Maintenance intelligence · Early access
Every breakdown leaves a trail.
Read it before the next one.
AIFaultline connects work orders, inspection reports, manuals and sensor readings for every machine on your floor, then uses AI to surface recurring faults and tell your engineers which asset to inspect first, with the records to back it up.
Aug 19 · WO-2340
Valve plate reseated
Oct 03 · INSP-118
Inspection: 94 °C discharge
Inspect now. Three repairs in one quarter, all after valve work. Vibration up 150% since August.
01
One record per machine
Work orders, inspection reports, manuals and readings linked to the asset they belong to.
02
Evidence-linked answers
Every AI summary cites the work orders and reports it was drawn from.
03
Transparent risk ranking
Inspection priority built from rules your engineers can read and adjust.
04
No sensors required to start
Begins with the maintenance records you already keep; sensor data adds detail.
The problem
Unplanned downtime is rarely a surprise. The signals were in the records.
A mid-sized plant can log hundreds of work orders a month. The pattern that predicts the next breakdown is usually there, spread across systems no one has time to cross-reference.
Records live in five places
CMMS exports, spreadsheets, vendor PDFs, paper inspection sheets and technician notebooks. Nobody sees them together.
Repeat failures go unnoticed
The same seal is replaced three times before anyone links the failures to an alignment problem introduced during reassembly.
Calendar-based PM misses real condition
Preventive schedules run on fixed intervals, so healthy assets get serviced while deteriorating ones wait.
Managers cannot see reliability
Downtime and repair cost per asset are reconstructed by hand at month end, if at all.
The cost: emergency repairs at premium rates, missed production targets, late customer orders, and engineers spending their week searching for history instead of fixing root causes.
AIFaultline turns that scattered history into one timeline per machine, flags the patterns, and ranks what to inspect next so your team acts on evidence instead of memory.
Core features
From scattered records to a ranked inspection list
Six capabilities that share one equipment record, so a reading, a report and a repair all land in the same place.
AI Equipment Health Monitoring
Analyzes vibration, temperature, runtime and maintenance data per asset and flags readings that drift from that machine's own baseline.
→ Catch deterioration between scheduled PMs.
AI Maintenance Assistant
Ask in plain language: "Why has Compressor 04 needed three repairs this quarter?" The answer summarizes the history and cites the work orders behind it.
→ Root-cause context in seconds, not an afternoon of digging.
Maintenance Document Analysis
Extracts asset IDs, readings, parts and findings from inspection reports, vendor service records and manuals, including scanned paper forms.
Asset: C-04
Temp: 94 °C
Oil: Normal
→ Paper history becomes searchable data.
Failure Risk Prioritization
Ranks assets for inspection using transparent rules: repair frequency, trend direction, threshold breaches and inspection notes. Predictive models are added once validated against your history.
→ A defensible inspection order every morning.
Maintenance Planning Workspace
Assign inspections, track work orders, record findings against the asset, and export reliability reports for plant management.
→ Findings flow straight into action and reporting.
Unified Equipment Registry
One record per machine with its type, line, location, documents, readings and full repair timeline. Imports from spreadsheets and CMMS exports.
→ The single place every other feature builds on.
How it works
Five steps from upload to inspection order
- 1
Connect
Upload work orders, inspection sheets, manuals and CMMS exports. Add sensor CSVs where you have them.
- 2
Extract & link
Documents are read, fields extracted, and every record attached to the right asset and date.
- 3
Analyze
AI reviews each asset's timeline for repeat repairs, drifting readings and threshold breaches.
- 4
Prioritize
Assets are ranked for inspection, each with a plain-language reason and cited evidence.
- 5
Act & report
Engineers assign work orders, log findings, and managers get reliability reports.
Interactive demo
Walk through a sample plant workspace
Click an asset to see its AI finding and evidence, filter the fleet, close work orders, inspect extracted documents, or ask the assistant a question.
Northgate Plastics · Plant 2 (sample)
Sample dataAssets tracked
7
Inspect now
1
On watch
3
Open work orders
4
| Asset | Line | Vibration trend | Risk | Status |
|---|---|---|---|---|
Compressor | Utilities | 87 | Inspect now | |
Centrifugal pump | Cooling loop | 64 | Watch | |
Extruder | Line A | 58 | Watch | |
Belt conveyor | Packing | 46 | Watch | |
Chiller | Cooling loop | 39 | Healthy | |
Standby generator | Utilities | 31 | Healthy | |
Induction motor | Line B | 22 | Healthy |
Activity
- 09:42Textract extracted 5 fields from INSP-118
- 09:40C-04 risk score rose from 71 to 87
- 08:15D. Mensah closed WO-2405 (chiller PM)
- YesterdayR. Okafor uploaded 12 historical work orders
Who it's for
Built for teams that keep machines running
Small & mid-sized factories
A maintenance team of 3–20 people running on spreadsheets or a basic CMMS.
How they use it: Centralize history, get a daily inspection list, and stop repeat failures on critical lines.
Food & beverage processing
Plants where an unplanned stop spoils product and breaks delivery schedules.
How they use it: Track compressors, chillers, pumps and conveyors, and flag drift before a sanitation-shift breakdown.
Industrial maintenance contractors
Service firms maintaining equipment across many client sites.
How they use it: One workspace per client, extracted service records, and reports that show the value of their work.
Utilities & fleet operators
Operators of generators, pumps and motors spread over many locations.
How they use it: Compare asset health across sites and focus field crews where risk is highest.
Technology
How We Use Amazon Web Services
AIFaultline's architecture is designed on AWS around one path: a maintenance document or reading enters, is stored against its asset, is processed into structured fields, is analyzed for fault patterns, and comes back to engineers as a ranked, cited finding. Each stage below maps to the AWS services selected for it as the platform is built out.
- 01
Data Collection
Amazon API Gateway
Receives document uploads, CSV imports and sensor readings from the web workspace.
- 02
Secure Storage
Amazon S3 · DynamoDB
S3 keeps original files; DynamoDB keeps assets, work orders and findings.
- 03
Processing
AWS Lambda · Textract
Lambda runs on each upload; Textract pulls fields and tables from scanned reports.
- 04
AI Intelligence
Amazon Bedrock · SageMaker
Bedrock writes evidence-linked findings; SageMaker hosts validated failure models.
- 05
Application Experience
Amazon Cognito · CloudWatch
Cognito secures each workspace; CloudWatch monitors every stage.
- Amazon Bedrock: Powers the AI Maintenance Assistant and generates evidence-linked summaries of repair histories and inspection notes.
- Amazon Textract: Extracts fields, tables and readings from scanned inspection reports, service tickets and equipment manuals.
- Amazon S3: Stores uploaded maintenance documents, equipment files and historical record exports.
- Amazon DynamoDB: Holds the equipment registry, work orders, findings and risk scores as structured records.
- AWS Lambda: Runs the document-processing, normalization and risk-scoring jobs triggered by each upload.
- Amazon API Gateway: Exposes the application API used by the web workspace.
- Amazon Cognito: Handles user sign-in and separates each customer workspace.
- Amazon CloudWatch: Collects application logs, processing metrics and alarms.
- Amazon SageMaker: Hosts predictive failure models once enough validated historical data exists per asset class.
Early access
Help shape the first release
AIFaultline is in active development. We are inviting a small group of maintenance teams to preview the workspace on their own records and tell us which problems matter most.
Current stage
Building the MVP: equipment registry, record uploads, document extraction and AI summaries.
What early-access teams get
A guided preview, direct contact with the founder, and early-access pricing.
What we ask
A sample of historical work orders or inspection reports and honest feedback.
Pricing
Simple per-month pricing
Early-access pricing per workspace, billed monthly once the platform launches.
Starter
Small maintenance teams getting records out of spreadsheets
$10 /month
- Equipment registry
- Maintenance record uploads
- AI record summaries
- Inspection task tracking
Growth
Most teamsMulti-line facilities with recurring equipment issues
$20 /month
- Everything in Starter
- AI Maintenance Assistant
- Failure risk prioritization
- Document extraction from manuals and service reports
- Management reliability reports
Enterprise
Multi-site operators and maintenance contractors
$40 /month
- Everything in Growth
- Multiple sites and workspaces
- Sensor data imports (vibration, temperature, runtime)
- Role-based access
- Priority onboarding
Team
The Team Behind AIFaultline
Jerry Paul
Founder
Jerry founded AIFaultline in 2026 to give industrial maintenance teams a practical way to use the records they already keep. He leads product, engineering and early-access partnerships.
khalil@aifaultline.siteAIFaultline is an early-stage company. We work directly with maintenance managers and reliability engineers to decide what gets built next.