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.

All comparisons

Decision Intel vs ChatGPT (or general-purpose LLMs)

ChatGPT is a general-purpose LLM; Decision Intel is a purpose-built reasoning-audit engine.

Decision Intel vs ChatGPT (or general-purpose LLMs)

ChatGPT is a general-purpose LLM; Decision Intel is a purpose-built reasoning-audit engine.

What ChatGPT (or general-purpose LLMs) does well

ChatGPT and other general-purpose LLMs (Claude, Gemini, Grok) are extraordinarily capable at generating, summarising, and critiquing text — including strategic memos. Cheap, fast, ubiquitous, with steadily improving reasoning quality. The bar to "get a useful second opinion on a memo" is now near-zero with any general LLM.

Where Decision Intel is different

Decision Intel is a purpose-built reasoning-audit engine, not a general LLM wrapper. The differentiation comes from: (1) the structured 22-bias canonical taxonomy with stable IDs (DI-B-001 → DI-B-022) — a general LLM has no fixed taxonomy and may hallucinate bias names; (2) the 143-case reference-class library against which every audit is scored; (3) the methodology-version-stamped Decision Quality Index with held-out-sample regression coverage; (4) the multi-judge cross-model jury (Gemini + Grok) measuring noise across three orthogonal sources of variance; (5) the hashed + tamper-evident Decision Provenance Record mapped onto EU AI Act Article 14 record-keeping. A general LLM produces a critique; Decision Intel produces a defensible, scored, audit-trail-ready artefact.

Axis	Decision Intel	ChatGPT (or general-purpose LLMs)
General-purpose conversational reasoning	Not the product surface	Core capability
Stable 22-bias taxonomy with DI-B-001..022 IDs	Canonical, published, citable, never hallucinated	No fixed taxonomy; ad-hoc per response
Methodology-version-stamped Decision Quality Index	Version 2.4.0 with held-out regression suite	No reproducible scoring
Multi-judge cross-model noise jury (3 frames, 2 model families)	Built-in; measures stochastic + architectural + framing variance	Single response, no variance measurement
143-case public reference library, similarity-scored per audit	Active on every memo	Training-data scraped; no per-audit retrieval
Hashed + tamper-evident audit-trail artefact (DPR)	Procurement-grade, regulator-defensible	Conversation log only
Regulatory mapping (EU AI Act, Basel III, NDPR, etc.)	19 frameworks across G7 + EU + GCC + African markets	Not in product
Per-org Brier-scored calibration over time	Active; published baseline 0.258	Stateless across conversations

FAQ

Can I just use ChatGPT to audit my memos?

You can use ChatGPT to get a critique — it will surface obvious issues, draft counter-arguments, name some biases. What you cannot get from a general LLM: a stable taxonomy that does not drift between sessions, a methodology-version-stamped score, a noise-jury reading from multiple model families, a cited reference-class comparison from a 143-case public library, or an audit-trail artefact that maps onto EU AI Act Article 14. If the memo will go to an audit committee, those are not optional; they are the artefact your reasoning has to defend.

How is Decision Intel different from a custom GPT or a Cloverpop-style prompt?

A prompt is configuration; Decision Intel is engineered infrastructure. The canonical taxonomy, the 12-node pipeline, the cross-model noise jury, the held-out DQI regression tests, the Brier-scored recalibration flywheel, the regulatory crosswalk — none of these can be replicated by a longer system prompt. The deeper answer: prompts produce text, Decision Intel produces an auditable record.

Will general LLMs eventually replace this?

Unlikely on the audit-trail dimension. Even as general LLMs improve, the requirements for a defensible audit artefact (stable IDs, reproducible scoring, regulatory mapping, tamper-evidence) are infrastructure concerns, not capability concerns. A more capable LLM does not produce a hashed audit log by itself; the discipline has to be designed in.

Other comparisons

vs Cloverpop

Cloverpop logs decisions; Decision Intel audits them.

vs IBM watsonx.governance

IBM audits the model; Decision Intel audits the human reasoning.

vs Aera Technology

Aera automates supply-chain decisions; Decision Intel audits strategic ones.

vs Palantir

Palantir is data-platform infrastructure; Decision Intel is the reasoning-audit layer above it.

vs McKinsey (or any strategy consultancy)

A McKinsey engagement is a one-time deliverable; Decision Intel is always-on infrastructure.

Run the audit on a real memo.

The category claim is the H1. The category proof is the artefact. Paste a strategic memo and see the audit run end-to-end, free.

Run the live demo

ChatGPT is a general-purpose LLM; Decision Intel is a purpose-built reasoning-audit engine.

What ChatGPT (or general-purpose LLMs) does well

Where Decision Intel is different

Axis	Decision Intel	ChatGPT (or general-purpose LLMs)
General-purpose conversational reasoning	Not the product surface	Core capability
Stable 22-bias taxonomy with DI-B-001..022 IDs	Canonical, published, citable, never hallucinated	No fixed taxonomy; ad-hoc per response
Methodology-version-stamped Decision Quality Index	Version 2.4.0 with held-out regression suite	No reproducible scoring
Multi-judge cross-model noise jury (3 frames, 2 model families)	Built-in; measures stochastic + architectural + framing variance	Single response, no variance measurement
143-case public reference library, similarity-scored per audit	Active on every memo	Training-data scraped; no per-audit retrieval
Hashed + tamper-evident audit-trail artefact (DPR)	Procurement-grade, regulator-defensible	Conversation log only
Regulatory mapping (EU AI Act, Basel III, NDPR, etc.)	19 frameworks across G7 + EU + GCC + African markets	Not in product
Per-org Brier-scored calibration over time	Active; published baseline 0.258	Stateless across conversations

FAQ

Can I just use ChatGPT to audit my memos?

How is Decision Intel different from a custom GPT or a Cloverpop-style prompt?

Will general LLMs eventually replace this?