RAI-1
Experimental
Offline research and early feasibility work with no operational decision authority.
ApprovalTechnical lead
Responsible AI in practice
AI controls that follow the system from idea to retirement.
TopsyBee applies a risk-based Responsible AI framework to every AI development project.
UniXAI adds technical assurance for image-processing models by making model behavior, test evidence, and review decisions easier to inspect.
Our operating framework is aligned to
NIST AI RMF
ISO/IEC 42001
ISO/IEC 42005
DoD RAI overlay when applicable
01 — Our framework
Risk determines the strength of the controls.
Every project receives an RAI classification.
Higher-consequence systems receive deeper testing, independent review, stronger human controls, and executive approval.
RAI-2
Limited impact
Bounded internal or customer-supporting use with limited consequence if an output is wrong.
ApprovalProject lead
RAI-3
Operational
AI that materially informs an operator, customer, or business decision.
ApprovalIndependent RAI review
RAI-4
Safety or mission critical
AI that can affect physical safety, autonomy, mission outcomes, or critical operations.
ApprovalIndependent assurance and executive release
Human accountability
A named owner remains responsible for the system, its approved boundaries, and its release.
Purpose and traceability
Intended use, prohibited use, data, code, model, tests, deployment, and changes remain linked.
Safety and resilience
We design for uncertainty, misuse, degraded inputs, failure behavior, recovery, and human override.
Measured performance
Claims are tied to testable thresholds, defined operating conditions, and retained evidence.
Lifecycle controls
Eight gates turn Responsible AI into a release process.
Use-case intake
Define the purpose, user, affected decision, operating environment, and why AI is appropriate.
Risk and impact
Assign an RAI level and identify affected people, operations, assets, and environments.
Data approval
Record source, rights, provenance, labeling, quality, restrictions, and known gaps.
System design
Set measurable requirements, human authority, safety boundaries, fallback behavior, and prohibited uses.
Controlled development
Link code, data, configuration, training runs, dependencies, and model artifacts.
TEVV
Test performance, robustness, security, human factors, failure behavior, and recovery under defined conditions.
Release approval
Review the evidence package and approve, restrict, return for rework, or reject the release.
Monitor and change
Track drift, failures, overrides, incidents, and changes through rollback, retraining, or retirement.
02 — Our proprietary toolkit
UniXAI turns image-model behavior into reviewable evidence.
UniXAI is TopsyBee's Responsible AI toolkit for image-processing systems.
We use it within the lifecycle to support explainability, error analysis, model review, and assurance reporting.
Establish the evidence baseline
UniXAI links the evaluated image set, reference labels, model version, configuration, and approved use case before analysis begins.
Inspect model attention
The toolkit produces method-appropriate visual explanations that help reviewers see which image regions influenced an output.
Diagnose errors and edge cases
Reviewers examine false positives, false negatives, confidence behavior, degraded inputs, and representative operating conditions.
Apply access-aware methods
Gradient-based techniques are used only when model internals are verified and available.
Frozen or external models use black-box methods.
Package reviewable results
Outputs are retained with assumptions, limitations, test conditions, findings, reviewer decisions, and links to corrective actions.
What UniXAI supports
- Visual explanation of model attention
- Confidence and error-pattern analysis
- Evaluation of representative and degraded images
- Traceable findings for technical and independent review
- Evidence for TEVV and the AI Assurance Package
Assurance boundaries
- Saliency is not proof that a model is correct or safe
- Explainability does not by itself establish fairness or causality
- Diagnostic findings require reference data and expert review
- UniXAI complements cybersecurity, safety, and operational testing
- Release authority remains with accountable human reviewers
03 — Evidence at release
Each significant AI release carries an assurance package.
The package shows what the system is intended to do, how it was built and tested, where its limits are, who approved it, and how it will be monitored.
Evidence completeIndependent reviewRelease decision
01AI System Card
02Risk and Impact Assessment
03Dataset and Model Cards
04Data Provenance Record
05Requirements Traceability Matrix
06TEVV Plan and Results
07Safety and Cybersecurity Assessments
08Human Oversight Plan
09Deployment and Monitoring Plan
10Rollback, Incident, and Retirement Records
11Software and AI Bills of Materials
12Responsible AI Approval Record
Responsible release
Evidence informs the decision.
People retain the authority.
Approved
Approved with restrictions
Rework required
Rejected
Work with TopsyBee