Uptime.AI helps operators, technicians, engineers, and managers resolve faults, follow procedures, train employees, and escalate support — using your approved manuals, SOPs, machine data, and history.
Bounded to your approved manuals, user role, machine context, and safety rules.
Uptime.AI turns static documentation and live machine context into step-by-step guidance that is faster and safer than searching manuals or calling support.
Translate fault codes and symptoms into corrective actions, parameter checks, safe recovery steps, and exact manual citations — calibrated to who is asking.
Walk users through startup, changeover, LOTO, cold start, PM, and recovery workflows — step by step, enforced in sequence, adapted to role and experience.
Mine conversations, faults, and historian data for repeat issues, support demand, parts opportunities, and training gaps across machines and sites.
Help new employees learn the machine through interactive, role-appropriate explanations — replacing the PDF stack and reducing time-to-competency.
Role-based access, audit trails, approved-document enforcement, refusal rules, and escalation paths. Audit-ready transcripts on every session.
Package the user's question, machine state, alarm, manual citations, and attempted steps before routing to support — automatically, with full context.
Operators do not need to know which manual, page, parameter, or support contact applies. Uptime.AI narrows the answer using machine context, role, and history.
Axis, drive, alarm, recipe, and service context prioritizes the answer.
The same fault should not be explained the same way to an operator, a technician, an engineer, and a manager. Role is set at login — no session configuration needed.
The core AI layer is hardware-agnostic and reusable across every project. The adapter layer changes per machine, PLC, historian, or customer standard — and it is the only layer that changes.
Reusable intelligence — hardware agnostic, never changes between projects.
Project-specific connectors — changes per machine, customer, or platform.
Existing equipment and enterprise systems — no replacement required.
General AI is open-ended. Uptime.AI is designed for controlled industrial environments where wrong answers, skipped safety steps, or missing context create downtime and risk.
A practical pilot focuses on one machine, one set of documents, a small user group, and measurable downtime or support outcomes. Phase 1 is live in 4 weeks at a fixed fee of $6,460.
Select the machine, user roles, manuals, common fault codes, and procedures that matter most on your floor.
Load up to 20 manuals, SOPs, drawings, alarm catalogs, and escalation rules. OCR fallback handles scanned documents.
Add optional PLC, historian, SCADA, CMMS, or email integrations as needed via OPC UA, MQTT, REST, or SQL.
Track MTTR reduction, first-contact resolution rate, support call deflection, onboarding time, and compliance readiness.
Start with a focused use case: fault recovery, maintenance procedures, operator training, OEM support deflection, or machine analytics. SE Automation Solutions handles the integration path.
It can be deployed as advisory-only, approval-gated, or integrated with workflows. For most pilots, the safest starting point is advisory guidance with citations, audit trails, and escalation — read-only, no machine commands.
Yes. Uptime.AI is designed to connect around existing equipment through standard OT and IT interfaces — OPC UA, MQTT, REST, SQL, Modbus — rather than forcing a controls replacement.
You own everything. Manuals, queries, and transcripts live in your own AWS, cloud, or on-premises accounts. There is no cross-customer model training. AWS Bedrock contractually guarantees Anthropic does not train on your data.
Yes. Depending on the AI model and customer IT requirements, the architecture can be edge, VM, cloud, hybrid, or restricted-network. Air-gapped options are available.
A good pilot has known manuals, frequent fault codes or procedures, clear user roles, and measurable outcomes — MTTR reduction, support call deflection, onboarding time, or compliance visibility. One machine, focused scope, 4 weeks.
Monthly cloud and AI operating costs run approximately $55–$220/month for a typical single-plant deployment (20 users, ~500 sessions/month). You own the code outright — no ongoing license fee or vendor lock-in.