Enrichment

Each section is the output.schema and prompt for one common Clay table. Run them with the steps in Move a table: rows go in as input_rows with your record ID, and the ID comes back on every row.

Inbound leads

Clay enriches each lead the moment its webhook fires. Autumn has no inbound webhook, so collect leads in your own queue and send each batch as one task.

{
  "prompt": "These are new product sign-ups. For each, identify the person and their company from the email and name. Return one row per input row, keeping each lead_id. For personal email addresses, only fill company fields if the person can be identified with confidence; otherwise leave them empty.",
  "input_schema": {"lead_id": "str", "email": "str", "name": "str"},
  "input_rows": [{"lead_id": "su_1042", "email": "ada@example.com", "name": "Ada Lovelace"}],
  "output": {
    "id": "inbound-leads", "kind": "research", "path": "outputs/inbound-leads.jsonl",
    "schema": {
      "company": {"type": "str"},
      "company_domain": {"type": "domain"},
      "employee_count": {"type": "int"},
      "title": {"type": "str"},
      "seniority": {"type": "str", "description": "ic, manager, director, vp or c_level"},
      "linkedin_url": {"type": "url"}
    }
  },
  "rules": {"identity": "The company and title belong to this person, not someone with the same name."},
  "clarify": false
}

In a four-lead test, the three work emails came back with company, headcount, title and seniority. A Gmail address with a common name came back empty with rule_identity: unclear instead of a guess. Keep the score formula from Clay in your code and run it on these fields.

Work emails and phone numbers

A Clay waterfall tries providers in order until one returns a value. In Autumn, state the bar the value has to meet and ask for a status next to it:

"schema": {
  "work_email": {"type": "str"},
  "email_status": {"type": "str", "description": "verified, likely, or not_found"},
  "phone": {"type": "str"},
  "phone_status": {"type": "str", "description": "verified, likely, or not_found"}
}

with this in the prompt: “For email and phone, only return a value you found or verified, and set the status to say how confident you are. Leave it empty rather than guessing.” Your code decides what each status is allowed to do (for example, only verified emails go to the sequencer).

AI and Claygent columns

Each AI column becomes one field. Put the question and its allowed answers in the description, and anything that must hold for the row in rules:

Clay column	Autumn field
Claygent: “Does this company sell to developers? Yes or no.”	`"sells_to_developers": {"type": "str", "description": "yes, no or unknown. Does the company sell to developers?"}`
AI: “Summarize their latest product launch in one sentence.”	`"latest_launch": {"type": "str", "description": "One sentence on the most recent product launch, with its date"}`
AI: “Is this a B2B company?” used to filter rows	`"rules": {"b2b": "Sells mainly to businesses."}`

unknown matters: without it, a yes or no question gets a guess.

Job changes

Send each contact’s last known title and company. Autumn returns the current ones:

{
  "prompt": "For every input contact, check whether they still hold the title and company we have on record. Return one row per input row, keeping each row's record_id. Fill current_title and current_company from the most recent dated evidence, set changed to true only when the evidence shows they moved company, and give the date of the change when it is known. Leave a field empty rather than guessing.",
  "input_schema": {"record_id": "str", "full_name": "str", "title": "str", "company": "str"},
  "input_rows": [{"record_id": "003A01", "full_name": "Ada Lovelace", "title": "CTO", "company": "Analytical Engines"}],
  "output": {
    "id": "job-changes", "kind": "research", "path": "outputs/job-changes.jsonl",
    "schema": {
      "current_title": {"type": "str"},
      "current_company": {"type": "str"},
      "changed": {"type": "bool"},
      "change_date": {"type": "str", "description": "YYYY-MM or YYYY-MM-DD when known"}
    }
  },
  "clarify": false
}

In a three-contact test, two people who had moved came back changed: true with their new company and the month they moved; the one who hadn’t came back false. Run it on the signal’s old schedule and act on changed: true rows.

Refresh CRM records

Export the records with their CRM IDs, run them through the same task as the table that first enriched them, then write back only fields whose value changed and whose row passed every rule. The diff in compare.py from Move a table works the same way; swap its Clay loader for one that reads the CRM export.

Structured output

Field types for output.schema.

Signals

Funding, news and hiring events on a schedule.

Start here

By workflow

Inbound leads

Work emails and phone numbers

AI and Claygent columns

Job changes

Refresh CRM records

Structured output

Signals

​Inbound leads

​Work emails and phone numbers

​AI and Claygent columns

​Job changes

​Refresh CRM records

Structured output

Signals

Inbound leads

Work emails and phone numbers

AI and Claygent columns

Job changes

Refresh CRM records