Deployment Options
AI-Powered Support & Diagnosis System for Industrial & Professional Equipment
Instant, secure, AI-driven technical support that reduces downtime, scales your service operations, and protects your proprietary knowledge base
Overview
An AI-powered support and diagnosis solution designed to help equipment-driven organizations deliver instant, accurate, and scalable technical assistance. The system analyzes historical service data, repair procedures, manuals, and real-world cases to automate diagnostics, triage issues across channels, and guide users through safe, step-by-step resolutions
Who This Solution Is For
Industrial equipment manufacturers responsible for maintaining the machinery installed at their customers’ facilities and facing the following challenges:
Global Customer Base with 24/7 Support Expectations
Equipment owners operate across different time zones and require immediate assistance at any hour
High Cost of Equipment Downtime
Every hour of outage leads to production delays, service interruptions, or lost revenue
Rising costs of technical support, field service, and service-center logistics
There is a need to prevent unnecessary shipments and field visits—cutting costs and restoring equipment uptime faster
Large Installed Base of Legacy Equipment without Active Field Service
Older models remain in use for years, even after the manufacturer phases out on-site support
Shortage of Skilled Service Engineers
Hiring, training, and retaining experienced technicians is difficult—especially across global markets
Growing Complexity of Equipment and Exploding Volumes of Technical Documentation
Devices evolve faster than service teams can absorb new manuals, updates, and service bulletins
How We Can Deploy Our AI-Driven Customer Support Solution
We provide secure deployment and data protection, offering flexible enterprise-grade options — from standard cloud setups to fully isolated on-prem environments — designed specifically for industrial equipment manufacturers, including those who prefer not to expose proprietary service knowledge or use external AI models.
Data Isolation & Security Guarantees
Three-Tier AI Support Flow
1st Line
AI Call Intake Assistant
2nd Line
AI Diagnostic & Resolution
3rd Line
Engineering Support
AI Call Intake Assistant
This AI layer handles the initial service request via phone (PSTN, SIP) and performs the full intake, validation, and routing workflow
Client Identification
- Detects the caller’s phone number
-
Checks whether the number is linked to an existing client account.
If the number is new:- asks for the company name, equipment serial number, and contact person
- adds the number to the account (or suggests linking it to an existing profile)
- Verifies the client’s service contract (warranty / paid / expired)
Initial Issue Intake
-
Asks structured questions to collect:
- symptoms
- error codes
- operator observations
- equipment model
- context in which the issue occurred
- Creates a structured incident record in the service system
Criticality Assessment
-
Evaluates:
- safety risks for the operator
- risk of equipment damage
- production impact
- whether the device is mission-critical (24/7)
- Assigns severity level: Critical / High / Medium / Low
Best-Route Decision
-
Decides whether to:
- escalate directly to a live engineer
- or forward the case to the AI Diagnostic & Resolution Assistant
-
Decision is based on:
- severity
- type of issue
- company policies
- device category
Transition to Digital Session
- Sends an SMS/email with a link to a digital session
- Explains that the next steps involve video diagnostics, chat, or guided instructions
Ticket Creation
- Creates a support ticket in the client’s CRM/HelpDesk (if integration enabled)
- Attaches all collected details
- Adds the preliminary assessment
- Passes the case to second-level AI or a human engineer
See how AI instantly picks up the call, validates the client, and prepares the case for diagnostics
AI Diagnostic & Resolution Assistant
This AI acts as a technical expert performing deep diagnostics, triage, repair guidance, and case resolution
Deep Diagnostic Data Collection
- Asks follow-up questions based on previous answers
- Conducts a dynamic interview (decision-tree + LLM reasoning)
-
Collects:
- video, photos, audio
- log files
- sensor data (if integrated)
Video Triage
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Analyzes user-submitted or live-stream video:
- reads indicators, displays, error codes
- identifies malfunctioning components
- evaluates visual symptoms (noise, vibration, abnormal movement)
- matches patterns to historical cases
Automated Diagnostics
-
Runs diagnostic algorithms by:
- comparing against historical cases
- analyzing failure-factors and causal chains
- determining the most likely fault scenario
-
Can state:
- “the issue is caused by component X”
- “let’s check module Y”
- “reset/calibration/test required”
Step-by-Step Repair Instructions
-
Generates stepwise repair guides:
- what tests to perform
- which tools are needed
- what to inspect or measure
- what actions may be unsafe
- Provides images or short video clips as guidance
Spare Parts Identification
- Identifies components requiring replacement
- Checks availability (if inventory integration exists)
- Builds a complete parts list + possible alternatives
- Generates a purchase request or initiates the ordering workflow
Field Engineer Dispatch, if onsite repair is required
- Creates a dispatch request
- Sends the technician the diagnostic summary, parts list, and procedural notes
- Offers available visit time slots to the client (if scheduling integration exists)
Validation Mode
If company policies restrict the AI from issuing repair instructions:
- Collects maximum diagnostic data
- Prepares a structured summary
- Hands off the case to a live specialist
Escalation When Resolution Fails
if AI exhausts all scenarios:
- Escalates to a human engineer
- Transfers full context (Q&A, video, photos, measurements, hypotheses)
Real-Time AI-driven Support Calls via WebRTC-based Mobile App
See how AI performs industrial diagnostics — analysing live equipment footage, identifying failure points, and guiding operators through safe, precise steps to restore functionality
Support Engineer Tools & Capabilities
When escalation occurs, the human specialist receives a complete toolset powered by the AI system
Full Case Visibility, The Engineer Sees:
- chronological Q&A
- video triage, photos, logs
- preliminary AI diagnosis
- incident severity
- client contract details, SLA, equipment history
AI-Powered Recommendations
- AI proposes fault hypotheses and suggested next steps
- the engineer can approve or override them
Interactive Remote Assistance Panel
- start an online call with the client
- perform real-time “remote validation” (watch live video)
- provide manual instructions or clarifications
Parts & Service Action Management
- edit the parts list
- approve spare-parts orders
- initiate service visits
- arrange return-to-service-center workflow
Manual Case Closure
- set the final case status: Resolved / Needs Parts / Needs Visit / Escalated / Warranty Claim / Return-to-Center
- add internal notes
- move the case to archive or schedule further actions
Knowledge Updates & AI Feedback Loop
- update troubleshooting steps, repair notes, and SOP corrections
- submit feedback on AI diagnostic accuracy
- improve future AI responses
- contribute new case patterns to the internal knowledge base
Live Video Support Interface Built on WebRTC
See how engineers take over with full AI-prepared context — resolving complex cases faster and with complete clarity
Solution Components
AI Core Engine
Client Admin Panel
Client App & Engineer App
Client Admin Panel
The Admin Panel centralizes control over the entire support system: from configuring how initial calls are handled and how cases transition between support tiers, to defining diagnostic workflows, enforcing AI safety boundaries, managing the knowledge base, and enabling engineers to review and improve AI behavior. It allows organizations to adapt the system to their processes, security requirements, and equipment-specific needs
Management of the Phone Entry Point (1st Line)
Administrators can:
- assign the AI Call Intake Assistant to the support phone number
- configure how initial service requests are handled
- define the conditions under which a case is routed to the 2nd Line AI or directly to an engineer
Support Tier Transition Rules
The console allows configuration of all automation logic, including:
- escalation rules from the AI Call Intake Assistant to the AI Diagnostic & Resolution Assistant
- severity-based rules that trigger immediate escalation to 3rd Line / Engineering Support
- policies that determine when AI can resolve a case autonomously and when human intervention is required
Diagnostic Workflow Orchestration (2nd Line AI)
Controls the behavior of the AI Diagnostic & Resolution Assistant:
- configuring allowed and restricted AI actions
- defining diagnostic workflows for different equipment types
- enabling/disabling modules such as video triage, file collection, guided repair, or parts workflows
Engineering Review Layer (AI Evaluation)
Engineers have a dedicated interface to:
- review AI-handled sessions
- score diagnostic accuracy and instruction quality
- correct AI conclusions when necessary
- add clarifications, new procedures, and technical instructions
- expand the knowledge base with new failure patterns
Knowledge Base Management
Admins and engineers can:
- upload and update technical documents, manuals, and SOP procedures
- add new troubleshooting steps and reference materials
- maintain version history and a full audit trail of changes
- ensure AI always operates on up-to-date, validated information
Safety & Human-in-the-Loop Controls
The console defines:
- scenarios where AI must not act autonomously
- cases requiring mandatory engineer approval
- safety boundaries and compliance policies for each AI module
Integration Management
The panel allows:
- configure integrations with CRM, ERP, HelpDesk, inventory, etc.
- manage authentication keys and secure API connections
- monitor integration status and resolve connectivity issues
Management of Client and Engineer Applications
The panel allows:
- enabling/disabling features in the Client App and Engineer App
- configuring access permissions
- maintain version history and a full audit trail of changes
- managing modules such as video submission, remote validation, and parts management
Administration & Configuration Interfaces
From discovery to full rollout — here’s how your deployment works
Our Deployment Process
Our deployment process is designed to deliver value quickly — whether your organization chooses a SaaS-style cloud setup, a private-cloud installation, or a fully isolated on-prem environment. We adapt the rollout to your infrastructure, security policies, and ecosystem, guiding you from discovery to full production deployment
Technical Discovery
Analysis of equipment types, service processes, support flows, and integration requirements (CRM/ERP/HelpDesk, telephony, apps)
Knowledge Base Ingestion
Import of manuals, service bulletins, repair procedures, historical tickets, photo/video cases, diagnostic logs. Creation of the structured internal knowledge base for AI retrieval
Mobile App Customization (Client App & Engineer App)
Branding, enabling required modules (video triage, remote validation, parts workflow), and preparing store-ready builds.
This stage includes:
— customization for iOS (Apple App Store)
— customization for Android (Google Play)
— compliance, signing, and submission
— submission of branded apps to the Apple App Store and Google Play Store, including handling of review feedback until approval
This stage includes:
— customization for iOS (Apple App Store)
— customization for Android (Google Play)
— compliance, signing, and submission
— submission of branded apps to the Apple App Store and Google Play Store, including handling of review feedback until approval
Environment Setup
Installation of the AI platform in the client’s private cloud or on-prem infrastructure. Configuration of AI modules, databases, and secure API connections
Support Flow Configuration
Setup of 1st Line → 2nd Line → 3rd Line routing logic, safety guardrails, escalation rules, and validation scenarios
Pilot Launch
Live pilot with a selected equipment line or customer segment. AI behavior tuning via Engineering Review Layer
Full Rollout
Once validated, the system is rolled out across all equipment types, service teams, and regions
Technical Discovery
Analysis of equipment types, service processes, support flows, and integration requirements (CRM/ERP/HelpDesk, telephony, apps)
Environment Setup
Installation of the AI platform in the client’s private cloud or on-prem infrastructure. Configuration of AI modules, databases, and secure API connections
Knowledge Base Ingestion
Import of manuals, service bulletins, repair procedures, historical tickets, photo/video cases, diagnostic logs. Creation of the structured internal knowledge base for AI retrieval
Support Flow Configuration
Setup of 1st Line → 2nd Line → 3rd Line routing logic, safety guardrails, escalation rules, and validation scenarios
Mobile App Customization (Client App & Engineer App)
Branding, enabling required modules (video triage, remote validation, parts workflow), and preparing store-ready builds. This stage includes: customization for iOS (Apple App Store); customization for Android (Google Play); compliance, signing, and submission; submission of branded apps to the Apple App Store and Google Play Store, including handling of review feedback until approval
Pilot Launch
Live pilot with a selected equipment line or customer segment. AI behavior tuning via Engineering Review Layer
Full Rollout
Once validated, the system is rolled out across all equipment types, service teams, and regions
Our deployment timelines are tailored to each organization’s infrastructure, documentation volume, equipment complexity, and integration requirements. A detailed timeline is provided after the Discovery Phase
Based on previous implementations and industry experience, full deployment typically spans between 3 to 6 months, depending on the scale of customization, number of equipment lines, and required integrations
What It Takes to Get Started
To help you plan investment and scope, the section below outlines the typical pricing structure for this solution
High-Level Pricing Model
Our pricing model is structured for enterprise deployments and reflects the scope of the solution components activated for each client, together with the required customization and setup effort
One-Time Implementation & Deployment Cost
A one-time project fee covering
- environment deployment (private cloud / hybrid / on-prem)
- integrations (CRM/ERP/HelpDesk/Telephony/Inventory if required)
- ingestion and structuring of the client’s knowledge base
- configuration of support flows (1st/2nd/3rd line)
- customization of Client App & Engineer App
- App Store / Google Play publishing process
- security & compliance adaptation
This implementation cost is calculated individually based on
- complexity of equipment
- volume of documentation
- number of workflows
- customization depth of mobile apps
- integration count
Fixed Monthly Subscription
A flat monthly subscription that includes
- unlimited usage of
AI Call Intake Assistant
AI Diagnostic & Resolution Assistant - Admin Console
- Engineering Review Layer
- Knowledge Base hosting & retrieval engine
- mobile applications (Client & Engineer apps)
- updates, monitoring, and ongoing system maintenance
- enterprise-grade support (tier options available)
The subscription is not tied to the number of sessions, users, or support lines. It is priced according to the selected product modules and the operational scale required by the client.
Additional Operational Usage Costs
Depending on the client’s deployment model and communication requirements, the following usage-based expenses may apply
Telephony costs
PSTN/SIP for voice calls handled by the AI Call Intake Assistant
Video/web-RTC usage fees
for video triage and remote-validation sessions inside mobile apps
Media storage
for uploaded videos, photos, and diagnostic logs (according to retention requirements)
Infrastructure consumption
compute/GPU load for AI inference, storage, networking for on-prem or private cloud deployments
Third-party communication services
used within the apps (if enabled)
These operational costs are external to the subscription and depend on actual usage, provider rates, and the client’s infrastructure choices