tickets resolve 50%+ faster.
Weeks 13-20
By end of Phase 3, cost surprises caught in hours, eval failures surface immediately, and engineering signal-to-noise improves.
Weeks 21-26
By end of Phase 4, renewal motions are consistent, QBRs are draft-ready by AM, and the company scales the next $10M ARR without proportional ops hiring.
Software is the most automation-friendly surface, but governance still matters around customer comms, code changes, billing, and security actions.
Most B2B SaaS buyers require SOC 2 by mid-market. The platform must support customer-facing SOC 2 evidence requests.
B2B SaaS handling EU/CA customer data triggers privacy obligations cascading to your customers' end users.
Healthcare-adjacent SaaS, MSP-supporting medical practices, dev tools used by healthcare customers, all touch HIPAA.
AI-native products carry obligations around model behavior, output safety, training data, and customer transparency.
Real, measurable pilots with explicit success criteria, so the answer at the end is "yes, kept" or "no, scrapped" — not "maybe."
Run auto-resolution on top 5 ticket categories for 30 days.
Success criteria
Connect product usage + support data + billing for 90 days.
Success criteria
Auto-assemble QBRs for top 20 clients for one full quarter.
Success criteria
Your systems of record stay. We plug into them and become the connective tissue between the tools you already pay for.
Tell us your stage, primary product type (B2B SaaS / dev tools / MSP / AI-native), and the workflow that costs you the most ops time. We'll come back with a written map of which 5-7 automations matter first and what the first 90 days would change.
