AI Execution Governance

Your AI decided.
Can you prove
it was right?

Every AI decision is becoming as accountable as a financial transaction. Most organizations aren't ready for that. Praxius is.

When regulators, auditors, or your board ask why your AI denied 40,000 claims last quarter, "the model said so" isn't an answer. They need the rule that was active, the evidence that existed at the time, and proof that a human actually reviewed it rather than clicked through it. In regulated environments, a content error is not a bug report, it is a regulatory event.

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Drop-in SDK, any agent framework
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No model access required
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Works with rules, AI, and humans
Decision Trace
trace-a4f8b291
Denied
Confidence: 64%
Policy: MGP-EXP-2025 v2.2 · Meridian Expense Policy
Receipt Compliance Pass
Business Purpose Adequacy Fail
Purpose too vague: "team lunch" -- no business context provided
Category Limit Compliance Fail
$284 exceeds $75 meal limit for standard market
Pre-Approval Compliance Pass
Pattern Intelligence
High Risk
Decisions missing business purpose documentation were 3.2× more likely to result in a bad outcome. This pattern is worsening.
3.2×
Higher Risk
Verified
Defensible
2,165
Decisions
⚠
HITL Complacency Signal: Average reviewer time dropped from 3m 58s → 47s over 90 days. Override rate collapsed from 38% → 3%. Reviewers may be rubber-stamping.
Decision Forensics
Pattern Intelligence
Outcome Attribution
HITL Complacency Detection
2,000+ Death by AI Legal Claims Projected · End of 2026
Policy Versioning
Temporal Accountability
EU AI Act Enforcement · August 2026
Evidence Gap Analysis
Regulatory Compliance
Decision Forensics
Pattern Intelligence
Outcome Attribution
HITL Complacency Detection
2,000+ Death by AI Legal Claims Projected · End of 2026
Policy Versioning
Temporal Accountability
EU AI Act Enforcement · August 2026
Evidence Gap Analysis
Regulatory Compliance
CMS WISeR Prior Auth Requirements
Treasury AI Risk Management Framework
EU AI Act High-Risk Decision Obligations
Deadline
EU AI Act high-risk system requirements take effect August 2026. Organizations deploying AI in regulated decision workflows need defensible accountability infrastructure in place before then.
Who This Is For
Chief Risk Officer
Your AI is making decisions that carry regulatory and reputational weight. You need proof that the evidence was adequate, the policy was followed, and a human actually reviewed it. Not a dashboard. Proof.
Chief Compliance Officer
When auditors ask why 40,000 claims were denied last quarter, you need a defensible record tied to the rule that was active at the time of each decision. Praxius builds that record automatically.
Head of AI / Technology
Your agentic workflows are making decisions you can't fully trace. When an outcome goes wrong, you need to know which step failed, what evidence was present, and whether a human actually reviewed it or just clicked through it. As AI gains execution authority, you become accountable for what the system actually does in production, not just what it was designed to do. Praxius instruments the full decision chain without touching the underlying model.
The Problem

AI decisions
are a black box.

Your AI makes thousands of decisions daily: approvals, denials, escalations. You can see the outcomes. You can't see why, whether the evidence was adequate, or whether the pattern is getting worse.

01 / 04
"Our denial rate is up 15%. Is the AI wrong, or did the policy change?"
Without connecting the policy rules, the decision evidence, and the downstream outcome, you can't distinguish between a working system and a broken one. You're flying blind.
→ Decision Forensics
02 / 04
"We have a 97% approval rate. That means it's working, right?"
High approval rates can mask silent quality degradation. If your human reviewers are rubber-stamping AI decisions in 45 seconds instead of 4 minutes, your safety net has a hole in it.
→ HITL Complacency Detection
03 / 04
"Which evidence gaps actually cause bad outcomes?"
Missing a receipt is annoying. Missing clinical documentation correlates with 3.2× more adverse outcomes. Without statistical proof, every gap looks the same. You can't prioritize what matters.
→ Pattern Intelligence
04 / 04
"We updated the policy in March. Did things get better or worse?"
Policy changes ripple through thousands of decisions. Without before-and-after statistical comparison tied to specific policy versions, you'll never know whether the change helped or hurt.
→ Temporal Accountability
△
Every monitoring tool captures the decision. None of them close the loop.
Observability platforms tell you what your AI did. They never ask: did that decision produce a good outcome 30 days later? Without closing that loop, you're flying blind with better instrumentation. And it's not just AI. Decisions flow through rules engines, models, and human approvers, often all three in sequence. If you can only see one link, you can't find the failure. Praxius sees the full chain.
Praxius doesn't replace your observability tools, your model monitoring, or your compliance platform. It connects them at the decision layer. As execution authority shifts from humans to AI agents, control of the decision layer becomes the control plane for enterprise risk.
How It Works

Four sides of
every AI decision.

Praxius captures the rules the AI was following, what it decided and why, and what happened as a result. Then it runs the math to show you where things are breaking. No other platform does this.

01 / 04
📐
Capture the Rules
Upload your policies, business rules, or compliance requirements. Praxius versions them with effective dates so every decision is evaluated against the rules that were active at the time.
policy: "MGP-EXP-2025"
version: "2.2"
effective: 2026-01-31
gates: [receipt, purpose,
  limits, pre_approval...]
02 / 04
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Record Every Decision
Add a few lines to your agent's post-processing step. Praxius captures the outcome, confidence, gate evaluations, evidence gaps, and reasoning for every decision. Rules-based, AI, or hybrid.
praxius.recordDecision({
  outcome: "DENIED",
  confidence: 0.64,
  gates: [...],
  gaps: ["business_purpose"]
})
First decision trace in < 1 hour
03 / 04
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See What's Going Wrong
Praxius connects evidence gaps and reviewer behavior to real outcomes. Not directional signals -- defensible findings. Which gaps actually cause bad outcomes. Which reviewers are rubber-stamping. Which policy changes made things worse. Proven, not assumed.
finding: "business_purpose"
risk_level: HIGH
bad_outcome_rate: 3.2x
trend: worsening
defensible: true
Findings are statistically validated and defensible under regulatory scrutiny. Full methodology available for technical review.
04 / 04
🔒
Immutable Audit Record
Every decision record is cryptographically sealed at capture. What Praxius recorded cannot be altered retroactively by the model, by an agent, or by anyone in the stack. Your audit trail is forensic-grade by construction, not by policy.
record_hash: "sha256:a3f9..."
sealed_at: 2026-03-14T09:12Z
tamper_evident: true
audit_ready: true
Deterministic evidence verification prevents LLM hallucination from corrupting audit records. Built for environments where what the AI decided is a matter of legal record.
How Praxius Handles Your Data
Storage and custody
Decision records are stored in your designated environment. Praxius does not require access to your underlying models, training data, or patient records. The instrumentation layer sits at the decision output, not inside the model.
Immutability and access
Records are cryptographically sealed at capture. Access controls are configured per your organization's requirements. No Praxius employee can alter a sealed decision record after the fact.
Retention and portability
Retention policies are set by your organization, not by Praxius. Your decision records are exportable in standard formats at any time. You own the audit trail.

In our reference deployment, Praxius detected that a policy change increased receipt-related evidence gaps from 0% to 46% of cases, while reviewer decision times simultaneously decreased 74%. No other tool in the stack saw it. The finding was statistically validated and audit-ready.

We have validated the core thesis through direct conversations with CROs and compliance leaders at major financial services and healthcare organizations, confirming decision-level AI risk as an unaddressed liability gap in regulated enterprise.
Where Praxius Fits

Built for decisions where
getting it wrong has consequences.

Enterprise Operations
Expense & Procurement AI Monitoring
Automated expense and procurement workflows blend rules, AI judgment, and human approvers. Praxius monitors the full chain: policy gaps, reviewer inconsistency, and cost leakage across thousands of decisions.
✓ Cross-method monitoring · Reviewer comparison · Cost impact quantification
Financial Services
Suitability & Recommendation Oversight
AI recommendation engines must demonstrate every suggestion had a policy-compliant evidence basis. Praxius tracks which suitability checks are being missed and whether those gaps correlate with compliance exceptions.
✓ Per-decision evidence trail · Treasury AI framework alignment · Pattern detection
Insurance · Underwriting
Underwriting Decision Quality
AI underwriting decisions get challenged, but the decision patterns that predict bad outcomes go undetected for months. Praxius detects when confidence scores rise without outcome improvement -- the signature of an agentic workflow that thinks it's getting better while it isn't.
✓ Decision pattern detection · Temporal policy analysis · Outcome attribution
Healthcare · Utilization Management
Prior Authorization Risk Intelligence
AI prior auth agents deny thousands of claims daily. Praxius shows which denials are linked to missing clinical documentation and which ones get overturned on appeal at 3× the normal rate.
✓ Gap-to-outcome attribution · Appeal pattern detection · CMS WISeR compliance
Where This Is Going

The decision audit is
becoming inevitable.

Today
See what went wrong, with proof
Praxius captures every decision, links it to the policy in effect and the outcome that followed, and runs the statistics. You get forensic-grade attribution: which evidence gaps actually cause bad outcomes, which reviewers are rubber-stamping, which policy changes made things worse.
Next
Flag risk before the decision is finalized
With enough decision history, Praxius can score risk in real time. Before an agent finalizes a denial or an approver clicks "accept," Praxius surfaces the pattern: decisions with this evidence profile have historically gone sideways. Escalate, don't rubber-stamp.
Eventually
Map entire decision chains end to end
When the triage agent's output feeds the authorization agent, and a denial surfaces three steps downstream as an overturned appeal, you need to trace the full chain. Praxius maps decision supply chains across agents, teams, and systems so when something breaks, you know exactly where the risk originated.
"Death by AI" legal claims will exceed 2,000 worldwide by end of 2026 due to insufficient AI risk guardrails. AI decisions in healthcare, financial services, and insurance represent billions in annual regulatory exposure.
Gartner Top Strategic Predictions for 2026 and Beyond
That accountability is coming whether the tooling is ready or not. The infrastructure doesn't exist yet. Praxius is building it.
Get in Touch

Start a
conversation.

We're working with a small number of regulated organizations ahead of the August 2026 EU AI Act deadline. If your AI is making decisions that matter and you can't fully explain them, let's talk.

No demo required. No commitment implied. We'll tell you plainly whether Praxius is a fit.

We'll review your note and respond within one business day.

We respect your data. No sales outreach without a clear fit.

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