How is sensitive data protected?

All documents are encrypted with AES-256-GCM at rest and TLS 1.3 in transit. A GDPR anonymization layer removes PII before any AI processing. Your data never leaves our SOC 2 certified infrastructure.

How long does integration take?

Less than 30 minutes. Upload documents directly, connect via OAuth for Slack, or use our REST API for bulk processing.

How does outcome tracking work?

Outcomes are detected automatically from follow-up documents, Slack messages, and web intelligence. You confirm with one click. Each reported outcome makes your future analyses more accurate.

How is Decision Intel different from ChatGPT?

ChatGPT gives one opinion from one model. Decision Intel uses 3 independent judges for noise measurement, a 20×20 bias interaction matrix for compound scoring, 31 domain-specific biases, and an outcome flywheel that gets smarter with every decision.

All case studies

Catastrophic failureHealthcare1996

Purdue Pharma

Purdue Pharma OxyContin Marketing

Estimated impact: $8.3B

F17

Decision Quality Index

Critical — 5 biases detected, 1 toxic combinations

Purdue Pharma aggressively marketed OxyContin as a low-addiction-risk painkiller, contributing to the opioid epidemic that has killed over 500,000 Americans. The company knew its claims about addiction risk were false but continued its marketing campaign for over two decades, driven by confirmation bias around its own flawed studies.

Decision context

Whether to market OxyContin with claims of low addiction potential based on limited and misleading evidence, and whether to continue aggressive sales tactics as addiction reports mounted.

Deep hindsight-stripped analysis coming soon. This case is part of our ongoing backfill to reconstruct what the platform would have flagged at decision time. The summary and bias profile below are drawn from primary sources.

Biases present in the decision

★ Confirmation BiasLoss AversionGroupthinkAuthority BiasSelective Perception

3D Decision Knowledge Graph

Purdue Pharma — 5 biases, 1 toxic combination

Drag to rotate · Scroll to zoom · Click to explore

Decision

Critical

High

Medium

Toxic

Bias Intensity Profile

Severity × confidence across 5 detected biases

Toxic combinations

Echo Chamber

Reference class base rates

Across all 146 curated case studies in our library:

90%

base failure rate

10%

base success rate

Lessons learned

Confirmation bias in interpreting addiction data allowed Purdue to maintain claims that contradicted real-world evidence for over two decades.
Loss aversion around a profitable product line can cause companies to ignore catastrophic societal harm.
Regulatory capture and aggressive lobbying can delay corrective action by years, amplifying public health damage.

Source: DOJ Settlement with Purdue Pharma (2020); Patrick Radden Keefe, "Empire of Pain" (2021) (Case Study)

Pilot slots available

See what we'd flag in your next strategic memo.

Upload a strategic memo or board deck. Get the same bias audit you just saw for Purdue Pharma, on your own high-stakes call, in under 60 seconds.

Try the Demo — Free Request a Pilot

Or leave your email, we'll run a strategic memo of your choosing and send the readout within a business day.

Ready to audit your own memo right now? Create a free account →

Related cases in Healthcare

Catastrophic failure2004

Johnson & Johnson Talc Powder Litigation

Catastrophic failure2014

Theranos

Theranos Blood Testing Patient Safety Failures

Lessons learned

Confirmation bias in interpreting addiction data allowed Purdue to maintain claims that contradicted real-world evidence for over two decades.

Loss aversion around a profitable product line can cause companies to ignore catastrophic societal harm.

Regulatory capture and aggressive lobbying can delay corrective action by years, amplifying public health damage.