ThirdEye
Data-Fused Predictive Foresight & Intelligence Pipeline
A state-machine-driven predictive analysis pipeline that aggregates multi-source data fusion, cognitive simulation, and automated certainty scoring into board-level foresight reports.
System Overview & Engineering Purpose
ThirdEye was engineered to transform noisy market, operational, or threat signals into defensible foresight reports. Managed via a MongoDB PredictionReport state machine (queued → gathering_data → analyzing → finalizing), ThirdEye invokes dedicated scenario priming, data fusion, and meta-evaluator services. If analytical certainty falls below 0.60, it automatically mandates prominent low-confidence disclosures.
Execution Pipeline & Component Nodes
Step-by-Step Runtime Protocol
Scenario Priming & Intent Parsing
ScenarioPrimer primes the strategic operational environment while IntentParser extracts core risk drivers and reporting requirements.
Data Fusion Aggregation
dataFusionService queries and fuses disparate signals into a unified operational context.
Simulation & Governance Audit
Executes scenario simulations, running parallel governance and ethical boundary checks across candidate analytical projections.
Certainty Scoring & Synthesis
metaEvaluatorService computes mathematical certainty. If < 0.60, a prominent warning is injected before synthesisService renders the report.
Implementation Snippet
const LOW_CONFIDENCE_THRESHOLD = 0.6;
const prepareSynthesizerInput = (report) => {
const certainty = report.evaluatedAnalysis?.metrics?.certainty ?? 1.0;
if (certainty < LOW_CONFIDENCE_THRESHOLD) {
const warningInstruction = `
CRITICAL INSTRUCTION: The underlying analysis for this report yielded a low certainty score of ${certainty.toFixed(2)}.
You MUST begin your final report with a prominent "Low Confidence Warning" section. This section must explicitly state that the analysis is preliminary and based on sparse or conflicting data.`;
report.parsedIntent.output_format_instructions += warningInstruction;
}
return report;
};