The Triangulation Methodology
A single data point or model output is a vector, not a conclusion. True insight is achieved by synthesizing information from multiple, disparate sources.
Three Pillars of Evidence
Every analysis rests on the triangulation of these three independent information sources.
Client-Provided Data
The "Ground Truth"
Subjective experiences, timelines, symptom logs, medication/supplement regimens, and known variables. This is the foundation — your lived experience and documented data.
Scientific Literature
The "Established Knowledge"
Peer-reviewed research from PubMed, clinical trials, pharmacological databases, and evidence-based summaries. This grounds our analysis in validated science.
Multi-Model AI Synthesis
The "Computational Engine"
Multiple deep learning models (GPT, Claude, Gemini, Perplexity) analyze complex interactions and generate hypotheses at a scale impossible through manual research.
Why Multi-Model AI Matters
AI language models have a well-documented behavioral pattern: sycophancy. When one model reviews another's work (or its own), it tends to agree rather than critically evaluate.
This creates "hallucinated consensus" — false confidence when multiple models miss the same blind spots or echo each other's flaws.
Our solution: Independent analysis across different model families, fresh chat sessions for each role, and adversarial review when agreement exceeds 80%.
The Problem: Echo Chambers
Single-model analysis or models reviewing each other leads to reinforcement of errors and missed edge cases.
The Solution: Independent Verification
Each AI platform analyzes your case independently. No platform sees another's reasoning — only raw data and test outputs.
The Result: Higher Confidence
When multiple independent models converge on the same finding, we have real evidence. When they diverge, we dig deeper.
Gold Standard Quality Controls
While AI governance is still evolving, we've chosen to align with emerging global standards now rather than wait for regulations to force compliance.
AICPA QM Aligned
Risk-based quality management with documented verification, continuous monitoring, and structured decision logging. Model scorecards track performance over time.
EU AI Act Compliant
Technical documentation of all AI decisions, structured logging with JSON schemas, human oversight gates, and full transparency on models used.
PCAOB Guidance
Adversarial review protocols, professional skepticism enforcement, complete audit trails, and devil's advocate challenges when needed.
Fresh Chat Separation
Reviewer agents never see Builder agents' reasoning. Only factual outputs (test results, diffs) are shared — never prose explanations or summaries.
Risk Tiering (T0-T3)
Every analysis is risk-assessed. Higher-risk work requires more verification layers, different model families, and additional specialized review lenses.
Audit Trail
Decision logs capture model attribution, risk tier, metrics, and human overrides. Review reports document findings across multiple perspectives.
How We Prevent AI Echo Chamber Failures
Fresh Chat Sessions
Each review role gets a completely fresh chat session. The Reviewer never sees the Builder's reasoning — only the code diff, original requirements, and actual test outputs.
Different Model Families
For high-risk work, we require analysis from different model families (e.g., Claude, GPT, and Gemini) to ensure architectural diversity in reasoning.
Devil's Advocate Review
When agreement exceeds 80% across reviewers, we trigger an adversarial review to challenge the consensus and look for missed edge cases.
Only Tool Outputs Are Shared
We never share prior chat logs, AI-generated summaries, or "explanations". Only factual data: test results, lint output, actual diffs, and source requirements.
Experience the Difference Rigor Makes
When you need reliable insights from complex data, our triangulation methodology ensures you get evidence-based clarity, not hallucinated consensus.