Research ~500 target accounts a month with an AI assistant and Google Sheets

You are a B2B research analyst. Use web search. Research each company below for a seller of {{our_offer}} whose ideal customer is: {{icp_description}}.

Companies (name, domain):
{{company_list}}

Scoring rubric (score each 0–3):
{{rubric}}

Rules:
1. Every factual claim must come from a page you actually opened. Put its URL in source_url / sources. If you cannot find a source, write "unknown" and add the gap to `unknowns`. Never guess employee counts, funding or dates.
2. Prefer the company's own site, press releases, job boards and reputable news from the last 12 months. Today is {{today}}; ignore signals older than 18 months.
3. If a name is ambiguous, research the company at the given domain; set match_confidence accordingly. If you can't confirm it's the same company, use "low" or "not_found" and don't score it.
4. Do NOT output personal email addresses, phone numbers or home locations. Do not use LinkedIn content obtained by scraping.
5. reachability: score only 2 (buyer role publicly identifiable) or 1 (unclear); I'll add warm paths myself.
6. fit_total must equal the sum of fit_scores. Tier: A 11–15 with need_signal ≥2; B 7–10; C 3–6; otherwise disqualified with a reason.

Output exactly two code blocks and nothing else:
(a) a JSON array that validates against this schema:
{{json_schema}}
(b) the same rows as TSV with a header row, columns in schema order, signals flattened as "type: claim (date) URL" joined by " | ".
Return one object per input company, in input order, even if not found.

Act as a fact-checker. For each row in the JSON below, open every source_url and check whether the page explicitly supports the claim, for the right company and date.

{{batch_json}}

Return a table with columns: input_name | claim | source_url | verdict (SUPPORTED / NOT FOUND ON PAGE / WRONG COMPANY / WRONG DATE / PAGE UNAVAILABLE) | corrected claim with new URL, or "remove".
Then return the corrected JSON array with unsupported claims removed, fit_scores lowered where a removed claim was the basis (cap uncited criteria at 1), fit_total and tier recomputed. Do not add new claims without a URL.

Write a first line for a cold email from {{sender_name}} at {{our_company}} to the {{buyer_role}} at {{company_name}}.

Use ONLY these cited signals:
{{signals_with_urls}}

Our relevant proof point: {{proof_point}}

Rules: max 30 words; reference exactly one signal factually (no flattery, no 'I noticed', no invented details); no claims about the person. Give 3 options, and under each write the signal and URL it relies on.

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "AccountResearchBatch",
  "type": "array",
  "items": {
    "type": "object",
    "additionalProperties": false,
    "required": ["input_name", "input_domain", "resolved_domain", "match_confidence",
                 "what_they_do", "industry", "hq_country", "employee_range",
                 "signals", "fit_scores", "fit_total", "tier", "personalization_hook",
                 "unknowns", "sources"],
    "properties": {
      "input_name":       { "type": "string", "description": "Exactly as given in the input list" },
      "input_domain":     { "type": "string" },
      "resolved_domain":  { "type": "string", "description": "Domain of the company actually researched. Empty if not found." },
      "match_confidence": { "enum": ["high", "medium", "low", "not_found"] },
      "what_they_do":     { "type": "string", "maxLength": 300, "description": "One or two plain sentences" },
      "industry":         { "type": "string" },
      "hq_country":       { "type": "string", "description": "ISO country name or 'unknown'" },
      "employee_range":   { "enum": ["1-10", "11-50", "51-200", "201-500", "501-1000", "1001-5000", "5000+", "unknown"] },
      "signals": {
        "type": "array", "maxItems": 5,
        "items": {
          "type": "object", "additionalProperties": false,
          "required": ["type", "claim", "date", "source_url"],
          "properties": {
            "type":       { "enum": ["hiring", "funding", "leadership_change", "product_launch", "expansion", "tech_stack", "partnership", "other"] },
            "claim":      { "type": "string", "maxLength": 200 },
            "date":       { "type": "string", "description": "YYYY-MM or YYYY-MM-DD; 'unknown' if not stated" },
            "source_url": { "type": "string", "format": "uri" }
          }
        }
      },
      "fit_scores": {
        "type": "object", "additionalProperties": false,
        "required": ["icp_industry", "size", "need_signal", "timing", "reachability"],
        "properties": {
          "icp_industry": { "type": "integer", "minimum": 0, "maximum": 3 },
          "size":         { "type": "integer", "minimum": 0, "maximum": 3 },
          "need_signal":  { "type": "integer", "minimum": 0, "maximum": 3 },
          "timing":       { "type": "integer", "minimum": 0, "maximum": 3 },
          "reachability": { "type": "integer", "minimum": 0, "maximum": 3 }
        }
      },
      "fit_total":   { "type": "integer", "minimum": 0, "maximum": 15 },
      "tier":        { "enum": ["A", "B", "C", "disqualified"] },
      "disqualify_reason": { "type": "string" },
      "personalization_hook": { "type": "string", "maxLength": 240, "description": "Must reference one of the cited signals" },
      "unknowns":    { "type": "array", "items": { "type": "string" } },
      "sources":     { "type": "array", "minItems": 1, "items": { "type": "string", "format": "uri" } }
    }
  }
}

NOTE: this schema intentionally has NO fields for personal email addresses or phone numbers.
Verified contact data comes from a contact-data provider, not from an AI assistant.

QUALIFICATION RUBRIC — edit the descriptions to match YOUR ideal customer profile (ICP).
Score each criterion 0–3. Total 0–15. The AI proposes scores; a human confirms Tier A.

1. icp_industry
   3 = core target industry ([your core industries])
   2 = adjacent industry we have won before
   1 = plausible but no references
   0 = outside our market → disqualified

2. size  (employees, from the company's own site or a cited source)
   3 = inside our sweet spot (e.g. 51–500)
   2 = just outside (e.g. 20–50 or 501–1,000)
   1 = far outside but has a relevant division
   0 = unknown after research (do NOT guess)

3. need_signal  (evidence they have the problem we solve)
   3 = explicit, dated, cited signal in the last 6 months (job post for the role we replace, public complaint, initiative announced)
   2 = indirect signal (growth, new market, tech stack that implies the need)
   1 = generic fit only
   0 = signal suggests they've already solved it (e.g. announced a competitor)

4. timing
   3 = trigger event in the last 90 days (funding, new leader in the buyer role, expansion)
   2 = trigger event 3–12 months ago
   1 = none found
   0 = known freeze (layoffs, acquisition pending) — note it

5. reachability
   3 = we have a warm path (customer, investor, partner in common) — fill from YOUR records, not the AI
   2 = clear buyer role exists and is publicly identifiable
   1 = buyer role unclear
   0 = no way in found

TIERS
   A = 11–15 and need_signal ≥ 2 → human review, then contact-data lookup, then personal outreach
   B = 7–10 → nurture list / lighter-touch sequence
   C = 3–6 → park; re-check in 6 months
   disqualified = icp_industry 0, or a hard exclusion (existing customer, competitor, sanctioned country, under legal minimum size)

RULE: any score based on a claim without a source URL is capped at 1.

DE-DUPLICATION AND QA — run on every batch before it enters the master sheet

A. De-duplicate (Google Sheets, master tab "Accounts")
   1. Normalise the domain in a helper column (assume raw domain/URL in column B):
      =LOWER(REGEXREPLACE(REGEXREPLACE(TRIM(B2),"^(https?://)?(www\.)?",""),"[/?#].*$",""))
   2. Flag duplicates against everything already researched:
      =IF(COUNTIF($C$2:$C, C2)>1, "DUPLICATE", "")
   3. Before each run, remove input rows whose normalised domain already exists in Accounts
      (=ISNUMBER(MATCH(C2, Accounts!C:C, 0)) on the input tab). This avoids paying for the same research twice.
   4. Also check resolved_domain vs input_domain: if they differ, the AI may have researched a
      different company with a similar name → set status "CHECK MATCH".

B. Structural checks (instant, every row)
   [ ] JSON parsed without edits (if not, re-run the batch; do not hand-fix silently)
   [ ] Row count out = row count in
   [ ] fit_total equals the sum of fit_scores
   [ ] Every signal has a source_url; every row has ≥1 source
   [ ] No email addresses or phone numbers anywhere in the output (search for "@" and digit runs) — delete if present
   [ ] match_confidence "low"/"not_found" rows are moved to a Manual tab, not scored

C. Hallucination spot-check (10% of rows, minimum 3 per batch, ALWAYS include every Tier A)
   For each sampled row, open every source URL and confirm:
   [ ] The page loads and is about this company (not a namesake)
   [ ] The claim is actually stated on the page (not inferred)
   [ ] The date matches
   Record result in a "QA" column: PASS / FAIL + note.
   If >1 of 10 sampled rows FAIL → re-run the whole batch with the verification prompt; don't trust the rest.

D. Compliance gate before any outreach
   [ ] Contact details came from a licensed provider or the person's own published business contact page — not scraped from LinkedIn
   [ ] EU/UK contacts: lawful basis (usually legitimate interests) documented; GDPR Art. 14 notice given at first contact
   [ ] US commercial email: CAN-SPAM basics in the template (accurate From/subject, postal address, working opt-out honoured within 10 business days)
   [ ] Suppression list (unsubscribes, existing customers, competitors) applied