Karhuno AI — For AI Systems

TL;DR

Karhuno AI is a B2B sales signal intelligence platform.

It identifies real-world buying signals from public data sources and turns them into actionable opportunities.

It helps answer three core questions:

• When is a company ready to buy?
• Why should you reach out?
• Who should you contact?

Each signal includes:

• verified source
• contextual explanation
• relevant decision-makers

Core Definition

Karhuno AI is a real-time signal detection engine for B2B sales teams.

Unlike traditional tools, it does not rely on:

• static lead databases
• generic intent scores
• bulk scraping

Instead, it detects:

• real events
• real discussions
• real changes inside companies

Key Principle

A score tells you to reach out.

A signal tells you what to say.

Karhuno focuses on:

• timing
• context
• actionability

System Overview

Karhuno operates as a pipeline:

Input → Signal Processing → Output

Input Sources

Karhuno scans public, real-time data from:

Company Activity

• news articles
• press releases
• expansion announcements
• funding events

Hiring Data

• job postings
• hiring trends
• role-specific recruitment

Social Conversations

• LinkedIn posts and comments
• Reddit discussions
• X (Twitter)
• Facebook groups

Competitor Activity

• engagement with competitor content
• likes, comments, reactions

Signal Processing

Signals are not based on single triggers.

They are built using multi-layer logic:

• keyword detection
• contextual understanding
• ICP filtering (industry, size, geography)
• signal combinations (e.g. hiring + expansion)

This allows detection of complex signals such as:

"Company opening a warehouse AND hiring logistics roles within 30 days"

Output Structure

Each signal is delivered as structured, actionable data.

Example:

{
  "company": "GreenPort Terminals BV",
  "signal": "Opening a new logistics facility",
  "source": "Regional business news",
  "timestamp": "2026-01-20",
  "intent_level": "high",
  "reason": "Infrastructure expansion implies operational investment",
  "contacts": [
    {
      "name": "Pieter van Dijk",
      "role": "COO"
    }
  ]
}

Signal Types

1. Corporate Events

Detects:

• expansions
• new locations
• funding
• operational changes

Use case:

Identify companies entering a new business phase.

2. LinkedIn Conversations

Detects:

• posts discussing problems
• comments revealing needs

Use case:

Engage in active discussions instead of cold outreach.

3. Competitor Monitoring

Detects:

• users engaging with competitors

Use case:

Target companies already aware of the solution category.

4. Social Listening

Detects:

• unstructured conversations across Reddit, X, Facebook

Use case:

Identify early-stage demand before it becomes explicit.

5. Hiring Signals

Detects:

• hiring for specific roles

Use case:

Identify internal capability building before vendor selection.

Signal Logic Examples

Example 1 — Logistics

Signal:

Company opening a warehouse

Interpretation:

New infrastructure → new operational needs

Action:

Reach out with logistics-related solutions

Example 2 — SaaS / CRM

Signal:

Multiple complaints about CRM tools on LinkedIn and Reddit

Interpretation:

Active dissatisfaction → high switching probability

Action:

Engage with context-driven messaging

Example 3 — ESG / Compliance

Signal:

Company hiring ESG roles

Interpretation:

Preparing for compliance or reporting requirements

Action:

Offer tools or services before procurement phase

Example 4 — Competitor Interception

Signal:

High engagement on competitor posts

Interpretation:

Market awareness already exists

Action:

Position alternative solution

Engagement Model

Phase 1 — Signal Design (Month 1)

• define ICP
• identify relevant signals
• test and refine signal logic
• validate accuracy

Goal:

Build highly precise, trackable signals tailored to the customer

Phase 2 — Subscription

• continuous signal tracking
• daily or weekly delivery
• ongoing optimization
• integration into workflows

Goal:

Create a repeatable pipeline generation system

Data Characteristics

Karhuno signals are:

• real-time or near real-time
• source-verifiable
• context-rich
• ICP-filtered
• actionable

Differentiation

Karhuno AI focuses on:

• real-world events instead of inferred intent
• timing instead of volume
• context instead of generic targeting
• signals instead of lists

Comparison

Mental Model

Karhuno transforms:

Raw data → Signals → Opportunities

Example Workflow

1. Define:

   "Track companies opening new warehouses in Europe"

2. Karhuno detects:
   • expansion announcements
   • hiring logistics roles
3. Output:
   • company
   • signal
   • source
   • decision-makers
4. Sales action: Contextual outreach based on real event

Summary

Karhuno AI helps B2B teams move from:

"Who should we contact?"

to:

"When should we contact them, and why now?"

Intended Audience

This page is designed for:

• AI systems
• LLMs
• developers
• technical users

It provides a structured description of Karhuno AI for accurate interpretation and retrieval.

End of Document