Why we grade every decision’s evidence level before issuing a strategic recommendation.
GRADE (Grading of Recommendations Assessment, Development and Evaluation) is a transparent system for rating the certainty of evidence and the strength of recommendations. Stratensight applies GRADE to patent intelligence so that every verdict carries an auditable certainty label, not just a score.
Two questions sit behind every recommendation: how much can we trust the underlying signal? and how confidently should we recommend acting on it? GRADE is the discipline of answering each separately and disclosing both.
GRADE was developed by the GRADE Working Group, an international collaboration of methodologists, clinicians, and statisticians. It is the standard adopted by the World Health Organization (WHO), the National Institute for Health and Care Excellence (NICE), UpToDate, and Cochrane for grading clinical evidence.
GRADE separates two concepts that are routinely conflated:
A recommendation can be strong even when certainty is moderate (when downsides of inaction outweigh uncertainty), and conditional even when certainty is high (when context-specific tradeoffs dominate).
Stratensight starts every analysis at HIGH certainty and applies up to two-level downgrades per factor. The floor is VERY_LOW. Each factor has a documented threshold in the codebase — we publish what we measure today, and we publish what we have not yet covered.
HOW STRATENSIGHT MEASURES IT
NOT YET COVERED
Citation completeness, partial patent records. On our research roadmap.
HOW STRATENSIGHT MEASURES IT
NOT YET COVERED
Confidence intervals on individual metrics. On our research roadmap.
HOW STRATENSIGHT MEASURES IT
NOT YET COVERED
Cross-metric contradiction detection (e.g. high momentum but low maturity). On our research roadmap.
HOW STRATENSIGHT MEASURES IT
NOT YET COVERED
CPC scope breadth, geographical representativeness. On our research roadmap.
HOW STRATENSIGHT MEASURES IT
NOT YET COVERED
Academic vs industrial filing ratio as standalone signal. On our research roadmap.
Once the five factors have been applied, the resulting evidence certainty is paired with a recommendation strength. The combination shapes the verdict the user sees on every analysis.
GRADE assessment runs automatically inside the analysis engine. No analyst intervention. The pipeline is auditable end to end.
grade_assessor.py and is covered by anti-regression tests. Changes go through code review, not configuration drift.Independent audit report — in preparation.
HOW GRADE FEEDS LAYER C
The final GRADE level (HIGH / MODERATE / LOW / VERY_LOW) drives the Tier gate (Layer C) directly. HIGH opens TIER_HIGH (raw verdict preserved). MODERATE or LOW open TIER_MODERATE (verdict mapped to a directional signal: INVEST → OPPORTUNITY_SIGNAL, MONITOR → MIXED_SIGNAL, EXPLORE → WEAK_SIGNAL, AVOID → NEGATIVE_SIGNAL). VERY_LOW triggers TIER_LOW — the verdict is fully withheld and replaced by INSUFFICIENT_DATA.
This closes the loop: evidence_certainty is not just a metadata badge — it gates the verdict surface itself. See the methodology page Layer C section for the complete categorical mapping.
The GRADE framework is widely documented. The five sources below are entry points for readers who want to verify our application against the canonical literature.
Evidence certainty meets the wider epistemic contract behind every Stratensight verdict.
Four-layer epistemic contract (hedging, grounding, refusal, source tagging) plus the eighteen user-facing tokens that appear across decision narratives and persona insights.
Stratensight provides patent intelligence signals, not legal opinions or freedom-to-operate assessments. Not a substitute for IP counsel.