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.
Each card is a real persona, a real day-in-the-life narrative, a real automation playbook, and a real example with a number on it. Click in to see how the platform runs inside a firm shaped like yours.
$1M-$50M ARR B2B SaaS. Product-led or sales-assisted. Lifecycle + support + customer success is the operational stack.
Estimated annual value
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.
Estimated annual value
Handles 80-90% of community questions automatically + reduces median response time 95%+ = 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.
Estimated annual value
Saves ~1,000-1,500 AM hours/year at a 25-employee MSP + improves QBR consistency = $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.
Estimated annual value
Recovers ~10-20 founder hours/week + dramatically improves product feedback signal-to-noise = compounds in product velocity + retention.
We're not going to tell you software & tech is broken. You know what's broken. We're going to tell you which 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.
One nervous system, written in your firm's language. n8n is the backbone. Zapier is the glue. Supabase + Claude is memory and reasoning. The result is a single layer that thinks, remembers, and acts on behalf of your team, without ripping out a single system you already use.
brain
n8n + Claude. Lifecycle decisioning, support triage, churn detection, eval analysis, alert correlation, QBR assembly.
memory
Supabase + pgvector. Stores customer history, product usage, conversation memory, codebase knowledge, eval history.
nervous system
Product event webhooks (signup, activation, key actions), billing cycles, renewal cycles, alert pipelines, deploy events.
hands
Sends in-app messages, drafts emails, posts to Slack, executes RMM scripts, creates tickets, generates documents.
eyes
Product analytics, billing data, support tickets, community channels, sales call transcripts, monitoring data, GitHub activity, social mentions.
The point of separating these layers is reusability. The same Brain and Memory power your client acquisition, document handling, and compliance workflows. New automations are written as new Hands , not as a new system. That's the difference between an AI Operating System and a stack of one-off Zaps.
Each entry below is a named workflow, not a category, not a promise. Every row describes what the workflow actually does in verb form, what it touches, and how it slots into your stack.
From signup to expansion, automated against actual product behavior.
Trial-to-paid lifecycle orchestrator
New trials trigger personalized onboarding sequence based on use case signals; activation events trigger upgrade nudges; deactivation triggers reactivation.
Activation event triggers
Per-product key activation events (first integration / first invite / first export) trigger contextual in-app + email follow-up.
Renewal motion drafter
60 days pre-renewal: drafts account-specific renewal motion (executive summary, usage trends, ROI estimate, expansion opportunities) for CS review.
Sales-call insights extractor
Sales 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
Drafts 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
Tickets auto-classified; 50-60% auto-resolved with brand-voice replies; complex tickets escalate with full customer + product context.
Technical ticket draft response
Technical tickets get drafted response from documentation + prior tickets + relevant code; engineering reviews before send.
Churn signal detector
Continuously monitors usage + support + key-user signals; aggregates into churn risk score; top-decile triggers CS + exec outreach.
Onboarding milestone tracker
Tracks every customer through structured onboarding milestones; surfaces stuck accounts to CS within 24h of stall.
Community question auto-responder
Discord/Slack/GitHub questions auto-classified + auto-answered for known patterns; novel surfaces to team.
Engineering productivity + reliability + cost discipline.
Inference cost anomaly detector
AI-native: monitors per-model + per-tenant inference cost continuously; alerts on >X% deviation within hours.
Eval pipeline failure alerter
AI-native: eval failures surface immediately with diff against prior runs + suspected commit.
Documentation auto-updater
Dev tools: monitors API surface for changes; flags affected documentation pages; drafts updated copy for maintainer review.
Usage anomaly alerter
Per-customer + per-feature usage anomalies (sudden spike or drop) surfaced for engineering + CS review.
Deploy + incident postmortem assembler
After 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
Feedback from support / Intercom / Discord / sales calls / NPS / GitHub auto-classified and aggregated into structured product feedback database.
Internal knowledge search
AI-searchable knowledge base across docs, decisions, architecture, and prior tickets, so engineers don't ask the same question 3 months apart.
Hiring pipeline coordinator
Inbound applications screened + ranked + scheduled; interview feedback consolidated; offer drafts generated.
Internal status assistant
Engineering / sales / CS daily standup auto-drafted from PR activity + ticket activity + meeting notes; surfaces blockers.
The MSP operational stack.
Ticket auto-triage + L1 resolution
MSP: tickets auto-classified by tier + technology + urgency; L1 attempted auto-resolution before tech engagement.
NOC alert correlation
MSP: monitors RMM/SIEM alerts continuously; correlates related alerts to suppress noise; surfaces real incidents with priority.
QBR auto-assembler
MSP: 10-day pre-QBR cron pulls all client data and assembles QBR deck in MSP standard format; AM reviews and edits.
RMM script orchestrator
MSP: common ticket types trigger pre-approved RMM scripts (clear cache, restart service, fix DNS) automatically with audit log.
Client onboarding workflow
MSP: new client signed → structured onboarding (network discovery, RMM agent deploy, security baseline, documentation, kickoff meeting).
We don't ship a 30-workflow operating system on day one, that never works. We ship in phases, each with a measurable success criterion before the next phase begins.
Phase 1 · Weeks 1-4
Success criterion
By end of Phase 1, support response time drops 80%+, trial conversion lifts 10-20%, and at-risk accounts surface 30+ days before churn.
Phase 2 · Weeks 5-12
Success criterion
By end of Phase 2, community handled at 100x scale by 2-person DevRel and technical tickets resolve 50%+ faster.
Phase 3 · Weeks 13-20
Success criterion
By end of Phase 3, cost surprises caught in hours, eval failures surface immediately, and engineering signal-to-noise improves.
Phase 4 · Weeks 21-26
Success criterion
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.
Honest compliance copy beats aspirational compliance copy. Below: the frameworks we're configured to support, the controls we ship with, and the explicit boundaries, actions the AI never takes without a human signing off.
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.
Software is the most automation-friendly surface, but governance still matters around customer comms, code changes, billing, and security actions.
| Action | Decision | Why |
|---|---|---|
| Send a product event-triggered email | AI | In-product lifecycle, opt-out respected. |
| Auto-resolve a routine support ticket | AI | Within playbook; sentiment-flagged escalates. |
| Suppress an alert (alert correlation) | AI | Documented correlation rules; full audit. |
| Send a renewal motion outreach | AI with human approval | CS reviews drafted motion before send. |
| Execute an RMM script (MSP) | AI with human approval | Pre-approved script library; tech reviews on first execution per client. |
| Draft a postmortem | AI with human approval | Engineering reviews and edits before publication. |
| Deploy code to production | Human only | Always engineering decision through standard CI/CD. |
| Issue refund or credit > $X | Human only | Configurable threshold; finance approves above. |
| Make a security incident disclosure | Human only | Always security + executive + legal. |
Each of these is a real, measurable pilot you can run with us over a single quarter, 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. Measure resolution rate + CSAT.
Success criteria
Connect product usage + support data + billing for 90 days. Measure at-risk identification + intervention success.
Success criteria
Auto-assemble QBRs for top 20 clients for one full quarter. Measure AM time + client satisfaction.
Success criteria
Monitor inference cost continuously for 30 days. Measure incidents caught + savings.
Success criteria
Connect 4-5 feedback channels for 60 days. Measure product signal velocity + founder time.
Success criteria
Your firm has a system of record for a reason. We plug into it. The platform is the connective tissue between the systems you already pay for, not a competing system you have to migrate to.
Billing stays source of truth. We extend with renewal motion drafting + churn signal aggregation.
Product analytics stays source of truth. We feed activation event triggers + churn signals.
Helpdesk stays source of truth for tickets. We extend with auto-resolution + drafting.
Community stays where developers are. We extend with auto-response + question routing.
Monitoring stays primary. We extend with anomaly correlation + alert intelligence.
PSA + RMM stays source of truth. We extend with ticket triage + script orchestration + QBR assembly.
Below are the four pre-packaged engagement bundles available in software & technology - useful when you want a single signed PO instead of assembling the catalog. The full Software & Technologypractice covers more: see the full catalog and the multi-tab coverage matrix for the department, technology, and workflow lenses.
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.
