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.
EU AI Act Compliant
Technical documentation of all AI decisions, structured logging with JSON schemas, human oversight gates.
PCAOB Guidance
Adversarial review protocols, professional skepticism enforcement, complete audit trails.
Fresh Chat Separation
Reviewer agents never see Builder agents' reasoning. Only factual outputs are shared.
Risk Tiering (T0-T3)
Higher-risk work requires more verification layers, different model families, and specialized review.
Audit Trail
Decision logs capture model attribution, risk tier, metrics, and human overrides.
How We Use AI Without Exposing Your Identity
AI is powerful—but only if it respects privacy. Our pipeline is built so that your identity stays protected while your data is still useful for analysis.
De-Identification Before Any AI Call
Remove direct identifiers, replace with neutral labels, mask quasi-identifiers.
Data Minimization by Design
Extract only the minimum necessary snippet for each question.
No Training on Your Data
We do not use your case data to train models without explicit consent.
You Stay in Control
See what was sent to AI, request deletion, and maintain separation.
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.