Reason over the equipment context.
Connect symptoms, service history and approved repair knowledge to support diagnosis and next actions.
Validate: grounded guidance, escalation quality and first-time-fix impact.
FOR CIOs, IT & AI GOVERNANCE LEADERS
Enterprise AI that delivers business value.The trust, control, and governance IT expects.
Purpose-built AI for service, parts, and warranty, evaluated against your architecture, security, governance, and operating requirements.
Service · Parts · Warranty · Service Contracts
SOC 2 Type IIIndependent assurance
ISO/IEC 27001:2022Certified ISMS
GDPR readinessPrivacy & contractual review
NIST AI RMF alignmentAI risk management practices
Review current scope, dates and supporting evidence in the Trust Center. GDPR readiness and NIST alignment are distinct from independent certification.
A SHARED CUSTOMER. A SHARED STANDARD.
Your business wants better service.
Your IT team needs a supportable way to deliver it.
Adding a vendor creates real work: architecture review, security diligence, integration, support and lifecycle management. Circuitry.ai treats IT as a delivery partner. Together, we define where specialized Service AI adds value, which controls are required and what evidence is needed to proceed.
Resolve the details that generic answers miss.
Use equipment configuration, part applicability, supersessions and availability in a parts recommendation.
Validate: fitment accuracy, exception handling and incorrect-order reduction.
Apply policy to the evidence.
Evaluate coverage, repair documentation, labor and parts against the terms that govern each claim.
Validate: false approvals, false denials, review effort and leakage.
Domain depth → Decision quality → Governed autonomy → Measured value
Reuse specialized AI Workers across adjacent use cases. Expand only when shared integrations, controls and measurable benefits justify it.
COMPOSE INTO YOUR ENTERPRISE AI STRATEGY
A domain decision layer that fits your architecture.
Keep your existing applications and approved data sources in the design. Circuitry.ai combines specialized AI Workers with decision orchestration, so your internal agents can call service capabilities and Circuitry.ai can invoke approved enterprise tools.
Explore the published architectureEXISTING SYSTEMS & DATA
ERP · CRM · FSM · DMS
Claims & service applications
Knowledge · Parts · History
Scoped read / write permissionsCIRCUITRY.AI
Service Decision Intelligence
Context + domain models + rules + evidence
AdvisorsRecommendAnalystsEvaluateAgentsAct
Human approval · Exceptions · Explainability · Audit
YOUR AI ECOSYSTEM
Enterprise agent platform
Approved model gateway
Internal tools & services
REST APIs / MCP interfacesAutonomous Service JourneysOrchestrate approved work across people, AI Workers and connected systems, with observability, feedback and evaluations.
Model flexibility
Evaluate OpenAI GPT, Google Gemini, Anthropic Claude or your approved model endpoint for the workload. Confirm supported APIs, model behavior and data-processing terms before enabling a provider.
Cloud clarity
Published subprocessors include AWS for core hosting, Azure for AI/ML and data services, and GCP for APIs and LLMs. Agree the actual deployment regions and data path; these roles do not imply identical hosting options on every cloud.
Review subprocessorsIntegration by contract
Use documented APIs and MCP services with scoped credentials. Agree schemas, rate limits, failure handling and ownership. Protocol support still requires testing against your applications and security policies.
Review MCP supportBUILT INTO THE DECISION INTELLIGENCE PLATFORM
Explain the decision.
Observe, evaluate and improve it.
Explainability, observability, feedback and evaluations are built into Circuitry.ai’s Decision Intelligence platform. Together, they help business and IT teams understand how AI is being used, review its outcomes and make informed decisions about changes and autonomy.
Understand the basis for a decision.
Review the evidence, relevant policy, recommendation and reasons for escalation. A technician, adjuster or reviewer needs a business explanation they can check against the source material.
Evidence to review together
A sample decision record showing source references, applicable rules, rationale, uncertainty and any human override.
An explanation supports review; it does not establish correctness. Validate the explanation against the evidence and outcome.
See what happened across the journey.
Follow the decision and its execution across AI Workers, model interactions, tool calls and human review. Review operational signals such as latency and failures alongside decision signals such as exceptions and overrides.
Evidence to review together
An execution trace, operational metrics and an exception path, with access and redaction controls for sensitive information.
Agree which events, identifiers and metrics are exposed, their retention, and integration with your monitoring or SIEM tools.
Connect corrections to actual outcomes.
Capture feedback from reviewers and the business process, including corrections, overrides and downstream results. Use that evidence to identify knowledge gaps, policy ambiguity and cases that need further evaluation.
Evidence to review together
An example linking a decision to reviewer feedback, the resolved outcome and a proposed improvement.
Feedback is input to improvement. Agree who validates it and approves changes before it affects production behavior.
Measure fitness for the specific decision.
Evaluate recommendations and actions against representative customer cases and agreed reference outcomes. Compare performance before and after changes to a model, prompt, rule, knowledge source or AI Worker.
Evidence to review together
Evaluation results by decision type, error severity and relevant operating segment, including exceptions and human review rates.
Agree baseline, test-set quality, pass thresholds, production sampling and re-evaluation triggers. Model benchmarks alone are insufficient.
THE GOVERNANCE LOOP
Evidence informs the next release.
Decision evidence and execution records connect to feedback and outcomes. Evaluations then inform approved changes and autonomy levels. Assign accountable owners, review cadence and release gates so improvement remains under your governance.
What this looks like in a warranty review
Illustrative scenario: A claim has incomplete repair evidence. The reviewer checks the relevant policy and explanation, follows the request and tool activity, records the final disposition, and adds the resolved case to an agreed evaluation set. Before changing an automation threshold, the team checks whether the proposed change improves results without increasing unacceptable approval or denial errors.
This is an evaluation scenario to walk through during your assessment; the exact fields, controls and acceptance criteria are agreed for your deployment.
01Data protection & ownershipKnow exactly where your data goes.
Your data remains yours. Circuitry.ai segregates customer data and does not use it to train another customer’s models or public models. The review should trace the full processing path, including retrieval, model requests, derived data, logs and backups.
Review output Approved data-flow diagram + DPA + retention schedule
Supporting reference02Identity & least privilegeKeep access under enterprise control.
Integrate SSO with your identity provider and align MFA with enterprise access policies. Agree roles for employees, dealers, administrators and AI Workers. User access and machine access each need explicit boundaries.
Review output Identity design + access test results + role matrix
Supporting reference03Integration & interoperabilityFit the systems you already operate.
OpenAPI-compliant REST APIs and MCP services provide integration paths for ERP, CRM, FSM, DMS, warranty systems and internal agents. Circuitry.ai can orchestrate domain AI Workers or expose them to your enterprise orchestration layer.
Review output Interface specifications + integration contract tests
Supporting reference04Model strategy & AI qualityApprove the model and the decision it supports.
Evaluate enterprise model options such as OpenAI GPT, Google Gemini and Anthropic Claude against each task. Bring your approved model API or gateway into the architecture review; compatibility, regional availability, capacity and commercial terms must be confirmed.
Review output Evaluation dataset + results by case type + model register
Supporting reference05Explainability & observabilityInspect the decision and its execution.
Built-in explainability and observability support a review of both the business decision and the operational path that produced it. Specify the evidence your reviewers need and the telemetry your IT team needs to operate the deployment.
Review output Sample decision record + execution trace + telemetry specification
Supporting reference06Feedback & evaluation lifecycleTurn learning into controlled improvement.
Built-in feedback and evaluations connect reviewer input and business outcomes to improvement. Define how proposed changes are tested, approved and monitored, and what evidence is required before autonomy can increase.
Review output Evaluation report + feedback example + change and autonomy gates
Supporting reference07Agent safety & human oversightGrant autonomy only within agreed limits.
Set decision rights before granting tool access. Separate advice from approval and execution. A useful evaluation should show what happens when evidence is missing, instructions conflict, a document is malicious or a tool fails.
Review output Threat model + adversarial tests + autonomy policy
External evaluation reference08Reliability, scale & resilienceSpecify production behavior under pressure.
Cloud infrastructure is a foundation; workload evidence is the acceptance test. Size the deployment for your concurrency, data volume, latency and transaction peaks, including upstream model and enterprise-system dependencies.
Review output Load-test results + recovery evidence + agreed SLA
External evaluation reference09Operations & controlled changeKnow who owns the service after go-live.
Enterprise AI as a Service includes ongoing maintenance, updates and support. Your operating agreement should make the boundaries explicit across Circuitry.ai, internal IT, source-system vendors and model providers.
Review output Support runbook + RACI + release and rollback plan
Supporting reference10Cost management & measurable valueMake the business case auditable.
Service Decision Unit pricing ties the subscription to business activity. Compare total cost over the contract term, including implementation, integrations, internal effort, optional services and any customer-supplied model consumption.
Review output Volume-based TCO model + baseline + benefits owner
Supporting reference11Vendor diligence & exit readinessMake reversibility part of the agreement.
Data ownership and open interfaces reduce dependency, but practical portability also needs a tested exit plan. Agree which customer data and decision artifacts can be exported, in what format, on what schedule and at what cost.
Review output Vendor review + sample export + documented exit terms
Supporting referenceA GATED PATH TO VALUE
Start in weeks.
Scale on evidence.
Preconfigured domain capabilities and integration patterns can shorten the path to a focused pilot. Establish the data, security and acceptance gates before committing to production.
Illustrative warranty deployment sequence based on Circuitry.ai’s published guidance. Timing depends on data readiness, integration access and approvals; it is not a guaranteed delivery schedule.
- WEEKS 1–2
Scope & connect
Choose one decision. Agree baseline, data access, risk owner and success criteria.
Gate: approved scope - WEEKS 3–6
Configure & evaluate
Map policies and evidence. Test a representative set of historical decisions.
Gate: acceptance evidence - WEEKS 7–8
Pilot alongside people
Compare results, capture reviewer feedback and test decision explanations and operational traces.
Gate: joint go / no-go - WEEKS 9–12
Launch with controls
Start at the agreed autonomy level. Monitor outcomes, evaluate changes and route exceptions.
Gate: production readiness
A FAIR BUILD, EXTEND OR PARTNER DECISION
Choose the approach your team can sustain.
Existing enterprise AI investments are part of the solution. Evaluate where your team should build differentiating capabilities and where domain software can reduce delivery and maintenance effort.
| Approach | A strong fit when… | Evaluate carefully |
|---|---|---|
| Build internally | Unique requirements justify dedicated product, domain and AI engineering ownership. | Domain modeling, evaluation data, integration delivery and lifetime operating capacity. |
| Extend an existing application | The decision, data and action largely stay inside one application. | Cross-system context, model controls and the cost of extending into adjacent use cases. |
| Partner with Circuitry.ai | Specialized service, parts and warranty decisions need shared context across systems. | Evidence of domain fit, connector scope, measured quality, full cost and exit terms. |
Domain depthValidate policies, equipment context and exceptions.
Accuracy & autonomyMeasure decision quality before expanding action rights.
ROI & valueCompare net benefits with the complete cost to operate.
EVIDENCE FOR YOUR REVIEW
Start with the documentation.
Then test the fit.
Use the public resources below to prepare. Request current assurance reports, relevant production references and deployment-specific evidence through the review process.
01 / SECURITY
Trust Center
Assurance documentation and controlled evidence requests.
02 / ARCHITECTUREDecision Intelligence architecture
Data, reasoning, AI Workers and enterprise integration.
03 / GOVERNANCEService AI governance
Decision rights, oversight and governance practices.
04 / CUSTOMER EXPERIENCEPublished customer case studies
Examples including CARS Protection Plus, Takeuchi and Hendrick AutoGuard.
Enterprise experience, with scope you can verify.
The Circuitry.ai team’s collective experience includes building and delivering solutions to hundreds of global enterprises across their careers. That experience informs software development, implementation and support; request current Circuitry.ai references for comparable workloads, volume and complexity.
Meet the teamIndependent frameworks behind the evaluation checklist
The checklist also draws on NIST AI RMF for risk ownership and lifecycle governance, OWASP’s LLM risk guidance for AI application threats, and Microsoft’s AI workload design principles for security, reliability, performance and cost. These are evaluation references, not endorsements of Circuitry.ai.
A SOLUTION BUSINESS LIKES. AN APPROACH IT CAN ENDORSE.
Start your IT review and assessment.
Bring IT, security, AI governance and the business owner into one working session. Share your checklist, and we will work through it together.
- Architecture and integration review
- Security questionnaire and evidence mapping
- AI governance and human oversight requirements
- Explainability, observability, feedback and evaluation walkthrough
- Data protection agreement review
- Availability, performance and support requirements
- A scoped validation plan with owners and decision gates
Share requirements first. We will identify fit, dependencies and any gaps to resolve before deployment.
Better Service Decisions.
Better Service Outcomes.
START YOUR IT REVIEW
Bring your standards.
Let’s work through them.
Please avoid including confidential architecture, credentials or customer data in this form. We can arrange an appropriate channel for detailed review materials.