Our Methodology
Signal-Grounded. Multi-Model. Transparent.
Every Grey Matter Report follows a structured methodology: real-time signals ground AI analysis, five frontier models reach consensus, and you get a calibration report in seconds.
Four Signal Layers
Before any model scores a technology, we ground the assessment in real-world data. Four signal layers provide the context that prevents AI hallucination and ensures relevance.
Economic Signals
Market capitalization, funding rounds, revenue trends, and competitive dynamics. These signals reveal whether a technology vendor is growing, stable, or at risk.
Developer Friction Signals
Stack Overflow trends, GitHub issue velocity, documentation quality scores, and developer sentiment. Real friction data reveals the gap between marketing and daily experience.
Research Signals
Academic publication velocity, patent filings, benchmark results, and technical maturity indicators. These signals predict where technology is heading, not just where it is.
Hiring Signals
Job posting velocity, skill demand trends, salary benchmarks, and talent availability. Hiring data reveals organizational commitment to specific technology bets.
Five-Model Consensus Engine
Signal-grounded context is fed to five frontier AI models in parallel. Each model independently evaluates your technology stack across every dimension, producing scores, confidence levels, and detailed rationale.
Signal Ingestion
Four signal layers are collected and formatted into structured context. Economic, friction, research, and hiring data provide the grounding that prevents speculative assessment.
Model Grounding
Each model receives your CTO self-assessment alongside the signal context. Models are instructed to score based on evidence, not speculation, and to explain their reasoning.
Five-Model Consensus
Five frontier models (Claude, GPT-4o, Gemini, Llama, DeepSeek) independently evaluate each dimension. Confidence-weighted aggregation identifies consensus, divergence, and contrarian signals across models.
Calibration Report
Your personal divergence report shows exactly where your self-assessment aligns with and diverges from AI consensus. Per-model breakdowns, IQ scores, and actionable recommendations are generated in seconds.
Understanding IQ Scores
Calibration IQ scores measure alignment between your self-assessment and multi-model AI consensus. Higher scores mean your view matches the signal-grounded AI evaluation.
| Score Range | Consensus Level | Interpretation |
|---|---|---|
| 90–100 | Very High | Near-universal model agreement. Your assessment is well-calibrated with AI consensus. Proceed with high confidence. |
| 75–89 | High | Strong consensus with minor divergence areas. Monitor flagged dimensions but overall calibration is strong. |
| 50–74 | Moderate | Mixed model opinion. Significant divergence detected between your assessment and AI consensus. Review per-model rationale. |
| 25–49 | Low | Substantial divergence. Models flag areas where your assessment differs significantly from signal-grounded analysis. |
| 0–24 | Very Low | Models fundamentally disagree with your assessment. Consider a deep-dive analysis to understand the divergence drivers. |
Our Guiding Principles
Consensus Over Opinion
A single model can hallucinate. Five models grounded in real signals produce robust consensus. We measure and report model agreement because confidence matters as much as direction.
Transparency Over Authority
We show our work. Every IQ score links back to per-model rationale, signal layer data, and confidence levels. You never have to take a number on faith.
Signals Over Speculation
Every model assessment is grounded in real-time economic, friction, research, and hiring data. No speculative analysis. No vendor-influenced conclusions.
Speed Over Ceremony
Your calibration report generates in seconds, not weeks. Fast feedback loops mean you can iterate on your technology strategy in real time.
Signal-Grounded. Model-Consensus. Instant.
Ready to see how your tech stack assessment compares to AI consensus? Start your calibration now.
Start Calibrating