AI Decision Accountability

AI is making decisions.
Can you prove it?

Govern, record, and replay critical AI decisions across regulated industries.

See the approach
Financial services
Prove credit, KYC, and fraud decisions
01

A regulator asks why

Credit declines, KYC escalations, and flagged transactions need more than model output.

02

Logs are not enough

System logs show activity. They rarely show the policy basis, review path, and decision rationale.

03

Evidence arrives too late

Manual reconstruction starts after the inquiry, when the decision context is already scattered.

04

Proof must be built in

Foundry Arbiter is designed to govern the decision and produce the record as work happens.

Built for critical industries

For teams that must explain decisions after the moment has passed.

Novaquant builds decision accountability for enterprise AI. Foundry Arbiter, our first product, delivers it for regulated and high-consequence work: credit, onboarding, clinical review, operations, defense, and mission workflows.

Decision environments

The common thread is not the industry label. It is the need to preserve context when software, policy, and human judgment meet.

01

Banking

Credit, onboarding, fraud, and compliance review paths.

02

Healthcare

AI-assisted review with protocol and escalation needs.

03

Manufacturing

Automated operations that need traceable actions.

04

Energy

Critical workflows where operational context matters.

05

Defense

Controlled environments with demanding accountability needs.

06

Space

High-consequence systems where decisions must remain reviewable.

Evidence by design

A defensible decision has a chain of custody.

The point is not to collect more logs. The point is to keep the business decision explainable after the model output has moved downstream.

  1. 01

    The decision has a source.

    Who asked, which system acted, and what context shaped the request.

  2. 02

    The decision has a control point.

    Which policy applied, when review was required, and who approved or stopped it.

  3. 03

    The decision has a record.

    A reviewable trail remains when audit, risk, or a regulator asks for proof.

The platform

Understand. Govern. Prove. Replay.

Foundry Arbiter sits between enterprise workflows and AI models. It applies policy, routes review, and writes the decision record without replacing the systems already in place.

01

Understand

Map a credit decline, KYC escalation, or flagged transaction from request to outcome in one reviewable timeline.

02

Govern

Route high-risk actions through policy checks and human review before they complete.

03

Prove

Attach a decision record that risk, audit, and compliance teams can review without rebuilding context from logs.

04

Replay

Replay what happened from initial request through action and outcome when a reviewer asks why.

NQDS™

The flight data recorder for AI decisions.

NQDS is Novaquant’s decision-record format. It preserves the inputs, policy checks, approvals, actions, and outcomes behind a governed AI decision.

Record model

Defines the context a governed AI decision should preserve.

Product layer

Connects policy, workflow, review, model interaction, and outcome.

Review artifact

Gives risk and audit teams a consistent reference for reconstruction.

Deployment architecture
Operate where your controls require.

Cloud

Managed deployment pattern

Private Cloud

Customer-controlled environment

On-Premises

Inside enterprise infrastructure

Air-Gapped

Target pattern for isolated systems

Security requirements
Identity, encryption, access control, and evidence ownership.
API-first integration
Designed to connect with existing systems and model providers.
Designed for enterprise control

Built around the requirements regulated teams cannot ignore.

Foundry Arbiter is designed around deployment control, data minimization, identity, reviewability, and model independence. Product-stage details stay in the Trust Center.

Customer-controlled deployment

Deployment is designed to stay under customer control: VPC, private cloud, or on-premises where required.

Data minimization

Arbiter minimizes retained customer data and keeps operational evidence under customer control by design.

Identity and access

Role-based access and enterprise identity integration shape the access model from the start.

Encryption

Encryption in transit, encryption at rest, and key-management options are treated as baseline requirements.

Review Security ApproachReview the Trust Center
Why Novaquant

Accountability belongs in the decision path.

We build for the operating teams who have to answer the hard question later: what happened, why did it happen, and who controlled it?

01

The decision is the unit of trust.

We focus on the moment where AI changes an outcome, not only the model that suggested it.

02

Human oversight

Define where people review, approve, redirect, or stop consequential actions.

03

Model-independent governance

Keep policy and review consistent as models, vendors, and providers change.

04

Built with regulated operators.

Partners bring the real workflows, exceptions, and review paths that shape the product.

Explore the problem

See why decision evidence is becoming an operating requirement.

Regulatory evidence

AI Compliance Fine Tracker

Review documented enforcement actions and the governance failures behind them.

Explore Fine Tracker
Interactive scenario

AI Decision Risk Simulator

Walk through a banking scenario and compare an ungoverned workflow with a structured decision record.

Open Risk Simulator
Talk with Novaquant

Trust starts with a decision you can reconstruct.

We are working with a small group of regulated teams to shape Foundry Arbiter before launch. Bring us the decision workflow you cannot afford to explain late.

Partner requests use the same secure inquiry flow and are reviewed by the Novaquant team.