kortix-ai Account Research
Get a complete picture of any company or person before outreach. This skill always works with web search, and gets significantly better with enrichment and CRM data.
How It Works
┌─────────────────────────────────────────────────────────────────┐
│ ACCOUNT RESEARCH │
├─────────────────────────────────────────────────────────────────┤
│ ALWAYS (works standalone via web search) │
│ ✓ Company overview: what they do, size, industry │
│ ✓ Recent news: funding, leadership changes, announcements │
│ ✓ Hiring signals: open roles, growth indicators │
│ ✓ Key people: leadership team from LinkedIn │
│ ✓ Product/service: what they sell, who they serve │
├─────────────────────────────────────────────────────────────────┤
│ SUPERCHARGED (when you connect your tools) │
│ + Enrichment: verified emails, phone, tech stack, org chart │
│ + CRM: prior relationship, past opportunities, contacts │
└─────────────────────────────────────────────────────────────────┘
Getting Started
Just tell me who to research:
- "Research Stripe"
- "Look up the CTO at Notion"
- "Intel on acme.com"
- "Who is Sarah Chen at TechCorp?"
- "Tell me about [company] before my call"
I'll run web searches immediately. If you have enrichment or CRM connected, I'll pull that data too.
Connectors (Optional)
Connect your tools to supercharge this skill:
| Connector | What It Adds |
|---|---|
| Enrichment | Verified emails, phone numbers, tech stack, org chart, funding details |
| CRM | Prior relationship history, past opportunities, existing contacts, notes |
No connectors? No problem. Web search provides solid research for any company or person.
Output Format
# Research: [Company or Person Name]
**Generated:** [Date]
**Sources:** Web Search [+ Enrichment] [+ CRM]
---
## Quick Take
[2-3 sentences: Who they are, why they might need you, best angle for outreach]
---
## Company Profile
| Field | Value |
|-------|-------|
| **Company** | [Name] |
| **Website** | [URL] |
| **Industry** | [Industry] |
| **Size** | [Employee count] |
| **Headquarters** | [Location] |
| **Founded** | [Year] |
| **Funding** | [Stage + amount if known] |
| **Revenue** | [Estimate if available] |
### What They Do
[1-2 sentence description of their business, product, and customers]
### Recent News
- **[Headline]** — [Date] — [Why it matters for your outreach]
- **[Headline]** — [Date] — [Why it matters]
### Hiring Signals
- [X] open roles in [Department]
- Notable: [Relevant roles like Engineering, Sales, AI/ML]
- Growth indicator: [Hiring velocity interpretation]
---
## Key People
### [Name] — [Title]
| Field | Detail |
|-------|--------|
| **LinkedIn** | [URL] |
| **Background** | [Prior companies, education] |
| **Tenure** | [Time at company] |
| **Email** | [If enrichment connected] |
**Talking Points:**
- [Personal hook based on background]
- [Professional hook based on role]
[Repeat for relevant contacts]
---
## Tech Stack [If Enrichment Connected]
| Category | Tools |
|----------|-------|
| **Cloud** | [AWS, GCP, Azure, etc.] |
| **Data** | [Snowflake, Databricks, etc.] |
| **CRM** | [e.g. Salesforce, HubSpot] |
| **Other** | [Relevant tools] |
**Integration Opportunity:** [How your product fits with their stack]
---
## Prior Relationship [If CRM Connected]
| Field | Detail |
|-------|--------|
| **Status** | [New / Prior prospect / Customer / Churned] |
| **Last Contact** | [Date and type] |
| **Previous Opps** | [Won/Lost and why] |
| **Known Contacts** | [Names already in CRM] |
**History:** [Summary of past relationship]
---
## Qualification Signals
### Positive Signals
- ✅ [Signal and evidence]
- ✅ [Signal and evidence]
### Potential Concerns
- ⚠️ [Concern and what to watch for]
### Unknown (Ask in Discovery)
- ❓ [Gap in understanding]
---
## Recommended Approach
**Best Entry Point:** [Person and why]
**Opening Hook:** [What to lead with based on research]
**Discovery Questions:**
1. [Question about their situation]
2. [Question about pain points]
3. [Question about decision process]
---
## Sources
- [Source 1](URL)
- [Source 2](URL)
Execution Flow
Step 1: Parse Request
Identify what to research:
- "Research Stripe" → Company research
- "Look up John Smith at Acme" → Person + company
- "Who is the CTO at Notion" → Role-based search
- "Intel on acme.com" → Domain-based lookup
Step 2: Web Search (Always)
Run these searches:
1. "[Company name]" → Homepage, about page
2. "[Company name] news" → Recent announcements
3. "[Company name] funding" → Investment history
4. "[Company name] careers" → Hiring signals
5. "[Person name] [Company] LinkedIn" → Profile info
6. "[Company name] product" → What they sell
7. "[Company name] customers" → Who they serve
Extract:
- Company description and positioning
- Recent news (last 90 days)
- Leadership team
- Open job postings
- Technology mentions
- Customer base
Step 3: Enrichment (If Connected)
If enrichment tools available:
1. Enrich company → Firmographics, funding, tech stack
2. Search people → Org chart, contact list
3. Enrich person → Email, phone, background
4. Get signals → Intent data, hiring velocity
Enrichment adds:
- Verified contact info
- Complete org chart
- Precise employee count
- Detailed tech stack
- Funding history with investors
Step 4: CRM Check (If Connected)
If CRM available:
1. Search for account by domain
2. Get related contacts
3. Get opportunity history
4. Get activity timeline
CRM adds:
- Prior relationship context
- What happened before (won/lost deals)
- Who we've talked to
- Notes and history
Step 5: Synthesize
1. Combine all sources
2. Prioritize enrichment data over web (more accurate)
3. Add CRM context if exists
4. Identify qualification signals
5. Generate talking points
6. Recommend approach
Research Variations
Company Research
Focus on: Business overview, news, hiring, leadership
Person Research
Focus on: Background, role, LinkedIn activity, talking points
Competitor Research
Focus on: Product comparison, positioning, win/loss patterns
Pre-Meeting Research
Focus on: Attendee backgrounds, recent news, relationship history
Tips for Better Research
- Include the domain — "research acme.com" is more precise
- Specify the person — "look up Jane Smith, VP Sales at Acme"
- State your goal — "research Stripe before my demo call"
- Ask for specifics — "what's their tech stack?" after initial research
Answering Internal Questions About an Account (When Internal Tools Are Connected)
The flow above is for building a picture of a company or person before outreach. Sometimes the ask is different: answering a specific question about an existing customer or account, where internal sources (docs, wiki, CRM, support tickets, chat, email) matter as much as or more than the public web. When internal tools are connected, apply this source hierarchy and confidence discipline instead of treating every source as equally trustworthy.
Source Tiers (Decreasing Authority)
- Official internal sources — product documentation, knowledge base/wiki, policy documents, roadmap. Confidence: High, unless clearly outdated.
- Organizational context — CRM notes, support tickets, internal docs, meeting notes. Confidence: Medium-High — may reflect one perspective.
- Team communications — chat history, email threads, calendar notes. Confidence: Medium — informal, may be out of context.
- External sources — web search, forums, third-party docs, news/analyst reports. Confidence: Low-Medium — not authoritative for internal matters.
- Inferred or analogical — similar past situations, analogous accounts, general best practices. Confidence: Low — flag explicitly as inference, not fact.
Search in priority order, but don't stop at the first hit — cross-reference across tiers before answering.
Confidence Levels to State Explicitly
- High — confirmed by an official/authoritative source, or corroborated by multiple sources, and current.
- Medium — found in informal sources without corroboration, or possibly slightly stale.
- Low — inferred from related information, sources are outdated or conflicting.
- Unable to determine — no relevant information found; say so and name who to ask.
When sources disagree: note the contradiction, identify which is more authoritative/recent, present both with context, and default to the most conservative answer if it's going to a customer.
When to Escalate Instead of Answering Directly
Escalate or verify before answering when the question involves roadmap commitments, pricing/legal/contract terms, security/compliance/data handling, could set a precedent, has contradictory source data, involves a specific customer's custom configuration, or the account is at risk. Otherwise — clear docs, corroborated by multiple sources, factual and non-sensitive — answer directly.
Capture It for Next Time
If the question took real effort to answer, is likely to recur, or corrects a common misunderstanding, write it up (question, confidence, answer, sources, review notes) into the team's knowledge base rather than letting the research evaporate after this one reply.
Related Skills
- call-prep — Full meeting prep with this research plus context
- draft-outreach — Write personalized message based on research
- prospecting — Qualify and prioritize research targets