Deterministic, auditable decisions for regulated industries
Policies define how regulated institutions should operate. Yet critical decisions still depend on human judgment as probabilistic AI systems cannot guarantee consistent, replayable outcomes.
closes the execution gap between written governance and operational decisions — so every outcome is traceable to the rule that produced it.
Every quarter, your compliance officer signs off on a book of decisions. Every quarter, your risk committee accepts a tolerance that assumes those decisions followed policy. Every few years, an examiner or a claimant tests that assumption.
When the test comes, most institutions discover that the rule wasn't written down the way it was actually applied — and the decisions can't be reconstructed. Juris closes that gap before it's tested.
The governance gap in numbers
$6.4B
In regulatory fines across US banking and insurance (2024-2025). Root cause consistently cited: inadequate decision rationale
15-20%
Variance in underwriter claim acceptance rates from stated policy
$500-$2K
Per manual case review for audit justification
2-3%
Acceptance rate drift creating $200-300M in unintended exposure
Industry statistics from regulatory filings and compliance reports. Not Juris claims.
What fails is not intent or effort, but the ability to determine — under the organization's own rules — which decisions were permitted and which were not.
The Journey
How decision logic disappears
Critical decisions still rely on human judgment
That judgment produces repeatable decision logic
Over time, the logic drifts, fragments, and becomes implicit
When experts leave, the logic disappears with them
Organizations are left with outcomes—not reasoning
When an examiner, an auditor, or a court asks how the decision was made, the organization produces a narrative — not evidence
gives you evidence of governance, not reconstruction. Every decision. Every time.
The Cost of the Gap
What policy-control drift costs in practice
Every institution that has faced a regulatory action in the last five years had a governance framework. The fine was not for lacking policy. It was for the gap between that policy and what operational controls actually executed — every day, across every case, for years.
Regulatory fines
$3B
TD Bank AML settlement — the largest in modern banking history
Wells Fargo: $2.5B in remediation, a $1.5B penalty, and 10,000 additional compliance employees hired as a direct consequence. Bank of America: cease-and-desist and mandatory lookback in December 2024. In every case, the policy existed. The controls drifted from it.
The consent order is not the cost. The remediation that follows is.
Read the analysisCompliance overhead
$274B
Global AML compliance spend annually
Institutions compensate for governance uncertainty with people: reviewers, monitoring teams, control testers, evidence gatherers. Compliance organizations grow not because governance is stronger — but because execution remains inferential and someone must manually verify what the system should have proven automatically.
More headcount does not close the execution gap. It manages the consequences of it.
Read the analysisAttention and time
Months
To answer: "did our controls reflect current policy last year?"
Every lookback review, every regulatory examination, every internal audit forces the institution to reconstruct what it should already know. A policy change that takes one committee meeting to approve takes months to propagate, verify, and evidence. Senior compliance and risk leaders spend their cycles on reconstruction — not governance strategy.
The institution's best people are explaining decisions, not improving them.
Read the analysisWhat you get instead
Walk into any exam with evidence, not reconstruction
When the regulator asks why a control fired — or why it didn't — you do not reconstruct. You replay. Every decision carries a full trace to the policy that governed it, the version active at the time, and the reasoning that produced the outcome.
Prove continuous alignment — not just at the next audit, but for any date in the record
Simulate a policy change before it reaches operations — know which cases would be affected
Free compliance and audit teams from evidence reconstruction — redirect them to governance strategy
Stop adding headcount to compensate for a gap that can be closed structurally
The Problem
Why conventional AI tools cannot solve this
Organizations have tried to preserve decision logic before. The tools available either require massive historical data, produce outputs no one can explain, or cannot represent the governed constraints that make institutional decisions defensible.
Statistical models
Require large datasets to train. Produce probability scores, not governed decisions. Cannot explain why a specific outcome occurred.
Legacy rule engines
Require manual rule specification. Drift from policy without detection. Cannot reconstruct the logic behind historical decisions.
Generative AI
Produces variable outputs. Cannot guarantee consistency. Not auditable. Unsuitable for decisions that must be explained under scrutiny.
gives you the layer that statistical AI, rules engines, and LLMs cannot: decisions derived from your policy, with a full evidence trace showing exactly why the outcome occurred.
What Juris is not
Not a GRC platform
GRC manages the process of compliance. Juris proves that individual decisions followed the rules.
Not a rules engine
Rules engines execute rules you already wrote. Juris turns policy into the rules — and keeps them versioned against the source.
Not a monitoring tool
Monitoring flags violations after they happen. Juris certifies decisions before they leave the desk.
Not an LLM
LLMs produce answers that vary. Juris produces the same decision for the same facts — every time, with an audit trail.
Regulatory Landscape
Regulatory timelines are accelerating
The window to build governance infrastructure before the next examination cycle is narrowing. Organizations that wait will be retrofitting under pressure.
Banking
Basel IV
January 2028
Enhanced credit governance documentation requirements. Banks must demonstrate decision logic traceability for credit risk models.
Insurance
Solvency II
Amendments 2026
Proof that underwriting and claims decisions follow stated policy. Regulators expect auditable evidence, not documentation.
Securities
SEC Enforcement
Record momentum
200+ enforcement actions in Q1 FY2025, $63M in penalties. Governance gaps are the primary vector for enforcement risk.
Cross-Industry
EU AI Act
Governance required
No existing framework proves AI-assisted decisions comply with policy. Organizations using AI in regulated decisions face a certification gap.
For the Chief Compliance Officer preparing for the next exam.
For the Chief Risk Officer whose portfolio is leaking through control drift.
For the General Counsel who will defend the decision in discovery.
For the Chief Audit Executive assuring the audit committee that the controls worked.
Industries
Governs Decision-Critical Industries
Pharma & Life Sciences
Certifies logic for clinical, regulatory, and portfolio decisions.
Learn more
Insurance
Establishes auditable underwriting, pricing, and claims logic.
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Banking & Financial
Credit decision traceability and model governance.
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Venture Capital
Formalizes IC logic: explicit, consistent, and defensible.
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Public Sector
Mandate-traceable logic for procurement and eligibility.
Learn moreKey Questions
What buyers ask first
Juris is a Certified Decision Governance platform. It transforms your written policies into executable, versioned rules and certifies every decision against them. It is AI in the sense that it automates rule extraction and verification — but it is not a generative model, not probabilistic, and not opaque. Same inputs always produce the same outputs, with an audit trail.
Rules engines execute rules you already wrote. GRC platforms manage compliance processes. LLMs generate variable text. Juris is the layer none of them is: it builds one canonical, versioned model of your decision logic and operates over it.
Against that same model, Juris evaluates each case with a full reasoning trace, explains every outcome back to the exact paragraph of the source policy it came from, and certifies decisions where the evidence is complete — or refuses, with reasons, where it isn't. It verifies the baseline itself for contradictions and gaps before any case is run. It compares two versions of a policy to show what changed and which decisions would have flipped. It simulates counterfactuals — what if this fact were different, what if last quarter's cases were decided under next month's rules. It tells an examiner exactly what evidence is still missing to reach a verdict. And where only historical decisions exist and the policy is silent or outdated, it can infer a candidate baseline from those decisions for human review.
It does not replace your decision processes. It makes them visible, reproducible, and defensible.
Juris refuses to certify — and tells the examiner exactly what was missing. This is a first-class design principle, not a failure mode. A system that always produces an answer is a system that sometimes produces a wrong answer. In regulated environments, a clear refusal with specified evidence gaps is more valuable than a confident approximation.
How It Works
From Policy to Certified Decisions
You provide your written policy — a procedure manual, investment mandate, underwriting rulebook, or the operational document that governs your decisions.
Juris extracts the decision rules in plain language, each tied to the exact paragraph it came from. Your experts approve every rule before it enters the constitution.
Juris codifies the approved rules into a sealed, versioned constitution — tamper-evident, with full provenance preserved.
You submit cases; Juris evaluates each against the constitution, with a full reasoning trace for every decision.
Juris certifies — or refuses with reasons — every outcome carries a signed, reproducible audit trail; cases with evidence gaps return an exact list of what's missing.
Decisions will be scrutinized. The reasoning should already exist.
See how certifies decision logic for organizations where accountability is inevitable.