Each company gets one draft based on its own website. The AI prepares the research and message. A person decides whether it is worth sending.
It is the last 30 seconds of human judgment: read the draft, decide if the lead is worth contacting, edit if needed, then send or skip.
The result in Airtable
The final table is the review queue. Good rows get Status = Done, plus a draft message and research notes. Broken, parked, or unusable websites are routed to Status = Blocked or Status = Needs review instead of forcing a fake personalization.
The full workflow map
The canvas starts with pending Airtable leads, loops through each row, researches the website, checks whether the research is usable, generates a draft only when the site is usable, saves the result, and waits three seconds before the next lead.
Research the company first
The Research Company node calls Groq using groq/compound-mini. Its job is narrow: visit the public website and return factual JSON with a research status field. In this workflow, that field is expected to be done, blocked, or unclear.
The research prompt asks for what the company sells, how customers book or inquire, visible calls to action, and a few useful personalization facts. It explicitly tells the model not to invent content for broken or empty websites.
Draft from one real detail
The draft step uses DeepSeek to write a short message from the research. The instruction is intentionally restrained: 3-5 short sentences, under 80 words, one specific detail, no fake enthusiasm, no invented statistics, no generic "businesses like yours" line.
Route bad inputs away from drafting
A practical outreach workflow should not pretend every website is usable. If parsing fails, Airtable gets Status = Needs review. If the website is blocked, broken, parked, or too thin, the row can be updated to Status = Blocked.
Why the cost is low
This pattern is cheap to test because each row is a small research-and-draft job, not a long agent session. In my Groq account, 16 days of workflow testing showed Current Monthly Usage = $0.26. That included more than this one workflow and a day where I tested around 100 runs across different models. DeepSeek draft testing was under $0.10.
That is not a production guarantee. Real cost depends on website size, model choice, prompt length, retries, and how many leads you run. But for a small outreach test, the bigger cost is usually not model tokens. It is finding a clean lead list and spending the review time well. A small Upwork research task can produce a niche list of real businesses with websites and public contact paths for a low cost.
Airtable: stores the review queue, status, notes, and draft message.
Groq Compound Mini: researches each public website and returns compact factual JSON.
DeepSeek: writes one short draft from the research instead of a generic template.
Human review: protects the workflow from becoming an auto-send spam machine.
Good fits
A freelancer or small team can review 20-50 researched leads instead of sending the same email to a scraped list.
A store can research potential stockists, draft a note from their product fit, and approve only the contacts that look relevant.
Form or Sheet rows can be enriched before a Gmail draft, Viber follow-up, or owner task is created.
What I would improve next
Add Approved, Skipped, and Sent states in Airtable so sending is a separate decision after review.
After approval, n8n could send the message automatically through Gmail or another tool, then log the sent timestamp and message id.
Add a 3-5 day follow-up draft for approved leads that did not reply, with a clear stop condition.
Check every lead against skipped domains, existing contacts, unsubscribes, and companies that should never be contacted again.
For some Philippines workflows, the output might be a Gmail draft, LinkedIn note, Viber follow-up, WhatsApp draft, or owner task instead of an email.
Bottom line
This workflow is useful because it avoids the weakest part of many outreach systems: a generic message sent to every company. It turns public website context into a draft that a person can review.
It is not a promise of sales results. It is a small, inspectable system for doing better first-contact prep: real research in, short draft out, human approval before send.