COMMAND CENTER | ALL SYSTEMS NOMINAL

7 AI Engines. One Mission: Your ROAS.

Samson-AI deploys a symphony of specialized optimization engines that work 24/7 to maximize your ad performance. Each engine handles a specific aspect of campaign optimization using cutting-edge algorithms.

Deploy Engines Now

ENGINE_01 ACTIVE

UPTIME: 99.97%

Economic Engine

Budget Scaling & Optimization

The Economic Engine uses polynomial regression to model the relationship between ad spend and revenue. It fits a quadratic curve to your historical data to find the optimal budget level where profit is maximized.

ALGORITHM	Polynomial Regression (Degree 2)
MIN R-SQUARED	0.60 for confident recommendations
UPDATE FREQUENCY	Every 6 hours
DATA REQUIREMENT	14+ days of spend data

Budget Optimization Saturation Detection Profit Maximization

economic_engine.algorithm

// Polynomial Regression Model

Revenue = B0 + B1*Spend + B2*Spend^2

// Normal Equations

B = (X'X)^-1 X'y

// Optimal Budget Calculation

optimal_spend = -B1 / (2 * B2)

if B2 < 0:

return optimal_spend // Concave curve found

else:

return "No saturation detected"

ENGINE_02 GUARDIAN

KILLS TODAY: 12

Sentinel Engine

Kill Switch & Budget Protection

The Sentinel Engine uses Sequential Probability Ratio Testing (SPRT) combined with a proprietary "Bleed Score" to identify and stop underperforming ads before they waste your budget. Protects high-value "Assist King" ads from premature termination.

ALGORITHM	SPRT + Bleed Score Hybrid
P0 THRESHOLD	Account 30-day CVR (expected)
P1 THRESHOLD	P0 * 0.5 (unacceptable)
BLEED LIMIT	3x Target CPA before kill

SPRT Testing Dynamic Thresholds Assist King Protection

sentinel_engine.algorithm

// Dynamic SPRT Thresholds

p0 = account_30day_cvr

p1 = p0 * 0.5

// Bleed Score Calculation

bleed_score = budget_consumed / allowed_burn

// Kill Decision

if bleed_score > 1.0 AND !is_assist_king:

if sprt_ratio < lower_bound:

return "KILL"

else:

return "CONTINUE"

ENGINE_03 ACTIVE

PID OUTPUT: 1.23

Control Engine

PID Bid Pacing & Stabilization

Applies classical control theory (PID controller) to bid management. Prevents oscillation and overshooting by using proportional, integral, and derivative terms with anti-windup protection to maintain smooth, optimal bid adjustments.

ALGORITHM	PID Controller with Anti-Windup
Kp (PROPORTIONAL)	0.15 (tuned per account)
Ki (INTEGRAL)	0.02
Kd (DERIVATIVE)	0.05

PID Controller Anti-Windup Smooth Pacing

control_engine.algorithm

// PID Controller Formula

Output = Kp*e + Ki*integral(e) + Kd*de/dt

// Error Calculation

e = target_cpa - current_cpa

// Anti-Windup Clamping

if output > max_bid_change:

integral_term = 0 // Reset windup

output = max_bid_change

ENGINE_04 ACTIVE

CREATIVES QUEUED: 8

Generative Engine

Creative Fatigue Detection & Swap

Monitors creative performance using Vision API for visual analysis. Detects fatigue patterns before performance degrades and automatically queues replacement creatives. Uses VAM (Visual Attention Metrics) scoring to predict fatigue.

ALGORITHM	CTR Decay + Vision API Analysis
FATIGUE THRESHOLD	-15% CTR decline over 3 days
REPLACEMENT BUFFER	48 hours before swap
VAM SCORING	0-100 visual attention score

Vision API VAM Scoring Auto-Swap

generative_engine.algorithm

// Fatigue Detection

fatigue_score = CTR_delta / exposure_days

// VAM Score Calculation

vam = vision_api.analyze(creative)

attention_score = vam.saliency * vam.contrast

// Swap Decision

if fatigue_score < -0.15:

queue_replacement()

return "SWAP_PENDING"

ENGINE_05 ACTIVE

GOLDILOCKS ZONES: 3

Strategic Engine

Goldilocks Zone Targeting

Finds the "sweet spot" audience segments - not too broad (wasted spend), not too narrow (limited scale). Analyzes segment efficiency across multiple dimensions to identify Goldilocks zones for optimal reach-to-conversion ratios.

ALGORITHM	Multi-Dimensional Efficiency Scoring
ZONE FORMULA	Reach * CVR / Cost
MIN AUDIENCE SIZE	100K (for statistical significance)
MAX AUDIENCE SIZE	5M (to avoid dilution)

Goldilocks Zones Segment Analysis Efficiency Scoring

strategic_engine.algorithm

// Goldilocks Zone Calculation

zone_score = (reach * cvr) / cpm

// Zone Classification

if audience_size < 100K:

return "TOO_NARROW"

if audience_size > 5M:

return "TOO_BROAD"

if zone_score > threshold:

return "GOLDILOCKS"

ENGINE_06 NEW

ASSIST KINGS: 4

Shapley Attribution Engine

Game Theory Multi-Touch Attribution

Uses game-theoretic Shapley values to fairly distribute conversion credit across all touchpoints. Identifies ads that assist conversions even without last-click credit. Classifies ads as Assist Kings, Closers, or Parasites.

ASSIST KING - High removal effect, low direct ROAS

CLOSER - High position, high direct ROAS

PARASITE - Low removal effect, claims credit

Shapley Values Removal Effect Assist Detection

shapley_engine.algorithm

// Shapley Value Calculation

for each coalition S not containing ad:

phi += [|S|!(n-|S|-1)!/n!] * [v(S+{ad}) - v(S)]

// Removal Effect

removal_effect = 1 - (value_without / value_with)

// Classification

if removal_effect > 0.3 AND direct_roas < 1:

return "ASSIST_KING"

if direct_roas > 2 AND avg_position < 3:

return "CLOSER"

ENGINE_07 NEW

EMBEDDINGS: 1,247

Semantic Similarity Engine

Pre-Launch Creative Validation

Converts ad copy into 1536-dimensional vectors using OpenAI embeddings. Compares new ads against your historical winners and losers to predict performance before launch. Prevents launching ads similar to known failures.

ALGORITHM	Cosine Similarity on Embeddings
EMBEDDING MODEL	text-embedding-3-small (1536d)
REJECT THRESHOLD	> 0.85 similarity to loser
WARNING THRESHOLD	> 0.70 similarity to loser

OpenAI Embeddings Cosine Similarity Pre-Launch Validation

semantic_engine.algorithm

// Cosine Similarity Formula

Similarity(A, B) = (A . B) / (||A|| x ||B||)

// Pre-Launch Validation

new_embed = openai.embed(new_ad_text)

for loser in loser_embeddings:

sim = cosine_sim(new_embed, loser)

if sim > 0.85:

return "REJECT"

if sim > 0.70:

return "WARNING"

for winner in winner_embeddings:

if cosine_sim(new_embed, winner) > 0.70:

return "PROMISING"