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A blueprint for a system that scores and routes every trial signup, runs onboarding that adapts to what each user actually does, flags accounts that stall or surge, and drafts support replies for your team to approve. Software does the watching and drafting. People make the judgment calls.
Solution blueprint: a system Callimachus Forge can architect for your operation. Not a case study, and not a claim of past deployment.
At a glance
Stack candidates
n8n · HubSpot · Stripe · Postgres · Gmail · Slack · Calendly · Vector database
System run
A simulated run of the exact steps documented on this page, from first trigger to final handoff. Nothing here is live client activity.
Simulation · Loops
The problem
Most SaaS teams treat every trial the same. A signup lands, a generic welcome email goes out, and then everyone waits to see who converts. Sales has no idea which trials are worth a call. Onboarding emails fire on a fixed schedule whether the user is stuck on step one or already inviting teammates. Nobody notices an account has gone quiet until the trial expires. Meanwhile support tickets pile up in a shared inbox, and the same setup questions get answered from scratch every day. The information needed to act well already exists: who signed up, what they did in the product, what they asked support. But it lives in four different tools, and nobody has time to connect it.
This blueprint fits a self-serve or trial-led SaaS company with a small go-to-market team: a founder or one or two salespeople, someone wearing the customer success hat, and support handled by whoever is available. Signups arrive through the product, billing runs through Stripe, and there is a CRM that is perpetually out of date. Product usage data exists in the application database or an analytics tool but is not connected to any outreach. Signup volume is high enough that manually reviewing every trial is impossible, and low enough that hiring a dedicated lifecycle team is not on the table.
The manual process
The automation opportunity
Every stage of this lifecycle runs on information the company already has. A signup can be looked up and scored the moment it arrives, so sales only sees trials worth a call. Onboarding messages can react to what a user actually does instead of following a fixed calendar. Quiet accounts and surging accounts can be flagged automatically, with a suggested message already written. Support tickets can be sorted and answered in draft form before anyone opens the inbox. The pattern throughout is consistent: software does the watching, the sorting, and the first drafts, and a person makes the calls that need judgment, like whether to offer a discount or how to handle an unhappy customer.
Architecture
Every step is typed: triggers, AI decision points, business logic, actions, data, and the human checkpoints that keep judgment where it belongs.
AI components
Human checkpoints
A new signup in the product fires a webhook into the n8n orchestrator, carrying email, company domain, plan selected, and signup source. Stripe events cover paid conversions and billing changes on the same rail.
The workflow looks up the signup's company domain through an enrichment API: company size, industry, tech stack where available. Results are written to the CRM contact record so every later step sees the same data.
A language model reads the signup and enrichment data and assigns a fit tier plus a likely use case, with a short stated reason. Output is structured JSON and is validated before it touches the CRM.
Deterministic rules act on the fit tier: high-fit trials get a sales owner and a Calendly booking link, mid-fit trials enter a guided track, low-fit trials go self-serve. Rules live in one place and are auditable.
The assigned track sends onboarding emails through the CRM or Gmail. Each send checks current usage first: a user who finished setup skips the setup nudge and gets the next relevant step instead of a redundant message.
Product events such as logins, key feature actions, and teammate invites land in Postgres. A scheduled job computes per-account flags: activated, stalled, surging, approaching trial expiry.
n8n polls the account flags on a schedule and also receives inbound support tickets from the shared inbox or a form. Any state change, such as a stall, a surge, a new ticket, or an expiring trial, creates a work item.
For each work item, a language model drafts the response: outreach for a stalled or surging account, or a triaged support reply. Drafts pull product documentation from a vector database so answers cite real steps.
Drafts post to a Slack channel with account context and Approve, Edit, or Discard actions. Refunds, discounts, angry customers, and anything the model flags as low confidence always stop here. Nothing sends itself.
Approved messages go out through Gmail or the CRM, the ticket or task is closed out, and the CRM stage advances. Every sent item records who approved it and which draft version went out.
A dashboard shows trials by stage, flagged accounts, and ticket load. A weekly Slack digest summarizes what was sent, what converted, and which drafts humans rewrote most, which feeds prompt improvements.
Engineering notes
Benefits are qualitative by design. We do not publish numbers we cannot verify.
The free audit maps your actual tools and process against this architecture, and tells you honestly what is worth building.