Sets the standards
Approved models, identity, data classification, residency, access controls, observability, risk policy, security, and vendor requirements.
Service AI Governance
Govern the decision. Control the autonomy. Scale Service AI with confidence.
Circuitry.ai gives CIOs, IT leaders, and AI governance teams a governed decision layer for service, parts, warranty, and support—designed to work with enterprise-approved models, data, identity, security, and observability while keeping people in control of consequential decisions.
ENTERPRISE AI CONTROL PLANE
Identity & accessData governanceModel strategySecurity policy
CONTEXT · POLICY · EVIDENCE · CONFIDENCE
Service Decision Intelligence
RULES · AI/ML · HUMAN REVIEW · AUDIT
AdvisorsRecommend
AnalystsScore & predict
AgentsAct within policy
Every decision connected to evidence, action, human override, and outcome
SOC 2 Type IIIndependent assurance
ISO/IEC 27001:2022Certified information security management
GDPR readinessPrivacy processes and contractual controls
NIST AI RMF alignmentGovern · Map · Measure · Manage
Current evidence & documentation ↗
A practical governance problem
Enterprise AI governance must extend from models to operational decisions.
Foundation-model standards, approved cloud platforms, data controls, and responsible-AI policies are essential. But service AI also makes domain decisions with operational and financial consequences: what failed, which part is right, whether a claim is covered, what action should happen next, and when a person must intervene.
Operationalizes the standards
Applies those controls to each service decision using domain knowledge, business rules, evidence, confidence, human oversight, and outcome measurement.
Circuitry.ai is the domain decision layer—not a replacement for your enterprise AI platform, foundation-model strategy, data fabric, or systems of record.
Service AI governance framework
Six control areas for trusted Service AI
Governance is applied to the full decision lifecycle—not only to the model call.
Data governance
Use approved sources, scoped access, customer data segregation, retention requirements, data quality checks, and policy-controlled context for each decision.
Model governance
Use enterprise-approved models through a model abstraction or gateway, with version awareness, evaluations, task-specific selection, and controlled change.
Decision governance
Combine deterministic rules, decision criteria, AI/ML, confidence thresholds, exception logic, and explicit policy precedence.
Human oversight
Define which decisions are advisory, which require approval, and which can execute automatically. Route uncertainty and higher-risk cases to people.
Security & access
Apply enterprise identity, role-based permissions, tenant isolation, secure integrations, encryption, and controlled tool or system access.
Observability & audit
Record the decision context, evidence, recommendation, confidence, action, human override, exceptions, and downstream outcome for review and improvement.
NIST AI RMF alignment
Map Service AI governance to the operating model your risk teams already understand.
Circuitry.ai is aligning its Service AI governance practices with the NIST AI Risk Management Framework. The practical objective is to make governance visible in the deployment lifecycle: who owns the use case, what data and systems it can access, how performance is measured, and how risk is managed as autonomy increases.
Review current governance documentation ↗GOVERNOwnership · policy · roles · approvals · accountability
MAPUse case · impact · data · system boundaries · stakeholders
MEASUREAccuracy · confidence · exceptions · outcomes · evaluations
MANAGEThresholds · human review · escalation · monitoring · change control
Governed by design
Control every decision from context to outcome.
A Service AI decision should be reproducible, reviewable, and connected to the business result it created.
1Assemble contextAsset, customer, history, policy, knowledge, evidence
2Validate inputsRequired data, conflicts, completeness, permissions
3EvaluateRules, decision models, AI/ML, scoring, confidence
4Apply controlsThresholds, policy limits, approval and exception logic
5Recommend or actHuman-guided decision or approved system action
6Measure outcomeResult, override, quality, cost, cycle time, learning
Governed autonomy
Autonomy is earned through evidence.
Start with the level of human oversight appropriate to the use case. Increase autonomy only when agreed accuracy, risk, and outcome thresholds are consistently met.
L0Inform
AI retrieves and summarizes approved context. A person makes the decision.
L1Advise
AI recommends a decision and explains the evidence. A person decides.
L2Approve
AI prepares the decision or action; a person approves before execution.
L3Guarded automation
AI acts inside defined thresholds and routes exceptions to people.
L4Autonomous
AI completes the approved decision journey with continuous monitoring and exception controls.
The appropriate autonomy level is configured by use case. Consequential decisions can remain human-reviewed while lower-risk steps are automated.
Enterprise architecture fit
Compose a domain decision layer into the AI architecture you already govern.
Use Circuitry.ai where specialized service reasoning and decision orchestration are required, while the enterprise retains control of foundation models, identity, data, security, and systems of record.
Not a foundation modelWorks with enterprise-approved model strategy.
Not a new system of recordReads from and writes to existing operational systems through governed integrations.
Not a generic agent frameworkProvides service-specific decision models and AI Workers with defined controls and outcomes.
Enterprise control planeIdentity · security · policy · observability · model gateway
Foundation modelsEnterprise-approved language, multimodal, embedding, and predictive models
Enterprise data fabricCustomer · asset · warranty · service history · parts · telemetry
Circuitry.ai domain intelligenceService decision models · Service Knowledge Graph · rules · orchestration · AI Workers
Systems of recordERP · CRM · FSM · warranty · DMS · dealer and service applications
ChannelsTechnician · adjuster · dealer · support · web · email · voice
Governance control matrix
A clear set of designs and controls for AI governance.
| Governance requirement | Circuitry.ai design response | Enterprise control |
|---|---|---|
| Foundation-model strategy | Model-agnostic design and model-gateway-compatible deployment patterns. | Approved provider, model, gateway, rate and data terms. |
| Customer data boundaries | Customer data is segregated and not used to train AI for another customer or a public model. | Approved sources, residency, retention and data-classification requirements. |
| Identity & access | Enterprise identity integration and role-based control patterns for users and AI Workers. | IdP, provisioning, role definitions, privileged access policy. |
| Decision logic | Rules, decision models, AI/ML, evidence and confidence can be combined with explicit controls. | Business policy, decision rights, risk classification and approval thresholds. |
| Human oversight | Human review, exception routing, confidence thresholds and configurable autonomy by use case. | Required review points and criteria for increasing autonomy. |
| Explainability & audit | Decision context, evidence, recommendation, action, override and outcome are captured for traceability. | Audit retention, SIEM/observability integration and review procedures. |
| Change governance | Decision rules, models and configurations can be managed as controlled deployment artifacts. | Testing, release approval, promotion, rollback and model-change policy. |
| Security & compliance | SOC 2 Type II, ISO/IEC 27001:2022, GDPR readiness and NIST AI RMF alignment; evidence available through the Trust Center. | Vendor review, regulatory obligations and customer-specific control requirements. |
Governance-first deployment
Start with one measurable decision. Build the evidence to scale.
A focused anchor use case creates a practical path to align architecture, security, governance, and operational value without waiting for a broad multi-year transformation.
Align
Architecture and governance workshop with IT, security, data, AI governance, and the service business owner.
- System boundaries
- Data access
- Model strategy
- Risk classification
Validate
Use historical or representative decisions to establish baseline performance and acceptance criteria.
- Accuracy
- Exceptions
- Business outcomes
- Human-review policy
Launch
Deploy at a conservative autonomy level with monitoring, auditability, and clear escalation paths.
- Human approval
- Decision logs
- Operational monitoring
- Change control
Expand
Increase autonomy and add adjacent use cases only when evidence demonstrates the required quality and value.
- Promotion gates
- Outcome feedback
- Additional AI Workers
- Service journeys
FAQ for CIOs & AI governance teams
Goverannce questions for deploying Service AI
Does Circuitry.ai replace our enterprise AI platform or foundation-model strategy?
No. Circuitry.ai is a domain-specific Service Decision Intelligence layer. It is designed to compose with enterprise-approved foundation models, model gateways, identity, data, security, observability, and operational systems rather than replace them.
Can Circuitry.ai work with our approved foundation models?
Yes. The platform is designed around model abstraction so service decision logic, policies, evidence, and governance remain stable as approved models evolve. The deployment architecture can be aligned to the enterprise model gateway and provider strategy.
Is customer data used to train models for other customers or public models?
No. Circuitry.ai states that customer data remains segregated, remains the customer's data, and is not used to train AI for another customer or a public model. Customer-specific processing and model use are governed by the authorized deployment.
How are AI decisions made explainable and auditable?
Service decisions are grounded in approved knowledge, operational history, policies, evidence, and decision criteria. The platform can capture the basis for a recommendation together with confidence, actions, human overrides, exceptions, and outcomes so teams can review and improve decision performance.
How do we keep humans in control of consequential decisions?
Human oversight is configured by use case. Organizations can begin with advisory recommendations or approval-required actions, define confidence and exception thresholds, and increase autonomy only when agreed performance and risk criteria are met.
Can we deploy AI without allowing it to take autonomous actions?
Yes. A use case can remain informational, advisory, or approval-based. Autonomous execution is a separate governance choice and can be limited to specific decisions, actions, thresholds, roles, or operating conditions.
How does Circuitry.ai integrate with existing ERP, CRM, FSM, warranty, or dealer systems?
Circuitry.ai is designed as a decision layer alongside systems of record. Integration patterns use APIs, events, webhooks, and connectors to retrieve approved context and return recommendations, decisions, or governed actions to existing workflows.
What security and compliance evidence is available for vendor review?
Circuitry.ai currently presents SOC 2 Type II and ISO/IEC 27001:2022 as independent certifications, with GDPR readiness and NIST AI RMF alignment. Security and governance teams can request current reports, documentation, and other controlled evidence through the Circuitry.ai Trust Center.
How should we govern model, prompt, rule, and AI Worker changes?
Treat them as controlled production artifacts: define owners, test against representative decisions, document acceptance criteria, version changes, approve promotion, monitor post-release performance, and maintain a rollback path. Circuitry.ai's decision-oriented architecture is designed to support this operating model.
What is a low-risk first deployment?
Choose one high-value decision with measurable outcomes and clear human ownership—for example claim scoring, service guidance, parts recommendations, or support triage. Complete an architecture and governance review, validate performance on representative data, then launch at an appropriate human-oversight level before expanding.
How do we assess portability and exit risk?
Include portability in the architecture review: document data ownership, decision logs, configuration artifacts, integration dependencies, model strategy, and the export requirements that apply to your deployment. This makes reversibility a governance requirement from the start.
Where can our security team obtain the latest information?
Use the Circuitry.ai Trust Center for the current security posture, compliance references, and requests for controlled documentation. Customer-specific architecture, data flows, retention, integrations, and governance requirements should be reviewed during technical due diligence.
Architecture + governance review
Bring your Service AI use case into your enterprise governance framework.
Review the architecture, data boundaries, model strategy, security controls, human oversight, auditability, and deployment path with Circuitry.ai.