How is sensitive data protected?

All documents are encrypted with AES-256-GCM at rest and TLS 1.2+ in transit. A GDPR anonymization layer removes PII before any AI processing. Hosted on Vercel + Supabase (both SOC 2 Type II); Decision Intel’s own product-level audit is targeted for Q4 2026.

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 or a general AI assistant?

ChatGPT gives one opinion from one model: ungoverned, untraceable, unaudited. Decision Intel is the reasoning audit platform. Most tools audit your data; we audit your reasoning — and catch the fatal blind spots in strategic memos before the committee does. It measures the noise in your reasoning the same way Kahneman did in the insurance underwriter study, simulates an AI boardroom of CEO, CFO, and board personas, runs what-if interventions against a 143-case public reference library, and compounds every confirmed outcome back into a calibrated Decision Quality Index your audit committee can defend. Not a chatbot; a reasoning audit, checkable from memo to outcome.

Which cognitive biases caused Rivian Automotive's damage?

5 bias patterns are documented in this case, led by Planning Fallacy. The full set: Planning Fallacy, Overconfidence Bias, Anchoring Bias, Optimism Bias, Bandwagon Effect. Each is mapped to Decision Intel's 22-bias R²F taxonomy with stable IDs and academic citations.

What is Decision Intel's reasoning audit?

Decision Intel runs a 12-node Recognition-Rigor Framework (R²F) pipeline on strategic memos, detecting cognitive biases from a 22-bias canonical taxonomy and producing a Decision Quality Index (DQI) score. The audit produces a hashed, tamper-evident Decision Provenance Record (DPR) suitable for audit-committee review.

All case studies

Partial failureTechnology2021

Rivian Automotive

Rivian Production Target Failures

Estimated impact: $55B+ in market cap decline from peak

D44

Decision Quality Index (simulated)

Poor · 5 biases detected, 2 toxic combinations

Rivian went public at $66B valuation with promises to deliver 1,200 vehicles in 2021. Delivered only 920 despite $0 revenue history. Production targets were consistently missed through 2022-2023.

Decision context

IPO valuation anchored on ambitious production forecasts that assumed manufacturing ramp rates comparable to mature automakers. Supply chain constraints and quality issues were systematically underestimated. Board and investors accepted optimistic timelines without independent manufacturing assessment.

Decision anatomy

Monetary stakes

Very high

Dissent suppressed

Time pressure

Present

Unanimous consensus

Decision participants

Dissent encouraged

External advisors

Iterative process

Red = risk factor present · Green = protective factor present

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

★Planning Fallacy

critical

Overconfidence Bias

high

Anchoring Bias

high

Optimism Bias

medium

Bandwagon Effect

medium

★ Primary driver · Severity estimated from bias type and decision outcome

3D Decision Knowledge Graph

Rivian Automotive · 5 biases, 2 toxic combinations

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

!Optimism Trap

Overconfidence and availability bias create a systematically positive outlook that ignores base rates.

!Blind Sprint

Overconfidence combined with planning fallacy creates unstoppable momentum toward failure.

Reference class base rates

Across all 143 curated case studies in our library:

89%

base failure rate

-0%

partial / mixed

11%

base success rate

■ Failure■ Partial■ Success

Lessons learned

EV manufacturing ramp rates cannot be benchmarked against software scaling curves
IPO valuations based on production forecasts require independent manufacturing due diligence
Supply chain complexity in automotive requires 2-3x timeline buffers

Source: Rivian SEC Filing S-1 2021; 10-K 2022; Bloomberg production analysis (SEC Filing)

Pilot slots available

We caught these patterns in Rivian Automotive's own record — before the outcome.

See the full bias auditwe ran — no login, no card. Then run the same 60-second audit on your own next memo.

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Workflows that fire on decisions like Rivian Automotive’s

The same Recognition-Rigor Framework that documents this case audits memos in the same shape — before the outcome forces the lesson.

For CSOs and Heads of Strategic Planning

Strategic memo audit

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Run a structured pre-mortem before the decision is final — surface failure modes the room cannot see from the inside.

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Audit a board deck before the presentation — predict the toughest questions the room will ask.

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All case studies

Partial failureTechnology2021

Rivian Automotive

Rivian Production Target Failures

Estimated impact: $55B+ in market cap decline from peak

D44

Decision Quality Index (simulated)

Poor · 5 biases detected, 2 toxic combinations

Rivian went public at $66B valuation with promises to deliver 1,200 vehicles in 2021. Delivered only 920 despite $0 revenue history. Production targets were consistently missed through 2022-2023.

Decision context

Decision anatomy

Monetary stakes

Very high

Dissent suppressed

Time pressure

Present

Unanimous consensus

Decision participants

Dissent encouraged

External advisors

Iterative process

Red = risk factor present · Green = protective factor present

Biases present in the decision

★Planning Fallacy

critical

Overconfidence Bias

high

Anchoring Bias

high

Optimism Bias

medium

Bandwagon Effect

medium

★ Primary driver · Severity estimated from bias type and decision outcome

3D Decision Knowledge Graph

Rivian Automotive · 5 biases, 2 toxic combinations

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

!Optimism Trap

Overconfidence and availability bias create a systematically positive outlook that ignores base rates.

!Blind Sprint

Overconfidence combined with planning fallacy creates unstoppable momentum toward failure.

Reference class base rates

Across all 143 curated case studies in our library:

89%

base failure rate

-0%

partial / mixed

11%

base success rate

■ Failure■ Partial■ Success

Lessons learned

EV manufacturing ramp rates cannot be benchmarked against software scaling curves
IPO valuations based on production forecasts require independent manufacturing due diligence
Supply chain complexity in automotive requires 2-3x timeline buffers

Source: Rivian SEC Filing S-1 2021; 10-K 2022; Bloomberg production analysis (SEC Filing)

Pilot slots available