Executive dashboard
KPIs for tracked decisions, implementation rate, active contradictions and strategic alignment.
Reduce risk / Internal Audit AI
Your strategy says one thing. The evidence says another.
Internal Audit AI turns Management Compass into a product page and operating model: track every strategic decision, score it against real evidence, detect contradictions and package audit-ready workpapers before the next board review.
20Decisions tracked in the prototype
35%Implementation rate visible to executives
15Active contradictions surfaced
management compass / dashboard
The blind spot
The gap between decision and reality
The proof that strategy is executing lives in systems no executive has time to reconcile. So the gap stays invisible until it is expensive.
01
Decisions without proof
Most decisions are reported as progressing, yet few are genuinely on track.
02
Contradictions build unseen
Evidence that conflicts with the plan rarely surfaces in time for management action.
03
No line back to source
"How do we know?" has no fast, traceable answer during board or audit review.
04
Audit arrives late
Workpapers and evidence packs are assembled after the risk has already matured.
The platform
From decision to evidence
Three connected views: from a single strategic alignment score down to the source record that proves or contradicts a decision.
Decision log
Every decision carries an evidence verdict, contradiction load, implementation score and source reference.
Evidence trail
Quarter-by-quarter signals link back to source documents, confidence scores and mitigations.
Audit workpapers
Findings, evidence, owners and sign-off history are generated into audit-ready packages.
Product modules
The audit product behind Management Compass
A client needs more than a dashboard. The product includes ingestion, scoring, contradiction detection, workflow and export layers so audit teams can operationalize the evidence.
Ingestion
Decision capture
Extract strategic decisions from board packs, committee notes, strategy decks, risk reviews and operating updates.
Evidence
Signal classification
Classify evidence as strong, positive, weak, missing or contradicted, with source references and confidence levels.
Contradictions
Gap detection
Detect where operating reality no longer supports the management narrative or decision intent.
Workflow
Owner follow-up
Assign evidence gaps to owners, track mitigation, add reviewer comments and maintain sign-off history.
Reporting
Audit-ready workpapers
Generate source-linked workpapers, exception packs, evidence trails and board-ready summaries.
Governance
Model and access controls
Permissions, model versioning, prompt logs, source retention and reviewer overrides for defensible AI use.
Product visual
Cross-reference trail for every decision
The detail view shows the decision, status, implementation score and an evidence timeline. Auditors can see what changed, when it changed, and which source record supports the assessment.
management compass / cross-reference trail
Data sources
What the product needs to read
Implementation starts by agreeing which sources are authoritative and how evidence should be weighted. The system can begin with documents, then expand into structured operational systems.
Governance documentsBoard packs, committee minutes, strategy notes, decision memos, risk appetite statements and policy documents.
Operating evidenceProject status, OKRs, incident logs, risk registers, issue trackers, implementation plans and owner updates.
Financial signalsBudget movement, cost-to-complete, revenue impact, project burn, delivery delays and benefits-realization data.
Control evidenceTesting results, control attestations, exceptions, remediation actions, approvals and sign-off records.
Human reviewAuditor notes, owner responses, reviewer overrides and final management action plans.
Operating model
Close the gap before the next review
Internal Audit AI runs continuously across strategy notes, risk reviews, board packs and operating updates, then turns signals into audit evidence.
01
Ingest decisions
Board packs, strategy notes, risk reviews and operating updates are normalized.
02
Score evidence
Signals are classified as strong, positive, weak or contradicted with source links.
03
Surface gaps
The system highlights decisions where reality no longer matches the narrative.
04
Package audit
Workpapers, mitigations and owner follow-ups are produced for review.
Implementation
How it gets installed inside a bank
Internal Audit AI can start as a controlled evidence-review pilot and then expand into continuous audit coverage across decisions, controls and management actions.
01
Scope the audit lens
Choose the decision domain, source systems, evidence taxonomy and review workflow.
02
Connect sources
Load documents, configure source permissions and map structured systems or exports.
03
Calibrate scoring
Align evidence verdicts, contradiction thresholds and reviewer override rules with audit methodology.
04
Run controlled pilot
Validate findings with auditors and owners, then package outputs for committee review.
Internal Audit AI is not a chatbot. It is a decision-control product: one place to reconcile management intent, operational reality and audit evidence.
For executives
Know which decisions are drifting before the board asks for an explanation.
For internal audit
Move from sample-based after-the-fact testing to continuous evidence review.
For risk teams
Prioritize contradictions and weak evidence by business impact.
For regulators
Show a defensible trail from decision to evidence to mitigation.
Buyer pack
What the audit sponsor receives before rollout
The product needs to pass internal scrutiny. We package the pilot plan, data boundaries, AI governance and output examples before scaling.
Audit artifacts
- Evidence taxonomy and contradiction scoring methodology
- Sample decision register and source-linked evidence trail
- Workpaper template, findings format and owner follow-up workflow
- Committee-ready dashboard and exception pack
Implementation artifacts
- Source inventory, data-retention rules and permission model
- AI governance controls, model logs and reviewer override policy
- Pilot timeline, user roles, acceptance criteria and success metrics
- Security review checklist and production operating model
Typical first phase: 2-3 weeks to configure a controlled decision domain, load evidence, calibrate scoring and produce a management-ready pilot pack.
Commercial outcomes
Why internal audit and executives buy it
Earlier warningFind strategic drift and contradictory evidence before quarterly review.
Less manual workReduce evidence collection and source reconciliation effort for audit teams.
Better challengeGive executives and auditors a shared factual basis for management challenge.
Defensible AIKeep source links, reviewer decisions and model behavior visible for governance.
Turn strategic decisions into auditable evidence.
Use Management Compass as the first productized Internal Audit AI experience, with screenshots, workflow and evidence trail already ready for stakeholder review.