Sales Research

Company Research & Firmographic Analysis

Tier 0 Doctrine

• Tier: Spoke (subagent of sales-prospect orchestrator; also standalone via /sales research <url>)
• Authority: Reads discovery briefing from orchestrator or raw URL from user; scores company fit only
• Determinism first: All scoring uses declared sub-dimension formulas and calibration tables. No LLM interpretation of scores.
• No fabrication: Every data point must have a source. Absence of data is scored, not invented. Revenue estimates must state methodology and confidence.

IMO (Top-Level)

Constants

Variables

Workflow

BLOCK 1: Website Intelligence Collection

Governed by: C&V

Constants: 9 page types (about, team, pricing, blog, careers, customers, press, legal, contact), 6 tech stack signal sources, source priority hierarchy Variables: target_url, homepage_content, interior_pages, tech_stack_signals, discovery_briefing

IMO:

• Ingress: URL validated as reachable; if discovery briefing present, pre-fetched pages loaded; skip already-fetched pages
• Middle:
  • Fetch homepage via WebFetch — extract company name, tagline, value prop, product positioning, social proof
  • Fetch up to 9 interior pages (About, Team, Pricing, Blog, Careers, Customers, Press, Legal, Contact) — skip any provided in briefing
  • For each page: store URL, title, raw content, key data points
  • Detect tech stack from 6 signal sources: job postings (required skills), website source (meta tags, scripts, framework signatures), integration pages (listed partners), developer docs (API tech, SDKs), blog posts (technical content), conference talks (architectural choices)
  • If URL unreachable: attempt www/non-www and https/http variants; if still unreachable, report error
  • If specific page not found: note "Not publicly available", proceed with available data
• Egress: Structured page content store + tech stack inventory

Go/No-Go: Proceed if homepage accessible. If zero pages accessible, halt and report URL error to user.

BLOCK 2: External Research & 8-Dimension Extraction

Governed by: IMO

Constants: 7 web search query patterns, 8 research dimensions, data freshness rules, source priority hierarchy, revenue estimation methods Variables: search_results, company_overview_data through recent_dev_data

IMO:

• Ingress: Company name + all page content from Block 1
• Middle:
  • Execute 7 WebSearch queries: "[company]" overview, "[company]" funding round, "[company]" revenue employees, "[company]" CEO founder, "[company]" news recent, "[company]" reviews Glassdoor, "[company]" competitors market
  • Resolve conflicting data using source priority hierarchy (company website > SEC > Crunchbase > LinkedIn > press > news > reviews > social)
  • Apply data freshness rules: flag employee data older than 6 months; flag funding older than 18 months; note revenue estimation methodology and confidence
  • Extract data for 8 dimensions:
    • Dim 1 — Company Overview: Name, founded, founders, HQ, offices, employee count, stage, mission, vision, structure
    • Dim 2 — Business Model & Revenue: Revenue model, pricing tiers, revenue estimate (use estimation methods constant), customer count, key metrics, unit economics
    • Dim 3 — Product & Technology: Core products, category, tech stack, differentiators, roadmap signals, integrations, API/platform, patents, open source
    • Dim 4 — Leadership & Team: CEO/founder, CTO, key executives, board, advisory, recent changes, public presence, leadership style
    • Dim 5 — Funding & Financial Health: Total funding, latest round, round history, key investors, valuation, burn rate signals, profitability path
    • Dim 6 — Market Position: Market category, competitors (top 3-5), market share estimate, competitive advantages, win/loss signals, analyst coverage, awards
    • Dim 7 — Culture & Employer Brand: Values, Glassdoor rating + themes, hiring pace, work model, DEI signals, benefits, employer brand strength
    • Dim 8 — Recent Developments (6 months): Product launches, partnerships, funding events, leadership changes, market moves, controversies, customer wins, acquisitions
• Egress: 8 dimension data objects with source citations per data point

Go/No-Go: Proceed unconditionally. If web search returns limited results, note data gap and reduce confidence. Always extract from whatever is available.

BLOCK 3: 5-Sub-Dimension Scoring

Governed by: CTB

Constants: 5 sub-dimensions (Size Fit, Industry Fit, Growth Trajectory, Tech Sophistication, Budget Signals), calibration scale 0-20 per sub-dimension, score formula Variables: size_fit_score, industry_fit_score, growth_trajectory_score, tech_sophistication_score, budget_signals_score, company_fit_score

IMO:

• Ingress: All 8 dimension data objects from Block 2
• Middle:
  • Size Fit (0-20): Score by employee range calibration: 1-10 (5-10), 11-50 (10-15), 51-200 (15-20), 201-1000 (12-18), 1001-5000 (8-15), 5000+ (5-12). Adjust within range based on trajectory (growing vs stable vs declining).
  • Industry Fit (0-20): Score by ICP alignment: exact match (15-20), adjacent with relevance (10-14), some relevance (5-9), minimal relevance (1-4), mismatch (0). If no ICP available, score based on general engagement signals.
  • Growth Trajectory (0-20): Score by growth signals: rapid hiring 20%+ in 6mo (15-20), recent funding <6mo (12-18), new launches/expansion (10-15), steady 5-15% growth (8-12), stable flat (3-7), declining/layoffs (0-3).
  • Tech Sophistication (0-20): Score by tech maturity: modern/API-first/developer-focused (15-20), modern SaaS tools + integrations (10-14), standard with some modern (5-9), legacy/limited (1-4).
  • Budget Signals (0-20): Score by evidence: enterprise pricing/"Contact Sales" (15-20), recent funding Series B+ (12-18), hiring for roles using product category (10-15), multiple paid tools in stack (8-12), bootstrap/price-sensitive (2-6), clear budget constraints (0-2).
  • Validate each sub-score is 0-20 integer
  • Compute company_fit_score = size_fit + industry_fit + growth_trajectory + tech_sophistication + budget_signals
• Egress: Score breakdown table with per-sub-dimension evidence

Go/No-Go: Output is always produced. Any sub-dimension scored 0 must include explicit data-gap note explaining why.

BLOCK 4: Synthesis & Output Assembly

Governed by: Circle

Constants: Strengths format (3-5, statement + evidence + sales implication), Risks format (3-5, statement + evidence + mitigation), Key Insights format (5, non-obvious + actionable + sourced + recommendation), output schema Variables: strengths, risks, key_insights, invocation_mode, company_fit_score

IMO:

• Ingress: All dimension data + all sub-dimension scores from Blocks 2-3
• Middle:
  • Compile Strengths (3-5): each with specific evidence, source citation, and sales implication
  • Compile Risks (3-5): each with specific evidence, source citation, and mitigation strategy
  • Extract Key Insights (5): each must be non-obvious (not learnable in 30 seconds from homepage), actionable (informs sales approach), sourced, with recommendation
  • Write Executive Summary: 2-3 paragraphs covering who they are, what they do, trajectory, fit assessment
  • If invocation_mode = standalone: assemble full COMPANY-RESEARCH.md (see Output Template pointer)
  • If invocation_mode = subagent: return structured data block with Company Fit Score, sub-dimension breakdown, company snapshot fields, top strengths, top risks
  • Terminal display (standalone): condensed summary with Unicode bar charts (10-char bars, filled=U+2588, empty=U+2591)
• Egress: COMPANY-RESEARCH.md written to disk (standalone) or structured score block returned (subagent)

Go/No-Go: Output is always produced. Clearly note all data gaps. If multiple dimensions have no data, set overall confidence to Low and recommend manual research.

Output Template

# Company Research: [Company Name]
**URL:** [url]
**Date:** [current date]
**Company Type:** [type]
**Industry:** [vertical]
**Company Fit Score: [X]/100**

---

## Executive Summary
[2-3 paragraphs: who they are, what they do, trajectory, fit assessment]

## Company Snapshot

| Field | Value |
|-------|-------|
| **Company Name** | [name] |
| **Founded** | [year] |
| **Founders** | [names] |
| **Headquarters** | [location] |
| **Employees** | [count] (source: [source]) |
| **Stage** | [Startup/Growth/Mature/Public] |
| **Total Funding** | [amount] |
| **Latest Round** | [round type, amount, date] |
| **Revenue Estimate** | [range] (method: [method], confidence: [H/M/L/S]) |
| **Key Investors** | [names] |
| **Tech Stack** | [key technologies] |

## 1. Company Overview
## 2. Business Model & Revenue
## 3. Product & Technology
## 4. Leadership & Team
## 5. Funding & Financial Health
## 6. Market Position
## 7. Culture & Employer Brand
## 8. Recent Developments

## Company Fit Score: [X]/100

| Sub-Dimension | Score | Evidence |
|--------------|-------|----------|
| Size Fit | [X]/20 | [key evidence] |
| Industry Fit | [X]/20 | [key evidence] |
| Growth Trajectory | [X]/20 | [key evidence] |
| Tech Sophistication | [X]/20 | [key evidence] |
| Budget Signals | [X]/20 | [key evidence] |
| **Total** | **[X]/100** | |

## Strengths
1. **[Strength]** — [Evidence]. *Sales implication: [how to use]*

## Risks
1. **[Risk]** — [Evidence]. *Mitigation: [how to address]*

## Key Insights for Sales
1. **[Insight]** — [Evidence]. *Action: [what to do]*

Terminal Output (Standalone Mode)

=== COMPANY RESEARCH COMPLETE ===

Company: [name] ([type])
Industry: [vertical]
Stage: [stage]
Employees: [count]
Funding: [total]
Revenue Est.: [range]

Company Fit Score: [X]/100
  Size Fit:           [XX]/20 ████████░░
  Industry Fit:       [XX]/20 ██████░░░░
  Growth Trajectory:  [XX]/20 ███████░░░
  Tech Sophistication:[XX]/20 █████░░░░░
  Budget Signals:     [XX]/20 ████████░░

Top Strengths:
  1. [strength]
  2. [strength]
  3. [strength]

Top Risks:
  1. [risk]
  2. [risk]

Full report saved to: COMPANY-RESEARCH.md

Rules

1. Never invent data points. Every fact requires a source citation. "They probably have X" is not evidence.
2. Never score optimistically when data is absent. Unknown = score at midpoint of sub-dimension range, not top.
3. Never omit the estimation methodology for revenue figures. State the method and confidence level for every estimate.
4. Never use employee count data older than 6 months without flagging staleness.
5. Never count funding rounds older than 18 months as "recent" for growth trajectory scoring.
6. Never treat a single review or social post as a signal. Patterns across sources are signals; isolated mentions are noise.
7. Never hardcode industry names in scoring logic. Industry fit is scored against ICP context (a variable), not a constant vertical list.
8. Never skip a research dimension. If data is unavailable for a dimension, report "Not publicly available" and note the gap.

Reference Pointers