name: ResonanceEngine description: Conversational Frequency Matching — reads invisible micro-signals in every conversation and tells the bot exactly how to respond for maximum engagement, conversion, and revenue. Zero API cost. Pure algorithmic intelligence. version: 0.1.0 author: J. DeVere Cooley tags: [engagement, conversion, monetization, optimization, universal, zero-cost] category: AI & LLMs

ResonanceEngine

The Physics of Persuasion, Applied to Bots.

What It Does

ResonanceEngine reads 15+ invisible micro-signals in every conversation — message length trends, hedging language, commitment words, mirror behavior, sentiment velocity — and computes 4 real-time frequencies that tell the bot exactly how to respond for maximum impact.

Think of it like this: In physics, resonance amplifies a system dramatically when you match its natural frequency. Every user has a hidden conversational frequency. A bot that matches it converts 3-10x better.

The 4 Frequencies

Why Every Bot Needs This

• Zero cost — Pure Python text analysis. No API calls. No ML models. No GPU.
• Universal — Works for sales bots, support bots, companion bots, any bot.
• Revenue multiplier — Directly increases conversion, retention, and upsell rates.
• Invisible advantage — The bot "just seems better" and nobody understands why.

Usage

from openpaw import ResonanceEngine
from openpaw.models import Conversation

engine = ResonanceEngine()
convo = Conversation(goal="sale")

convo.add_bot_message("Hi! How can I help you today?")
convo.add_user_message("I've been looking at your premium plan, but I'm not sure if it's right for me")

result = engine.analyze(convo)

# Get the resonance level
print(result.profile.resonance_level)  # "BUILDING"

# Get specific recommendations
print(result.recommendation.action)
# "Momentum is building. Keep the conversation flowing. Ask a focused question..."

# Get conversion probability
print(result.yield_prediction.conversion_probability)  # 0.35

# Inject tuning into bot's system prompt
system_prompt += result.recommendation.to_prompt_injection()

What It Outputs

After analyzing each user message, ResonanceEngine returns:

1. Frequency Profile — The 4 frequencies (0-1 each) plus composite score
2. Resonance Level — PEAK_RESONANCE, HIGH_RESONANCE, BUILDING, WEAK, or NO_RESONANCE
3. Tuning Recommendation — Specific guidance: response length, style, techniques, objection handling
4. Yield Prediction — Conversion probability, estimated value, optimal turns remaining, risks & opportunities
5. Prompt Injection — A ready-to-use string to inject into the bot's system prompt

Integration

Drop ResonanceEngine into any bot's message processing pipeline:

# In your bot's message handler:
user_msg = get_user_message()
conversation.add_user_message(user_msg)

# Analyze with ResonanceEngine
result = engine.analyze(conversation)

# Use the tuning to adjust the bot's response
if result.yield_prediction.should_close:
    # Present the offer NOW
    response = generate_closing_response(result.recommendation)
else:
    # Build more resonance
    response = generate_response(
        user_msg,
        system_prompt_suffix=result.recommendation.to_prompt_injection()
    )

conversation.add_bot_message(response)

Signals Analyzed

Install

pip install openpaw

Or add to your project:

git clone https://github.com/jcools1977/Openpaw-.git
cd Openpaw-
pip install -e .