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Don't Just Manage
Your Aftermarket. Own It.

Unlock hidden revenue with an intelligent, unified platform for warranty and predictive maintenance. Tailored for the complex needs of CNC, mining, and heavy equipment manufacturers.

ServiceCPQ — Industry Use Cases
Industry Use Cases

One platform. Every stage of equipment revenue.

From warranty claims to service contracts, rebuild costing and predictive maintenance — AI-powered, deeply integrated, built for heavy industry.

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50%Faster Claim Processing
30%More Aftermarket Revenue
700+Service Partners
40%Fewer Support Calls
Industry Solutions

Select your industry

Choose an industry to see exactly how ServiceCPQ eliminates manual effort and captures hidden service revenue.

HVAC & Compressors
🔧

Warranty & Claim Automation

Cut claim processing from weeks to hours. AI validates, fraud-screens, and settles — while SAP stays in sync. 700+ service partners, zero paper trail.

Explore use case →
Construction Machinery
🏗️

Dealer & Field Repair Platform

From first field call to final billing — give dealers a unified platform for repair workflows, AMC renewals, and supplier claims with full automation.

Explore use case →
CNC Manufacturing
⚙️

B2B Self-Service Portal

Let customers self-serve 24/7. Visual parts lookup, AI maintenance kits, and one-click warranty claims slash support calls by 40% and grow parts revenue.

Explore use case →
Mining Equipment
⛏️

AI Component Rebuild Automation

Eight AI agents guide every rebuild from intake to billing. Dynamic pricing and SAP integration make complex, multi-line repairs consistently profitable.

Explore use case →
Heavy Equipment
🚜

AI Service Opportunity Engine

Stop chasing leads manually. AI models pinpoint which customers will buy next, what they need, and at what price — before your competitors get there.

Explore use case →
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Insights & Guides

Deep dives worth reading

Practical thinking on pricing, rebuild costing, and AI maintenance — from the people building these tools.

Insights
How AI is Transforming Industrial Service Operations
ServiceCPQ Team · 5 minDec 15, 2024
Case Study
ROI Analysis: Digital Service Transformation in Heavy Industry
ServiceCPQ Team · 8 minDec 10, 2024
Guide
Complete Guide to Warranty Claim Automation
ServiceCPQ Team · 12 minDec 5, 2024
Trends
Future of B2B Service Portals: 2025 Industry Outlook
ServiceCPQ Team · 6 minNov 28, 2024
Deep Dive 📋
How to Price Long-Term Service Contracts Accurately
Lifecycle cost modeling that protects your margins across 3–5 year contracts — parts inflation, labor escalation, and equipment aging all accounted for.
ServiceCPQ Team · 10 min Read →
Deep Dive 🔩
Why Excel Fails for Rebuild Cost Modeling
Eight ways spreadsheets silently destroy your rebuild margins — and what AI-powered job libraries and live pricing do differently.
ServiceCPQ Team · 9 min Read →
Deep Dive 🔬
How AI Improves Maintenance Interval Modeling
From OEM manuals and historical records, AI auto-generates model-specific schedules — precision-calibrated per machine, not one-size-fits-all.
ServiceCPQ Team · 11 min Read →
HVAC & Compressors

Smarter Warranty Management — From Submission to Settlement

Automate claim validation, fraud detection, and SAP reconciliation across 700+ service partners — without adding headcount.

Industry Overview

HVAC and compressor manufacturers operate through dense partner networks where warranty claims arrive in high volumes — often with incomplete data, fraudulent signals, or multi-level approval requirements. ServiceCPQ replaces fragmented email workflows and manual SAP entries with a single AI-driven platform that validates, approves, and settles claims end-to-end.

Featured Use Cases

1. Automated Warranty Claim Management

Scenario

700+ service partners submit claims via mobile or web. AI instantly validates eligibility, cross-references failure codes, checks fraud signals, and routes to the appropriate approver — with SAP updated in real time.

Value Delivered

  • 50% faster claim processing — days become hours
  • Real-time visibility for partners and OEM finance teams
  • Automated inventory credit and debit in SAP
  • Significantly reduced manual errors and fraudulent submissions

2. Goodwill & Out-of-Warranty Claims

Scenario

Claims submitted just outside warranty are AI-triaged for goodwill eligibility, then routed with full cost context — OEM absorption split, supplier recovery potential, and customer lifetime value — directly to the right approval level.

Value Delivered

  • Higher customer retention through fair, fast goodwill decisions
  • Transparent, auditable approval workflows at every level
  • Optimised cost recovery with automated supplier claims

Why Choose ServiceCPQ?

  • Deep SAP and Salesforce integration — zero re-keying
  • Built-in AI for fraud detection and intelligent approval routing
  • Scales to thousands of partners without process complexity
  • Compliance-ready with full audit trail and reporting
Construction Machinery

End-to-End Dealer & Field Repair Platform

Unify field service, dealer tools, and complex repair workflows on a single AI-driven platform — from intake to invoice.

Industry Overview

Construction equipment companies that procure globally and assemble locally face uniquely complex service challenges — multi-brand parts catalogues, international supplier claim chains, and dealer networks that need digital tools to meet modern customer expectations. ServiceCPQ turns every service event into a structured, trackable, billable workflow.

Featured Solutions

1. Field Service & Complex Repair Management

  • Centralised call intake, technician dispatch, and real-time job tracking
  • Digital job cards capturing all parts, labor hours, consumables, and costs
  • Agentic repair workflow: intake → diagnosis → estimation → approval → assembly → testing → return
  • OEM and supplier claim management with automated cost absorption splits
  • Remanufacturing support: track salvaged components through every rebuild step

Value Delivered

  • Faster, error-free repair cycles with full traceability
  • Reduced downtime and improved asset lifecycle management

2. Dealer Portal & Proactive Service

  • Unified portal: visit planning, AMC management, digital job card generation
  • AI-guided quote generation with customisable service letter templates
  • Automated customer notifications, service reminders, and real-time updates
  • Centralised service history enabling proactive upsell and intervention

Value Delivered

  • Higher AMC renewal rates and expanded service revenue
  • Full operational visibility across the dealer network

Why Choose ServiceCPQ?

  • End-to-end lifecycle — field calls through complex repairs and dealer empowerment
  • Adaptable workflows for imported, locally assembled, or remanufactured machinery
  • Real-time ERP, inventory, and financial system integration
  • Best-practice design inspired by global leaders such as Caterpillar and Komatsu
CNC Manufacturing

A B2B Portal That Pays for Itself in Year One

Visual parts lookup, AI maintenance kits, and one-click warranty claims — all in a white-labelled portal that reduces support calls by 40% and grows parts revenue.

Challenge

CNC customers expect Amazon-like digital experiences. But most manufacturers still handle parts orders over email, service bookings by phone, and warranty claims by PDF. The result: overloaded support teams and parts revenue leaking to third-party distributors.

Key Features

Visual Parts Procurement

  • Click on interactive exploded CAD drawings to identify and order parts directly
  • AI suggests alternatives if primary parts are out of stock
  • Subscription maintenance kits — configure once, ship automatically

Proactive AI Maintenance

  • AI analyses machine usage, error logs, and OEM guidelines to personalise maintenance plans
  • Automated alerts when maintenance is due or parts nearing end-of-life
  • Technician dispatch triggered automatically from the portal

Warranty & Documentation Hub

  • One-click warranty claims: serial number, photo, and error code in seconds
  • Auto-populated warranty periods, service contracts, and claim history per machine
  • Defective part returns with automated labels and SAP inventory updates

Results

40%Fewer Support Calls
70%Faster Parts Ordering
30%More Parts Revenue
20%Longer Machine Life

Workflow Example

  1. Customer selects machine by serial number in the portal
  2. Clicks interactive drawing to identify a worn spindle bearing
  3. AI recommends exact part number and a matching maintenance kit
  4. Customer checks out with subscription discount in under 2 minutes
  5. ERP processes the order — part ships same day
  6. Portal updates maintenance schedule and auto-dispatches technician if required
Mining Equipment

AI-Driven Component Rebuild Automation

Eight specialised AI agents guide every repair stage — from intake to billing — making complex, high-value rebuilds consistently profitable.

Challenge

Mining equipment rebuilds involve hundreds of line items, complex supplier chains, and significant cost exposure if estimated incorrectly. Traditional workshop management relies on experienced engineers guessing — leading to wide quote-to-actual variances and margin erosion.

Key Features

Agentic Repair Workflow

  • Eight AI agents: intake, disassembly, diagnosis, estimation, proposal, approval, assembly, testing, billing
  • Consistent execution at every stage — no missed steps, no ambiguity
  • Automated quality checks, compliance tracking, and exception escalation

Smart Parts Management

  • Upload Excel parts lists — AI parses and maps to your ERP catalogue instantly
  • Natural language description → AI builds bill of materials automatically
  • Live parts pricing and availability pulled from ERP in real time

AI Pricing & Margin Optimisation

  • Proprietary templates enable rapid, accurate estimation from job history
  • Dynamic pricing and margin recommendations per job type
  • Competitive pricing benchmarked against historical win rates

Results

  • Faster rebuild cycles — automation significantly reduces turnaround time
  • Tighter cost variance — AI estimation closes the quote-to-actual gap
  • Improved margins — dynamic pricing prevents systematic under-quoting
  • Customer trust — transparent digital proposals and real-time job status

Workflow

  1. Intake agent logs serial, condition, and customer details from site photos
  2. Disassembly agent guides technicians and flags known failure points for that model
  3. Estimation agent generates comprehensive quote: labour, parts, predictive add-ons
  4. Customer receives digital proposal for review and e-signature
  5. Assembly agent tracks progress, quality checks, and test milestones
  6. Billing agent generates invoice and triggers SAP reconciliation automatically

Why ServiceCPQ?

  • Agentic AI purpose-built for the complexity of mining equipment rebuilds
  • Deep ERP integration — SAP, Oracle, and major platforms supported
  • Handles OEM and third-party component models
  • Proven at scale across high-volume, high-value repair operations
Heavy Equipment

AI Service Opportunity Engine — Sell More, Serve Better

Propensity modeling, gap-to-entitlement analysis, and AI virtual reps that surface and close service revenue your team would otherwise miss.

Challenge

Heavy equipment OEMs are sitting on vast pools of untapped aftermarket revenue — customers overdue for service, fleets with coverage gaps, upsell opportunities buried in historical data. Traditional sales teams cannot surface these at scale.

Key Features

Propensity-to-Buy Modeling

  • Machine learning analyses customer profiles, equipment usage, and purchase history
  • Predicts who will buy next, what they need, and when
  • Prioritises high-value targets so sales effort goes where it converts

Gap-to-Entitlement Analysis

  • Instantly shows what each customer has purchased vs. what they are eligible for
  • Reveals untapped contract renewal and upgrade potential across the full fleet
  • Surfaces missed opportunities before they lapse or go to a competitor

AI Virtual Service & Sales Reps

  • Natural language prompts: "What opportunities exist for Customer X's excavator fleet?"
  • AI responds with upsell options, labour recommendations, parts, and pricing
  • Personalised proposal generation — ready to send in seconds

Results

30%More Service Revenue
3×Sales Team Output
25%Higher Customer Retention
60%Less Manual Analysis

Workflow

  1. AI scans customer data overnight — flags high-propensity accounts with overdue maintenance
  2. Dashboard shows gap-to-entitlement: Customer X hasn't purchased recommended service kits
  3. Rep asks AI: "Suggest service opportunities for this fleet"
  4. AI returns upsell options, recommended labour hours, parts, and pricing with margin impact
  5. Rep sends AI-generated personalised proposal from the platform
  6. Accepted proposals auto-convert to service orders — zero re-entry

Why ServiceCPQ?

  • Move from reactive to predictive — find opportunities before competitors do
  • Every rep performs like your top performer — AI democratises expertise
  • Works with existing CRM, ERP, and service management tools
  • Natural language interface — immediate productivity, no training required
Deep Dive · 10 min read

How to Price Long-Term Service Contracts Accurately

Multi-year contracts erode margins silently. Here's the lifecycle cost framework that protects you across 3–5 years — inflation, aging, and utilisation all accounted for.

The Problem: Guesswork Erodes Margins Over Time

Most industrial OEMs price multi-year service contracts using a fixed markup on year-one cost — or an intuitive "feels about right" adjustment. Neither accounts for the compounding effect of parts inflation, labour rate escalation, or equipment degradation over a 3–5 year horizon.

⚠ The Silent Margin Killer

A contract priced at a 15% margin in year one can swing to a −3% loss by year four if parts costs inflate at 6% annually and the contract assumed 3%. On a ₹50 lakh contract, that's a ₹9 lakh swing the pricing team never saw coming.

The failure isn't negligence — it's the absence of a structured lifecycle cost model at the time of contract creation.

The Lifecycle Cost Framework

Accurate long-term contract pricing requires modeling five interconnected cost dimensions across every year of the contract.

1. Baseline Cost Structure

Start with the fully loaded cost of delivering service in year one: parts at current supplier prices, labour at current technician rates, consumables, travel, and overhead. This is your foundation — every other component builds on this.

2. Escalation Modeling

Apply independently calibrated escalation indices to each cost category. Parts categories escalate differently — bearings vs. seals vs. electronics have different supply chain dynamics. Using a single blended escalation rate is the most common pricing mistake in long-term contracts.

  • Parts escalation by category (engineered components, wear items, consumables)
  • Labour escalation by skill tier and region
  • Travel and logistics inflation modeled separately

3. Utilisation-Based Cost Adjustment

A machine running 3,000 hours/year consumes parts at a fundamentally different rate than one running 1,200 hours. Flat-rate contracts on variable-utilisation fleets systematically under-price high-use customers.

  • Hours-based or cycles-based cost scaling per asset
  • Usage bands: low / standard / high / extreme duty
  • Environmental modifiers: dust, humidity, altitude, temperature

4. Equipment Age & Condition Risk

Older equipment costs more to maintain on a bathtub-curve pattern. Years four and five on an already-aging machine carry substantially higher unplanned repair risk — this must be priced upfront.

  • Age-based failure rate multipliers by equipment class
  • Condition assessment at contract inception (oil analysis, inspection scores)
  • Risk premium bands: standard / elevated / high-risk

Contract Tiers: Align Scope to Price

🥉
Preventive Only

Scheduled maintenance only. Customer bears unplanned repair costs. Lowest price point — ideal for new machines.

🥈
Comprehensive

Preventive maintenance plus most unplanned repairs. Parts and labour included. Standard choice for mid-age fleets.

🥇
All-In

Full coverage including major component rebuilds, travel, and guaranteed uptime SLAs. Premium tier for critical assets.

Each tier should carry a distinct margin target, and the pricing model must stress-test each tier under pessimistic scenarios before the contract is signed.

How ServiceCPQ Builds Your Contract Pricing Model

  • Pre-built escalation indices by part category, sourced from historical purchase data
  • Historical cost actuals feed the baseline — not manufacturer list prices
  • Side-by-side scenario comparison: flat-rate vs. usage-based vs. indexed pricing
  • Stress-test simulator: "What happens to margin if parts costs run 8% above projection?"
  • AI suggests optimal tier pricing based on similar contracts in your history
  • Automated PDF contract with full pricing breakdown for customer review

✓ Human-in-the-Loop by Design

The model surfaces recommendations. Your service pricing team reviews, adjusts, and approves every contract before it goes out. AI accelerates the analysis; your experts make the final call.

Results

60%Less Finance Effort per Contract
4×More Scenarios Modeled
±2%Margin Accuracy over 5 Years

See how ServiceCPQ's lifecycle cost engine works for your contract types.

Deep Dive · 9 min read

Why Excel Fails for Rebuild Cost Modeling

Eight ways spreadsheets silently destroy rebuild margins — and what AI-powered job libraries, historical records, and live pricing do differently.

8 Ways Excel Silently Destroys Rebuild Margins

📂
1. No Version Control

Competing "final_v3_REAL.xlsx" files in shared drives. No one knows which quote the customer has.

✍️
2. Manual Entry Errors

Hundreds of BOM line items entered by hand. One wrong multiplier compounds across the entire quote.

💸
3. Stale Pricing

Parts prices last updated six months ago. You're quoting at historical cost — not current cost.

🧠
4. Knowledge in Heads

The job library lives in senior engineers' memories. When they leave, institutional knowledge walks out.

🔍
5. No AI Suggestions

Junior estimators routinely miss associated parts and labour items experienced engineers would catch automatically.

📉
6. Backward Margin

Margin analysis happens in finance, 3 months after the job closes. The same pattern has repeated ten more times.

🔄
7. No Learning Loop

Historical actuals never feed future estimates. The same systematic under-estimates repeat, job after job.

📧
8. Collaboration Chaos

Quotes circulate by email attachment. Reviewers overwrite each other. No audit trail, no single source of truth.

The Job Library: Institutional Knowledge, Digitised

The foundation of modern rebuild cost modeling is not a blank spreadsheet — it is a structured library of every job your workshop has ever completed, indexed by equipment model, failure mode, and job type.

What a Good Job Library Contains

  • Every completed job: equipment model, serial number, failure code, symptom description
  • Actual BOM: every part used, quantity, and supplier cost at time of repair
  • Actual labour hours: by skill tier, by task, by technician
  • Quote-to-actual variance: what was estimated vs. what was spent
  • Customer outcome: accepted, reworked, disputed, or repeat failure

ServiceCPQ ingests your historical job data from ERP exports, scanned job cards, or direct system integration — and builds this library automatically. Your institutional knowledge becomes a searchable, AI-accessible asset.

AI-Led Part & Labour Suggestions

Once the job library exists, AI can do what your best engineers do — faster, at scale, available to every estimator.

  1. Estimator enters a natural language description: "Engine overhaul, Komatsu PC800, excessive oil consumption, low power"
  2. AI searches the job library for the 20 most similar completed jobs by model, failure pattern, and symptom
  3. AI generates a ranked BOM suggestion with confidence scores: "92% of similar jobs required these 47 parts"
  4. Labour hours are suggested from actuals — not OEM time standards engineers know are optimistic
  5. Estimator reviews, adjusts, and approves — AI informs, it does not override
  6. Live ERP pricing is pulled for every part at the moment of estimation

✓ Confidence-Scored Suggestions

High-confidence suggestions (30+ matching jobs) can be accepted quickly. Low-confidence ones flag for senior review — keeping the human in exactly the right place in the loop.

Real-Time Margin Intelligence

  • Target margin vs. estimated margin shown live as the BOM is built
  • Alert fires if estimated margin falls below minimum threshold — before the quote is sent
  • One-click variance analysis: quoted vs. actual, updated as jobs close
  • AI suggests equivalent part alternatives at better margin
  • Full version-controlled quote history with approver sign-off at each stage

Results

70%Faster Quote Preparation
40%Less Quote-to-Actual Variance
25%Margin Improvement via Part Selection

See how ServiceCPQ's job library and AI suggestions work with your data.

Deep Dive · 11 min read

How AI Improves Maintenance Interval Modeling

OEM manuals give you averages. AI gives you precision — auto-generating model-specific, usage-calibrated maintenance schedules from your own records and manuals.

The Problem with One-Size-Fits-All Schedules

OEM maintenance manuals specify intervals designed for average conditions: average ambient temperature, average duty cycle, average operator behaviour. In practice, none of your customers' machines are average.

⚠ The Real Cost of Averaged Intervals

Machines in harsh environments maintained on standard intervals accumulate undetected wear. The result: unplanned breakdowns 40–60% more frequently than properly-calibrated schedules would predict — and warranty claims that should have been preventable. Meanwhile, light-duty machines are over-maintained, burning service cost and frustrating customers with premature reminders.

The answer is not a single adjusted schedule — it is a model-specific, usage-calibrated schedule that learns from your actual maintenance history and improves continuously.

The Science: Four Data Streams

1. OEM Manual Extraction

AI reads your service manuals — PDF, scanned, or structured — using OCR and NLP to extract every maintenance task, interval, and condition note into a structured database. A 400-page service manual becomes a queryable task library in minutes.

  • Extracts interval tables, task lists, and conditional triggers
  • Handles multiple manual versions and updated service bulletins
  • Flags ambiguous or conflicting interval specifications for human review

2. Historical Maintenance Record Analysis

AI analyses every completed maintenance record — what was done, when, at what hours or cycles, and what condition was found — to build a statistical model of actual failure rates by component and operating condition.

  • MTBF calculated from actuals, not OEM assumptions
  • Failure patterns identified by equipment model, age band, and application type
  • Early-failure and wear-out modes distinguished from random failure modes

3. Condition Variable Modeling

Each machine's operating environment is characterised by measurable variables that AI uses to adjust intervals from the OEM baseline.

  • Hours per day / duty cycle intensity: light, standard, heavy, severe
  • Ambient conditions: dust level, humidity, temperature range
  • Application type: construction, mining, agriculture, port operations
  • Operator behaviour: idle time, overload events, warm-up compliance

4. Continuous Learning Loop

Every completed maintenance job feeds back into the model. When a machine arrives earlier than recommended with a specific failure, the model tightens that interval. When a component consistently runs beyond its recommended life without issues, the model extends it — reducing unnecessary service cost.

How ServiceCPQ Builds Your Maintenance Model

  1. Upload OEM manuals — AI extracts interval tables, task sequences, and conditional rules
  2. Import historical records — from ERP, service management systems, or scanned job cards; AI identifies patterns across thousands of completed jobs
  3. Define condition profiles — for each customer or application type, specify the operating environment variables
  4. AI generates calibrated schedules — one per equipment model, adjusted for condition profile, not a fleet-wide average
  5. Anomaly detection runs in background — flags early failures that suggest intervals need tightening for a specific machine or batch
  6. Schedules are published — to technician apps, customer portals, and automated work order systems
  7. Feedback loop closes — every completed job updates the model; intervals improve with every data point

Human-in-the-Loop: AI Recommends, Engineers Decide

The system does not automatically change maintenance intervals. Every model update is surfaced to your service engineering team as a recommendation — with full supporting evidence — before it is published.

✓ Approval Workflow for Every Change

  • AI flags: "Historical data suggests hydraulic filter interval should reduce from 500 to 350 hours for Group A (high-dust). Basis: 23 premature failures over 18 months."
  • Service engineer reviews evidence, checks for confounding factors, approves or overrides
  • Approved change is version-controlled with approver name, date, and justification
  • Rollback is one click if field feedback contradicts the change

What You Get

  • Machine-specific maintenance schedules — not fleet-wide averages, not copy-pasted OEM tables
  • Predictive alerts: "This PC800 is 91% likely to need hydraulic service in the next 45 days"
  • Automated work order generation — right task, right technician, right parts pre-kitted
  • Customer-facing maintenance calendars visible in the self-service portal
  • AI-generated interval data feeds the service contract lifecycle cost model directly

Results

35%Fewer Unplanned Breakdowns
20%Less Over-Maintenance Cost
91%Predictive Alert Accuracy
2×Faster Schedule Build

These numbers improve as the system learns from your data. The model on day 365 is materially more accurate than day one — because it has seen a full year of your machines, your customers, and your failure patterns.

See how ServiceCPQ extracts and calibrates schedules from your own manuals and records.