AI Decision Observability

Your AI decided.
Can you defend
the decision?

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

Decision accountability infrastructure for regulated AI in healthcare, financial services, and insurance.

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-9c2f4a81
Denied
Confidence: 71%
Policy: MCG-CARD-2026 v3.1 · Cardiac Imaging Coverage Policy
Clinical Necessity Documentation Pass
Prior Conservative Treatment Fail
No documentation of 30-day conservative treatment trial as required by policy
Coverage Criteria Match Fail
Submitted CPT code 75574 not aligned with documented clinical indication
Provider Network Status Pass
Pattern Intelligence
High Risk
Illustrative example
Decisions missing prior conservative treatment documentation were 3.2× more likely to result in a bad outcome. This pattern is worsening.
3.2×
Higher Risk
Tested
Stat Validated
2,165
Decisions
⚠
HITL Review Analytics · Example signal
Investigative prompt: Across this reviewer cohort, median review time fell from 3m 58s → 47s over 90 days while case complexity held steady. Override rate fell from 38% → 3%. Flagged for investigation, not a conduct finding.
Decision Attribution
Pattern Intelligence
Outcome Attribution
HITL Review Analytics
2,000+ Death by AI Legal Claims Projected · End of 2026
Policy Versioning
Temporal Accountability
EU AI Act High-Risk Obligations · December 2027
Evidence Gap Analysis
Tamper-Detectable Audit Trail
Decision Attribution
Pattern Intelligence
Outcome Attribution
HITL Review Analytics
2,000+ Death by AI Legal Claims Projected · End of 2026
Policy Versioning
Temporal Accountability
EU AI Act High-Risk Obligations · December 2027
Evidence Gap Analysis
Tamper-Detectable Audit Trail
CMS WISeR Prior Auth Requirements
Treasury AI Risk Management Framework
EU AI Act High-Risk Decision Obligations
Regulatory Timeline
US state law is moving first. Washington and Maryland already require a licensed human reviewer to own AI-assisted adverse determinations, with Maryland reporting quarterly whether AI was involved. The NAIC AI Systems Evaluation Tool entered market conduct examinations in 2026. EU AI Act high-risk obligations follow in December 2027 for standalone systems.
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 Attribution
02 / 04
"We have a 97% approval rate. That means it's working, right?"
High approval rates can mask silent quality degradation. If review time across a cohort has fallen from four minutes to forty-five seconds while the cases have not gotten easier, the safety net has a hole in it.
→ HITL Review Analytics
03 / 04
"Which evidence gaps actually cause bad outcomes?"
Missing a receipt is annoying. Missing a required evidence field can correlate with 3× 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.

01 / 04
📐
Capture the Rules
Upload your policies, business rules, or compliance requirements. Praxius versions them with effective dates so every decision is linked to the rules that were active at the time. Policies are client-owned. Praxius stores and references them; it does not interpret or maintain regulatory content.
policy: "MCG-CARD-2026"
version: "3.1"
effective: 2026-01-15
gates: [clinical_necessity,
  prior_treatment, coverage...]
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: 0.71,
  gates: [...],
  gaps: ["prior_treatment_docs"]
})
First decision trace in < 1 hour
03 / 04
🔬
See What's Going Wrong
Praxius connects evidence gaps and reviewer behavior to recorded outcomes and runs the statistics. Which gaps are associated with bad outcomes. Where review time is collapsing across comparable case complexity. Whether a policy change moved the numbers. Tested, with sample size and effect size shown.
finding: "prior_treatment_docs"
risk_level: HIGH
bad_outcome_rate: 3.2x
trend: worsening
sample_n: 2165
Findings include effect size and sample size. Thin samples are withheld rather than overstated. Methodology available for technical review.
04 / 04
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Tamper-Detectable Audit Record
Every decision is sealed into an audit record the moment it arrives. Records are protected against quiet modification, and auditors can independently verify that what they are looking at matches what was originally captured. Technical verification details are available under NDA for compliance and security review.
sealed_at: 2026-03-14T09:12Z
write_protected: true
independently_verifiable: true
audit_ready: true
Built for the question auditors actually ask: can you prove this record has not been altered since the decision was made?
How Praxius Handles Your Data
Storage and custody
Decision records are org-scoped in Praxius. Praxius does not require access to your underlying models, training data, or source systems of record. The instrumentation layer sits at the decision output, not inside the model.
Audit record integrity
Decision records are sealed at capture and protected against quiet modification. Auditors can independently verify a record without relying on a dashboard screenshot. Architecture and verification materials are available under NDA for compliance and security review.
Portability
Your decision records are exportable in standard formats at any time. You own the audit trail.

In our validation environment, running a simulated policy change scenario, Praxius surfaced a policy change that increased evidence gaps from 0% to 46% of cases while reviewer decision times fell 74% over the same period. The finding was statistically tested and exportable as an audit record. This was a synthetic scenario in a controlled environment, not a production deployment.

We have pressure-tested 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.

Healthcare · Utilization Management
Prior Authorization Risk Intelligence
AI prior auth agents deny thousands of claims daily. Praxius shows which denials are linked to specific missing evidence fields and how those gaps associate with later outcomes, with a policy-versioned trail suitable for human-review oversight.
✓ Gap-to-outcome attribution · Policy-versioned audit trail · Human-review evidence for WISeR-style oversight
Financial Services · Credit
Lending Decision Accountability
AI credit agents approve or deny thousands of applications daily. Praxius shows which denials are linked to specific missing evidence fields, which gates failed under the policy version in effect, and how those gaps associate with later outcomes.
✓ Gap-to-outcome attribution · Policy-versioned audit trail · Adverse-action ready records
Insurance · Underwriting
Underwriting Decision Quality
AI underwriting decisions get challenged, but the decision patterns that predict bad outcomes go undetected for months. Praxius records the confidence value attached to each decision alongside the outcome that followed, so you can test whether reported confidence tracks reality instead of assuming it does.
✓ Decision pattern detection · Temporal policy analysis · Outcome attribution
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
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 statistically tested attribution: which evidence gaps are associated with bad outcomes, where review behavior is flattening across cases of comparable complexity, and whether a policy change moved the numbers.
Next
Surface the pattern while the case is still open
With enough decision history, the reviewer could be shown what the record already contains: decisions with this evidence profile have historically gone sideways. Praxius stays out of the execution path and never issues the determination. It surfaces what the evidence shows and leaves the call to the person accountable for it.
Eventually
Follow risk across multi-step workflows
As agentic systems hand work from one step to the next, the hard question becomes where risk entered the chain. The long-term direction is clearer lineage across those handoffs, so a bad outcome downstream can be traced to the decision that created it.
"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 Technology Trends. Gartner does not endorse any vendor, product or service.
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 in early conversations with a small number of regulated organizations working through the 2026 and 2027 accountability requirements. If your AI is making decisions that matter and you can't fully explain them, let's talk.

No commitment required. No long sales process. We'll tell you plainly whether Praxius is a fit.

If you found us through The Compliance Gap series and want to see what answering the three questions actually looks like, that's a good place to start.

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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