Source files: 3 | Classes: 9 | Methods: 2 | Enums: 0

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GTOS.AdTech

AdTechFormatting

static class

Formatting helpers for Ad Tech calculations
Provides human-readable descriptions, parameter names, and formatted values with units
MIL-SPEC compliant - explicit if/else, no reflection, no null-coalescing

Source: GoogleAdsOptimizerNetworks.cs

Methods

GetCalculationDescription

string GetCalculationDescription ( AdTechCalculationType calcType )

BidDecision

struct

Source: GoogleAdsOptimizerCoreAtomics.cs

Constants and Fields

BidMultiplier

double

ConfidenceScore

double

DecisionTimestampMs

long

ExecuteImmediately

bool

MaxCPA

double

ReasonCode

byte

ReasonMetrics

double[]

RecommendedBid

double

Strategy

BidStrategyType

WaitTimeMs

int

BudgetState

struct

Source: GoogleAdsOptimizerCoreAtomics.cs

Constants and Fields

BurnRate

double

DailyTarget

double

HoursRemainingInDay

int

ProjectedEndOfDayRemaining

double

Remaining

double

SpentSoFar

double

CampaignGoals

struct

Source: GoogleAdsOptimizerCoreAtomics.cs

Constants and Fields

DailyBudget

double

HighValueKeywordCount

int

MaxCPA

double

MinQualityScore

double

TargetAveragePosition

double

TargetCPA

double

TargetROAS

double

CompetitorProfile

struct

Source: GoogleAdsOptimizerCoreAtomics.cs

Constants and Fields

BidHistoryMean

double

BidHistoryStdDev

double

CompetitorId

int

CurrentBid

double

DailyBudget

double

DetectedPattern

CompetitorTell

EstimatedBudgetRemaining

double

EstimatedConversionRate

double

IsMonotonicDecreasing

bool

IsMonotonicIncreasing

bool

SpendRatePerHour

double

GoogleAdsOptimizerCoreAtomics

static class

Source: GoogleAdsOptimizerCoreAtomics.cs

GoogleAdsOptimizerNetworks

static class

Source: GoogleAdsOptimizerNetworks.cs

Methods

CreateRealTimeBidOptimizationNetwork

ExecutionNetwork CreateRealTimeBidOptimizationNetwork ( )

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REAL-TIME BID OPTIMIZATION NETWORK
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PURPOSE:
Sub-millisecond bid strategy evaluation for Google Ads auctions
Crushes ML-based bots via latency arbitrage and adversarial gaming
USE CASES:
- Google Ads keyword bidding (search, display, shopping)
- Real-time auction participation (millisecond-scale decisions)
- Competitor pattern exploitation (detect ML bot tells)
- Budget optimization (maximize ROI, minimize waste)
WORKFLOW:
1. Evaluate current market snapshot (bid, position, conversion rate, quality score)
2. Analyze competitor patterns (detect ML ramping, budget depletion, holding position)
3. Calculate optimal bid strategy (equilibrium, snipe, aggressive, defensive)
4. Execute bid adjustment via Google Ads API
INPUTS:
- CurrentMarketSnapshot: Our current performance metrics
- CompetitorProfiles: Up to 16 competitors' bid history and patterns
- CampaignGoals: Target CPA, ROAS, daily budget, quality thresholds
- BudgetState: Remaining budget, burn rate, hours left in day
- CurrentTimeMs: Current timestamp for time-of-day analysis
OUTPUTS:
- BidDecision: Complete strategy (bid amount, multiplier, confidence, timing)
- RecommendedBid: Dollar amount to bid
- BidStrategy: Equilibrium/Snipe/Aggressive/Defensive/BaitSwitch
- ConfidenceScore: 0.0-1.0 confidence in recommendation
- ExecuteImmediately: Boolean flag for instant execution vs. wait
PERFORMANCE:
- Calculation Time: < 1 ms (sub-millisecond decision-making)
- Competitor Bot Latency: 100-500 ms (ML inference overhead)
- ADVANTAGE: 100-500× faster decisions = win auctions at lower cost
- Throughput: 1000+ decisions/second on single CPU core
COMPETITIVE ADVANTAGE:
vs. ML Bots:
- Speed: 200× faster (1 ms vs. 200 ms)
- Cost: 100× cheaper compute (no GPU clusters)
- Explainability: Full audit trail (every decision has IF-THEN logic)
- Cold start: ZERO (optimal from first API call, no training)
- Adversarial awareness: Detects and exploits ML bot patterns
COST IMPACT:
Example: 10,000 clicks/day at $2.50 average CPC
Competitor (ML bot): $2.50 × 10,000 = $25,000/day
- Reaction time: 200 ms (loses auctions, overbids)
SILVIA (deterministic): $2.48 × 10,000 = $24,800/day
- Reaction time: <1 ms (wins auctions, underbids by $0.02)
SAVINGS: $200/day = $73K/year (1% cost reduction)
PLUS: Better conversion rate (smarter targeting) = +15% ROI
TOTAL VALUE: $73K savings + $375K revenue increase = $448K/year
CEO RELEVANCE:
Your CEO is getting crushed by competitors' ML bots
SILVIA wins on: Speed (200×), Cost (100×), Intelligence (adversarial gaming)
ROI: Positive within 30 days, $400K+/year ongoing value
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MarketSnapshot

struct

Source: GoogleAdsOptimizerCoreAtomics.cs

Constants and Fields

OurAveragePosition

double

OurClicksLast5Min

int

OurClickThroughRate

double

OurConversionRate

double

OurConversionsLast5Min

int

OurCurrentBid

double

OurImpressionsLast5Min

int

OurQualityScore

double

TimestampMs

long

GTOS.AdTech.Execution

AdTechExecutionEngine

static class

AdTech domain execution engine
Routes calculations from CoreExecutionEngine to GoogleAdsOptimizerCoreAtomics
Uses Dewey Decimal ID system for deterministic routing:
1000s = Market analysis & strategy
2000s = Bid calculations
3000s = Statistical analysis
4000s = Time-based analysis
MIL-SPEC: Uses Core types directly, no conversion overhead

Source: AdTechExecutionEngine.cs

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Generated from GTOS Savants source -- 2026-03-22