The sequencer does not generate anything. It substitutes a string that was computed earlier and stored on the lead.
Generate two fields, never the letter
Hey {{firstName}},
{{observation}} ← generated, 15–25 words
[fixed offer skeleton, identical across the whole batch]
{{question}} ← generated, one sentence
{{senderName}}
A fully generated email always drifts to the mean: correct, smooth, faceless. The fixed skeleton holds the voice; the model only does the part a template cannot — read someone else's document and form one thought about it.
Roughly 30 of 45 words move. That is enough.
The pipeline
1. signal ATS aggregator → posting + raw quote
2. people domain → person search → name, title, LinkedIn, email
3. text model on the quote → observation + question + confidence
4. gate branch → confidence < 4 → human review queue
5. load connector → lead with custom fields
6. send sequencer → 20–30 per mailbox per day
All six can live in one workbook if the platform has connectors to the sequencer. Check before designing — a missing final connector turns this into CSV export by hand.
Feed the model facts, not a brief
Input is a fixed record, not "write a personalized email":
| Field | Example |
|---|---|
| quote from the posting | "Our application is a Majestic Monolith built with Rails…" |
| posting title | Senior Software Engineer |
| days open | 8 |
| recipient title | Senior Director of Software Engineering |
| company and size | 142 people |
Output is fixed too: observation ≤ 25 words, question one sentence, confidence 1–5.
One hard constraint in the prompt: the observation may rest only on the quote. Not "I saw your LinkedIn", not "as a fast-growing company". No quote means no observation.
The gate
Without it this is a spam cannon with extra steps.
A lead does not enter the campaign when:
- confidence is below 4
- the line contains template tells: I noticed · as a · impressive · exciting
- the observation runs longer than 25 words — the model started summarizing the posting
- the quote is a false positive on the keyword
Everything blocked goes to a human review queue. Expect about one lead in seven. If it is far below that, the gate is not doing anything; if it is far above, the source data is too thin.
The fallback route matters as much as the main one
A lead with no quote is not discarded and is not given an invented observation. It moves to a separate campaign on a weaker signal, using a skeleton with no generated line — only plain variables.
Measure the two campaigns separately. Merged, the weak track drags the strong one and you kill a hypothesis that was working.
What automation does not change
Send limits stay. Hyper-personalization does not buy volume. 20–30 per mailbox per day, two to three weeks of domain warm-up, SPF/DKIM/DMARC green, inbox placement tested. The best copy in a spam folder returns zero and a false verdict on the hypothesis.
The top of the list stays manual. The 20–30 highest-scoring accounts never enter the pipeline. A human writes those. Automation takes the middle, not the peak.
Cost is per lead, and it is small. Roughly 1.5 credits for the person, 1 for the generated line, pennies for the posting. A 300-lead batch lands near 750 credits. Say the number out loud before building — it is usually lower than the team fears, which changes the conversation about scope.
Output
- Node diagram of the six steps with the actual tool at each one
- The exact input record and output schema for the generation step
- Gate conditions as a checklist
- Fallback route
- Cost per lead and per batch
- What stays manual