Resources - Performance Metrics
Performance Metrics
Signal™ benchmarked against generalized frontier AI models on the tasks that matter for organizational intelligence.
Benchmark comparison
Signal™ versus Generalized Frontier AI Models
Signal™ is benchmarked against generalized frontier models on metrics that drive organizational intelligence precision.
| Metric | Generalized AI | Signal | Performance advantage |
|---|---|---|---|
Hallucination Rate Fabricated facts, invented attributions, and unsourced claims in model responses | 20-40% Domain-specific attribution tasks | <5% Grounded to verified source data | 4-8x reduction |
Factual Accuracy Correct retrieval and representation of real events, decisions, and commitments | 55-70% Enterprise domain retrieval | >90% Fine-tuned + verified harness | ~1.5x improvement |
Sparse signal from large corpora of structurally similar data Identifying the meaningful signal in high-volume, structurally uniform communications | ~20% precision Similar-language corpora | >90% precision Domain-tuned retrieval model | ~5x improvement |
Corporate Voice Consistency Correct identification of individuals, roles, and communication patterns across the org | 45-65% Without org-specific training | >90% Org-specific fine-tuning | ~1.7x improvement |
Scalability at Volume Performance maintenance as corpus grows to thousands of employees and millions of messages | >30% degradation At enterprise corpus scale | <5% variance Purpose-built indexing | Structural advantage |
Source Attribution Accuracy Correctly attributing statements and commitments to the specific individual who made them | 60-80% Text inference only, no identity layer | >95% Pre-model identity resolution | Structural advantage |
Entity Disambiguation Correctly resolving "Mark," "the Henderson deal," or "the Q2 launch" across a large corpus | 65-80% Text inference only, no identity layer | >95% Directory-resolved at ingestion | Structural advantage |
Temporal Precision Correctly placing events and statements in time: "last Tuesday" vs. "three months ago" | 55-75% Text inference only, no metadata layer | >95% Metadata-resolved at ingestion | Structural guarantee |
Signal™ Architecture Stack
Signal™ performance metrics reflect five architectural layers applied before and during AI processing. Pre-model identity resolution: at onboarding, a full employee directory (name, role, department, and all connector IDs: email, Slack handle, business phone) is ingested; every communication is definitively attributed to a named individual before any AI model processes it. Complete metadata capture: all call times, durations, counterparties, message timestamps, and meeting metadata are captured as structured data at ingestion; temporal context is resolved structurally, not inferred from text. Automated structured data extraction at ingestion. Fine-tuned and post-trained proprietary models optimized specifically for corporate communications intelligence. Custom tool harness with rigorous system prompt engineering and guardrailing algorithms.
Metrics marked Structural guarantee reflect pre-model architectural solutions: the AI receives pre-resolved attribution, identity, and temporal context as structured inputs, not natural-language inference problems. Generalized AI performance figures reflect published benchmarks and credible estimates for frontier models (GPT, Claude) on domain-specific enterprise tasks without specialized data architecture.