Value Scanner
Extract actual value priorities from decision logs — not slogans. Surface the gap between stated and practiced values across your organization.
What Does Your Organization Actually Value?
Not what it says.
What it decides.
Organizations often declare values. Few know which values are actually practiced.
The Gap Between Words and Actions
Stated Values
- "We prioritize customer experience"
- "Innovation is our core value"
- "We move fast and take risks"
- "Employee wellbeing comes first"
From mission statements, town halls, and strategy decks
Practiced Values
- Cost cuts approved 3x faster than UX improvements
- 80% of "innovative" proposals stopped at risk review
- Approval delays average 4.2 days for external writes
- Overtime requests auto-approved 94% of the time
From actual decisions, approvals, and system behavior
The difference is not hypocrisy.
It's lack of visibility.
Collective Values Scanner
Scanning What Matters Most
MARIA OS continuously scans and evaluates collective values across your organization, transforming qualitative principles into quantifiable decision constraints.
Value Extraction
Extracts and normalizes values from organizational context.
Conflict Detection
Identifies potential conflicts before they become blockers.
Priority Encoding
Encodes priorities as hard constraints for decisions.
"Values are not suggestions. They are the immutable laws that govern every decision."
The Value Extraction Pipeline
From enterprise data to governed AI operations
Neural Value Network
Value Hierarchy Processing
Values are processed hierarchically. L0 constraints must pass before L1 optimization.
Undefined values trigger safe defaults.
Value Extraction & Analysis
From raw enterprise data to structured, weighted decision values
Initiating value extraction from scanned enterprise documents... First, I need to identify explicit policy statements. Scanning document: "Corporate Governance Policy v3.2" Found statement: "All financial transactions exceeding $10,000 require dual approval from department head and finance." Extracting value: FINANCIAL_APPROVAL_THRESHOLD = $10,000 Extracting value: APPROVAL_REQUIREMENT = DUAL_SIGN_OFF Confidence score: 0.94 Now analyzing implicit patterns from historical decisions in the CRM database... Pattern detected: Customer refunds above $500 consistently escalated to senior management in 847 of 892 cases (94.9%) Extracting implicit value: REFUND_ESCALATION_THRESHOLD = $500 Extracting implicit value: ESCALATION_TARGET = SENIOR_MANAGEMENT Confidence score: 0.89 Cross-referencing with email communications archive... Found recurring phrase in executive correspondence: "customer retention takes priority over short-term cost savings" Mapping to value hierarchy: CUSTOMER_RETENTION > SHORT_TERM_COSTS Weight assignment: CUSTOMER_RETENTION = 0.85, SHORT_TERM_COSTS = 0.45 Confidence score: 0.78 Analyzing Slack channel #finance-approvals for decision patterns... Identified 1,247 approval conversations over 18 months Extracting decision factors: urgency, amount, vendor_relationship, budget_status Building decision tree from historical outcomes... Decision pattern extracted: IF urgency=HIGH AND vendor_relationship=STRATEGIC THEN fast_track_approval Confidence score: 0.91 Processing code repository commit messages and PR reviews... Pattern: Security-related PRs require approval from security team lead Extracting value: SECURITY_PR_APPROVAL = MANDATORY Extracting value: SECURITY_APPROVER_ROLE = SECURITY_TEAM_LEAD Confidence score: 0.97 Analyzing financial database transaction logs... Identifying spending patterns by department... Detected anomaly threshold: Transactions 3x above department average trigger review Extracting value: ANOMALY_DETECTION_MULTIPLIER = 3.0 Extracting value: ANOMALY_ACTION = TRIGGER_REVIEW Confidence score: 0.86 Now consolidating extracted values into value clusters... Cluster 1: FINANCIAL_GOVERNANCE - FINANCIAL_APPROVAL_THRESHOLD: $10,000 (weight: 0.92) - DUAL_APPROVAL_REQUIRED: true (weight: 0.94) - ANOMALY_MULTIPLIER: 3.0 (weight: 0.86) - REFUND_ESCALATION: $500 (weight: 0.89) Cluster 2: CUSTOMER_PRIORITY - RETENTION_PRIORITY: HIGH (weight: 0.85) - COST_SENSITIVITY: MEDIUM (weight: 0.45) - SATISFACTION_THRESHOLD: 0.8 (weight: 0.72) Cluster 3: SECURITY_COMPLIANCE - CODE_REVIEW_MANDATORY: true (weight: 0.97) - SECURITY_APPROVAL_ROLE: SECURITY_LEAD (weight: 0.97) - DATA_ACCESS_LOGGING: REQUIRED (weight: 0.93) Cluster 4: OPERATIONAL_VELOCITY - FAST_TRACK_CONDITIONS: [urgency=HIGH, relationship=STRATEGIC] (weight: 0.91) - STANDARD_SLA: 48_HOURS (weight: 0.78) - ESCALATION_TIMEOUT: 24_HOURS (weight: 0.82) Calculating inter-cluster relationships and potential conflicts... Conflict detected: CUSTOMER_PRIORITY vs FINANCIAL_GOVERNANCE at refund threshold boundary Resolution strategy: Apply CUSTOMER_RETENTION weight (0.85) > SHORT_TERM_COSTS (0.45) Recommendation: Increase refund auto-approval to $750 for customers with LTV > $10,000 Building value graph with weighted edges... Total nodes: 47 extracted values Total edges: 156 relationships Graph density: 0.142 Validating extracted values against organizational charter... Charter alignment score: 0.89 Flagged items requiring human review: 3 - FAST_TRACK_CONDITIONS may conflict with audit requirements - ANOMALY_MULTIPLIER threshold needs CFO confirmation - SECURITY_APPROVAL_ROLE expansion to include DevOps leads Preparing value package for AI Agent deployment... Serializing value structures to Decision Axis format... Generating boundary conditions and stop triggers... Creating responsibility gates for R2+ decisions... Value extraction complete. Total extracted values: 47 High confidence (>0.9): 18 Medium confidence (0.7-0.9): 24 Low confidence (<0.7): 5 (flagged for review) Ready for deployment to MARIA OS governed agents.
Values are automatically clustered, weighted by confidence, and validated against your organizational charter before deployment.
Value Extraction Output
What MARIA OS surfaces from your organization — before any automation begins
> Organization prioritizes safety and accountability
> Speed is optimized only when risk is assessed as low
> Consistent behavior across Finance and IT universes
> Gap detected: Cost Efficiency is stated but not practiced
> Recommendation: Re-evaluate cost KPIs or acknowledge safety-first trade-off
Observed Values — Strength Distribution
Values extracted from 12,847 decisions across 3 universes — not surveys, not interviews, but actual behavior
Judgment Pattern Mining
Patterns extracted from 12,847 real decisions — not surveys, not interviews
External writes almost always escalate to human approval
Latency spikes are tolerated during month-end close
Recovery succeeds more after evidence enrichment
Finance zone has 2.3x stricter approval than Marketing
These patterns reveal how your organization actually thinks, not how it claims to think.
Three Layers of Organizational Truth
Organizations don't have one kind of value. Some are immutable constraints. Some are strategic priorities. Some are operational metrics. MARIA OS distinguishes them.
These values are never compromised. Safety always wins. Human authority is preserved.
These are optimization targets. Different organizations weight them differently.
These are measured, not optimized. They tell you if the system is healthy.
L0 blocks. L1 guides. L2 monitors.
Stated vs. Practiced
Organizations declare values in mission statements. But decisions reveal different priorities. MARIA OS measures the gap.
The difference is not hypocrisy. It's lack of visibility. Gap detection enables policy calibration, not blame.
What you say matters. What you do matters more.
From Values to Governance
Value Scanning is not a dashboard. It's a compiler. Discovered values are transformed into executable policies that govern agent behavior.
Nothing stays abstract. Values become rules. Rules become enforcement. Enforcement produces evidence.
Values that don't execute are just decoration.
Not Accuracy. Override Reduction.
"Extraction accuracy" is unmeasurable — no ground truth exists for values. We measure operational outcomes.
Fewer overrides means AI decisions align with organizational values. Less audit effort means governance is built-in, not bolted-on.
When values are properly encoded, AI agents make fewer decisions that humans need to override. This is not about AI being smarter. It's about AI being aligned.
Value Scanning is not analysis. It is governance.
Decision Flow
Unbreakable Decision Sequence
Don't start with evaluation. Build unbreakable choices from values.
1/8Input context and action candidates
Decisions with irreversible consequences are filtered out first, regardless of score
Values are evaluated independently. Processed as prioritized constraints, not weighted sums
Even without defined values, infers minimum safety constraints and keeps only unbreakable options
Other AI evaluates and chooses. MARIA OS discards what breaks first.
Collective Value Sphere
Values are never mixed.
Processed in order.
Values used in decisions are structured as layers.
Human life, irreversible loss, legal - No one can override
Industry-specific regulations, safety, audit
CEO values - Challenge, long-term, speed, aesthetics
Project discretion - Cost, effort, ops load
Upper layer values are never optimized away by lower layers.
That's the design of "unbreakable decisions".
From Insight to Governance
Scanner -> MARIA OS
Scan findings flow directly into the decision pipeline — value gaps become governed decisions, workflow anomalies trigger responsibility gates automatically.
Zero manual handoff — scanner insights become executable governance in seconds.
Contact
Value Scanner Inquiry
Share your goal, deadline, constraints, and target systems. We will return scope and execution steps.