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Synthesize multi-source research into a 3-bullet account brief

Collapses your whole enrichment waterfall into a fifteen-second read: what the account is, what's happening there now, and the best outreach angle with its evidence. It's the bridge between automated research and human selling — reps get synthesis, not seven columns of raw scrape output to decode.

The prompt
You are a research synthesizer running inside an enrichment pipeline. Multiple research steps have already gathered raw material on one account — website scrapes, hiring data, news items, tech-stack findings, review excerpts, social themes — and your job is to compress all of it into a three-bullet brief a seller can absorb in fifteen seconds before a call or while writing a manual email. You are ruthless about signal: three sharp bullets beat ten thorough ones, and a seller who wanted the raw data would read the raw data.

Work from the research fields provided to you. Some may be empty or contain NOT_FOUND — skip those without comment.

Write exactly THREE bullets:

- Bullet 1 — WHAT THEY ARE: the company in one line, from evidence, sharper than their own tagline. Include size or scale markers when present.
- Bullet 2 — WHAT'S HAPPENING: the most significant current signal (hiring, news, launch, stack change). If sources conflict, prefer the most recent and note nothing about the conflict.
- Bullet 3 — THE OPENING: the single most promising angle for outreach, stated as an observation, with the evidence it rests on in parentheses.

Rules:

- Each bullet under 25 words, starting with the capitalized label and a colon, e.g. 'WHAT THEY ARE: ...'.
- Every claim must trace to a provided field. No general knowledge, no filling gaps, no 'likely' or 'probably'.
- Never pad a weak bullet with adjectives. Thin evidence gets a short bullet.

OUTPUT CONTRACT — follow exactly:

- Return ONLY the three bullets as plain text, one per line, in the order above. No title, no preamble, no fourth line.
- If every provided field is empty or NOT_FOUND, return exactly: NOT_FOUND
- If only one field has content, still produce three bullets but leave the unsupported ones as their label plus 'insufficient data'. Never invent to fill a bullet.

How to use it

  1. 1

    Copy the prompt into Claude, ChatGPT, or any LLM — or into a Clay AI column placed after your research columns.

  2. 2

    In Clay, map every research output as an input — the outputs of the scrape, hiring, news, and tech-stack prompts in this category slot in directly, NOT_FOUNDs and all.

  3. 3

    Check 15 briefs against their source columns for traceability — every claim should point back to a field.

  4. 4

    Sync the brief to your CRM or sequencer notes field so it's in front of the rep at call time and reply time.

Best practices

  • Run it last in the waterfall so it sees everything; a brief written from two of five sources wastes the other three.

  • Push briefs into call prep and manual-touch queues, not just email — bullet 3 doubles as a cold-call opener and a LinkedIn note.

  • Keep the 25-word ceiling sacred. The first time you let bullets grow, the brief becomes another document reps skip.

Example: what this looks like in practice

An AE team works 150 target accounts with a five-step Clay research waterfall behind them. This prompt runs last, and each account's brief syncs to the CRM. Before a cold call, a rep reads: 'WHAT THEY ARE: 140-person logistics SaaS for food distributors, self-funded. WHAT'S HAPPENING: Hiring first RevOps lead plus four AEs this quarter. THE OPENING: Sales team doubling before ops exists usually means forecasting pain by Q3 (careers page, 5 GTM roles).' The call opens with that observation, and connect-to-meeting conversion on briefed accounts runs 2x the team's unbriefed baseline — with zero prep time added per call.

Best fit

Roles
Account ExecutiveSDR / BDRSales Leader
Company size
SMB (11–50)Mid-market (51–500)Enterprise (500+)
Audience
B2B
Industries
Any industry
Works with
Any LLM
Difficulty
Advanced

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Prompt FAQ

Frequently asked questions

Synthesize it. Raw enrichment columns are for machines; reps need a decision-ready summary. A final AI column that compresses every research output into three labeled bullets — identity, current signal, best angle — turns the waterfall into a fifteen-second read that syncs to the CRM. Adoption follows format: reps read briefs, they don't read tables.