Your maintenance data is lying to you.
We fix that.

Missing failure codes. Inconsistent technician notes. Invisible recurring failures. We analyze your CMMS data and show you what's actually going wrong — and what it's costing.

No software to install · Works with any CMMS · Results in 5 days
$50B
Lost to unplanned downtime across U.S. manufacturing yearly
Siemens True Cost of Downtime, 2024
800 hrs
Avg. unplanned downtime per manufacturer per year
ABB Value of Reliability Report
7%
Of maintenance leaders fully trust their asset data
Limble CMMS Benchmark, 2026
80%
Of corrective maintenance repeats previous failures
SwainSmith Failure Code Study
The Problem

Same failure. Five descriptions. Zero visibility.

Your technicians describe a pump seal failure as "pump leaking from seal area", "REPL mech seal", "seal blew out again", "pmp lkg frm seal bad", and "fixed pump."

Your CMMS sees five unrelated events. We see one recurring failure costing you $180K a year.

work_orders.csv — 12,847 records
Scanning data quality...
43% of failure codes missing
! 1,247 duplicate records found
Normalizing 847 unique descriptions...
→ 23 actual failure categories identified
Calculating financial impact...
$4.2M estimated annual loss identified
See It Working

Raw Technician Notes → Structured Intelligence

Our AI expands abbreviations, understands industry jargon across 5 sectors, and classifies with transparent confidence scores.

Technician Wrote
AI Interpretation
Category
Confidence
"REPL mech seal P-101 lkg"
Replaced mechanical seal on Pump P-101, leaking
Seal Failure
● High
"BRG noise conv DE side"
Bearing noise on conveyor, drive end
Bearing Failure
● High
"MTR tripped on O/L"
Motor tripped on overload
Motor Failure
● High
"CKT BKR tripped MCC reset"
Circuit breaker tripped in motor control center, reset
Electrical Fault
● High
"XMTR reading erratic recal"
Transmitter reading erratic, recalibrated
Instrumentation
◐ Medium
"fixed it"
Insufficient detail for classification
Unclassified
○ Low

200+ abbreviations · 5 industry taxonomies · ISO 14224 aligned · Every result includes a confidence score

What You Get

Six analyses. One file. One week.

Send us one export from your CMMS. Any format. We handle the rest.

📋

Data Quality Score

Completeness, consistency, and usability — field by field. See exactly where your data breaks down.

🧠

AI Failure Normalization

Every technician note classified into standardized categories. Patterns emerge that were invisible before.

💰

Financial Impact

Downtime hours converted to dollars. Know which failures cost the most — with stated assumptions.

📊

Top Problem Assets

Equipment ranked by downtime, failure count, and MTTR. A prioritized action list, not a data dump.

Recommendations

Specific actions ranked by impact. "Fix this failure code." "Investigate this pump." "This PM isn't working."

🔄

Workflow Improvements

How to fix data quality at the source — simplified codes, field rules, and technician quick-reference guides.

Methodology

No black boxes. Here's how we calculate everything.

Transparent enough for your reliability engineer to defend to leadership.

Data Quality

Weighted scoring across three dimensions

40% completeness (critical fields filled), 30% consistency (date logic, duplicates, format), 30% usability (generic codes like "Other" flagged). Every field scored individually.

AI Normalization

Abbreviation dictionary + LLM classification

200+ term expansion layer → industry-specific LLM prompt (ISO 14224, GMP, FSMA context) → confidence scoring. Low-confidence results flagged, not guessed. Raw mapping provided for spot-checking.

Downtime

Calculated from timestamps, estimated when missing

Malfunction end − start = downtime. When timestamps are missing, estimated from created-to-completed time. Every estimate is labeled as an estimate. MTTR and MTBF calculated per asset.

Financial Impact

Your production value × our downtime hours

You provide production $/hour (or we use industry defaults). Loss = downtime × rate. Every assumption stated in the report. Purpose: frame failures in business terms, not replace cost accounting.

How It Works

Four steps. Zero disruption.

No integration. No IT. No system access. We work entirely outside your environment.

01

Export

Pull work order history from your CMMS. Any format. 10 minutes.

02

Send

NDA signed first. Encrypted transfer. Your data stays confidential.

03

Analyze

We auto-map your columns, run six analyses, generate the report.

04

Act

Full report + 45-minute walkthrough. Every finding explained.

Proof

Don't trust claims. See the output.

Download a sample report built from synthetic plant data. Exactly what you'd receive.

📊

PDF · 12 pages

Sample Assessment Report

2,000 synthetic work orders. General manufacturing plant. Every section included.

Executive Summary Data Quality Top 10 Assets AI Failure Analysis Financial Impact Recommendations
Download Sample Report →
Data Security

Your data is safe. Non-negotiable.

📝

NDA First

Mutual NDA signed before any data exchange.

🔒

Encrypted

TLS 1.3 in transit. AES-256 at rest.

🗑️

Deleted

Raw data permanently deleted within 30 days.

🛡️

Insured

Professional liability coverage on every engagement.

Why This Exists
  Every plant I looked at had the same problem — expensive CMMS platforms full of data nobody could use. The dashboards looked great. The numbers underneath were unreliable. MaintSignal exists to close that gap.
Founder, MaintSignal
Maintenance Data Intelligence
Get Started

First assessment. Free.

One data export. Five business days. Full intelligence report. Zero cost. If the findings aren't useful, you've lost nothing but 10 minutes of your admin's time.

Request Free Assessment →
NDA signed first
Any CMMS
5 days
Data deleted after