“AI” gets thrown around in every industry right now, often with more buzzwords than benefits.
For thermographers, the question is simpler and sharper:
Can AI actually help me inspect faster, grade risk more accurately, and deliver better reports – without turning my workflow upside down?
The answer is yes, when AI is used practically.
SnapCor is built on TICOR’s proven thermography engine – already used for real-time reporting, automatic problem grading, and knowledge-based root cause suggestions in commercial inspections. SnapCor takes that foundation and brings AI into the heart of daily thermal inspection work: grading severity automatically, guiding root cause recommendations, and turning raw images into risk-prioritised, client-ready reports on-site.
This article looks at how real, working AI – not sci-fi – is transforming thermal inspection workflows.
The Real Bottlenecks in Thermal Inspection
Thermography has always been powerful, but the pain points are well known:
- Manual fault grading Thermographers interpret temperatures, load conditions and standards, then decide if a fault is minor, serious, or critical. It’s expert work – but also time-consuming and subjective.
- Inconsistent reporting between engineers Two different engineers might classify the same anomaly differently, especially under time pressure.
- Spreadsheet-heavy post-processing Calculating load-corrected temperatures, compiling fault tables, and building PDFs can take hours after every site visit.
- Limited use of historical data Older methods rarely take full advantage of trending – how a component’s temperature and severity change over successive inspections.
AI – done properly – addresses these exact problems.
What “Practical AI” Should Mean in Thermography
In thermal inspections, AI doesn’t need to “replace the thermographer”. Instead, it should:
- Standardise how risk is graded Given the same inputs (measured temperature, ambient, load, component type, rating), the system should always produce the same severity classification.
- Embed expert rules and standards The logic should reflect relevant guidance like BS 7671 and ISO 18436-7, not just arbitrary thresholds.
- Use historical data intelligently AI should highlight trends – components that are heating up faster than before, or faults whose severity is escalating.
- Automate repetitive, text-heavy tasks AI should assist with fault descriptions, suspected causes, and remedial recommendations using a consistent knowledge base.
- Keep the thermographer in the loop The engineer should still make the final call, but with better, faster decision support.
That’s exactly the design philosophy behind TICOR and SnapCor.
The Engine Behind SnapCor: Algorithms + Knowledge-Based Libraries
TICOR, developed by Ti Thermal Imaging Ltd, was one of the first Android-based thermal imaging reporting applications to bring automatic problem grading into the field.
Key capabilities that underpin SnapCor include:
- Automatic problem grading Faults are automatically graded into Minor, Important, Serious, or Critical, based on formulas that combine load, temperature, and component rating.
- Knowledge-based library (KBL) A pre-programmed library of root causes and remedial actions allows anomalies to be classified and described consistently via dropdowns instead of free-typing everything from scratch.
- Unique formulas for different inspection types
These logic layers are not just “AI” in a vague sense; they’re structured, rules-based systems built on years of inspection data and standards. SnapCor builds on this engine and adds modern AI assistance on top.
How SnapCor Uses AI in Day-to-Day Thermal Inspections
SnapCor is designed to transform how inspections are captured, completed, and delivered, while still working with any thermal camera and supporting instant on-site reporting.
Here’s where AI shows up in a practical way.
1. Automatic Problem Severity on Every Fault
When a thermographer records an anomaly in SnapCor, they capture:
- Thermal and visual images
- Measured component and reference temperatures
- Ambient conditions and load
- Component rating and type
SnapCor then:
- Applies load correction or thermal indexing based on inspection type (electrical vs building).
- Compares the corrected temperature rise against built-in thresholds that mirror real-world best practice and standards.
- Assigns a severity band – Minor, Important, Serious, or Critical – automatically.
Instead of manually cross-referencing charts or spreadsheets, thermographers see an instant, consistent severity recommendation for each fault.
You still remain in control: if site-specific conditions justify a different rating, you can override it – but the system gives you a defensible starting point in seconds.
2. AI-Assisted Root Cause & Remedial Suggestions
Beyond severity, SnapCor leverages the TICOR knowledge-based library and AI assistance to speed up narrative work:
- Suggesting probable root causes (e.g., loose termination, overload, design issue, insulation breakdown).
- Proposing standard remedial actions (e.g., re-make terminations, uprate components, investigate load distribution, repair or replace affected section).
Instead of rewriting similar paragraphs for every report, engineers can:
- Select from pre-built, best-practice phrases
- Add job-specific detail where necessary
This keeps reports professional, consistent, and audit-ready, even across large teams.
3. Turning Trend Data into Actionable Insight
AI is most powerful when it has history to work with. SnapCor and WebCor enable trending at the level of individual components and fault types:
- Each new inspection adds data points for component temperature, load, and severity.
- The system can highlight where:
This is very similar to how research-grade AI models are used in other thermal applications, such as PV fault detection or vacuum insulated glazing quality control, where deep learning models detect patterns and anomalies across thousands of IR images.
For the thermographer, this means:
- Easier prioritisation of remedial work
- Stronger justification for capital decisions (e.g., replacement vs repair)
- Clear visual evidence when presenting long-term risk to stakeholders
4. Smarter Reports, Generated On-Site
Because severity grading, root cause, and remedials are handled by AI and knowledge-based logic as you go, the final report is largely assembled automatically:
- Summary tables grouped by severity and location
- Fault pages with consistent narrative, images, and data tables
- Trending graphs for repeat inspections
- Inventories auto-generated from the assets you inspected
In practice, a multi-page, fully formatted PDF report can be generated on-site in under a minute from the same device used for data capture – rather than after hours of office work.
This is where SnapCor becomes the “smart” alternative to old-school methods: it doesn’t just digitise your clipboard; it actively thinks along with you.
SnapCor vs Old-School Reporting: What Changes?
The result is not fewer thermographers – it’s more effective thermographers, with more time for analysis and client interaction.
Where Human Expertise Still Matters
Even with AI, thermography is not a “push-button” activity. Human judgment remains critical:
- Contextual decisions AI can’t see everything – e.g., a component that’s “hot” on paper may be acceptable given specific design, load or ambient conditions.
- Understanding operational reality Only the engineer and client know what’s critical for that particular plant, data centre, or facility.
- Interpreting complex or rare anomalies AI and knowledge libraries are trained on patterns they’ve seen before; unusual conditions still need expert interpretation.
SnapCor is built to keep the thermographer firmly in the loop, offering AI as a co-pilot – not a replacement.
How to Bring AI into Your Thermal Inspection Workflow
If you’re running thermographic inspections today and still relying on manual reporting, here’s a practical path forward:
- Start with one inspection type Begin with electrical thermography (panels, MCCs, UPS, etc.), where load correction and severity grading deliver fast wins.
- Standardise templates & severity rules Agree on how your organisation wants to treat severity bands, and align SnapCor’s configuration accordingly.
- Pilot SnapCor on a single site or client Run SnapCor in parallel with your existing reporting for one cycle, compare grading, reporting time, and client feedback.
- Train your team on AI features Make sure thermographers understand why the system grades a fault as Serious vs Important, and when it’s appropriate to override.
- Roll out WebCor for trending and management Centralise inspections across clients and sites to unlock the full value of historical data.
Conclusion: AI That Makes Thermographers Faster, Not Redundant
AI in thermal imaging doesn’t have to mean black-box predictions or replacing skilled engineers.
Used correctly, it means:
- Automatic, standardised problem severity
- Faster, more consistent reporting
- Better use of historical data and trending
- More time spent analysing and advising, less time formatting reports
SnapCor, powered by TICOR’s proven thermographic engine, brings this practical AI to the field – turning thermal inspections into a smarter, faster, and more defensible part of your reliability and maintenance strategy.
If you’re ready to move beyond manual spreadsheets and subjective grading, SnapCor is built to be your AI-smart upgrade to traditional thermal reporting.
