TREN

SaaS · Scope to be defined during discovery

AI System for Extracting Issue Context from Support Screenshots

We design an AI system that reviews a support message, screenshot and software version together with source and freshness information. The system prepares issue context and questions about missing information but does not treat them as final decisions. If personal data is present or the issue is unclear, the suggestion is not put into effect; the support representative makes the final assessment.

Representative support content analysis panel with a work queue, checks and an audit trail: screenshot context queue — **Screenshot context queue** Representative interface — contains no real data. Account details are masked. The panel does not send responses or close support cases.

The problem

In day-to-day operations, support messages, screenshots and software versions arrive in separate records and files, preventing the team from reviewing the same case in one place.

As a result, the link to the source can be lost while issue context and questions about missing information are prepared, leading to repeated checks and uncertainty about the basis for the decision.

Incomplete automation may overlook personal data or an unclear issue, while poorly controlled automation creates a risk of an incorrect issue assessment.

What we set out to improve

Time taken for the draft issue context and missing-information questions to be ready for review
Accurate routing of records containing personal data or an unclear issue to the correct specialist queue
Recording of suggestions amended or rejected by the support representative

How the system works

01 Retrieve support-message, screenshot and software-version data from an approved source
02 Verify the source system, record identifier and freshness information
03 Transform the fields into the target process schema
04 Prepare draft issue context and missing-information questions
05 Compare the draft with business rules and source records
06 Exception check: personal data is present or the issue is unclear
07 Support representative approval, amendment or rejection
08 Write the input, suggestion, amendment and final decision to the audit log

Methods we used

Source-traceable data extraction for the support message, screenshot and software version
Structured output that restricts the issue-context and missing-information-question fields through business rules
An exception gate that separates records containing personal data or an unclear issue
A queue that shows the source and suggestion together for the support representative
Duplicate-processing and rollback records to manage the risk of an incorrect issue assessment

Where people stay involved

The support representative is the final decision-maker. If personal data is present or the issue is unclear, the workflow stops and the case is presented to the specialist with the supporting records. The specialist can amend or reject the suggestion, or stop the process.

Data and security

Only the fields required for the task are processed from the support message, screenshot and software version. Source-system permissions are preserved, and personal and commercially sensitive data is minimised before being passed to the model. Every read, suggestion, human amendment and write to the target system is logged.

Who this suits

A good fit

Teams carrying out repetitive reviews in SaaS operations
Companies with defined ownership for sources and approvals
Organisations that want exceptions to remain under human oversight

Not a good fit

When source data is not current
When no decision owner or rollback process has been defined
When the existing method is sufficient for the low volume involved

Frequently asked questions

What data is used?

A support message, screenshot and software version are used. The exact connections, fields and retention limits are defined during the access review.

How are exceptions handled?

If personal data is present or the issue is unclear, the workflow stops. The record enters the support representative's queue with source evidence.

How is poorly controlled automation limited?

Because the issue could be assessed incorrectly, no final action is taken without human approval. The source, suggestion and human decision are recorded separately.

Project led by:DijitalPi

✦ PI ASSISTANT · ARTIFICIAL INTELLIGENCE

online

Ask the DijitalPi AI Assistant your question

No need to fill in a form and wait for a reply — write your question and let Pi, trained on DijitalPi's 20 years of know-how, answer within seconds.

Instant answers, 24/7
Service-specific context — no generic replies
Can connect you to a free consultation if you wish

Hi! 👋 I'm Pi — DijitalPi's AI assistant. Write whatever you'd like to know about AI System for Extracting Issue Context from Support Screenshots and I'll answer right away.

Pi Assistant answers are for information purposes; for a proposal get in touch.

ASK AI ABOUT DIJITALPI

Let an AI explain what DijitalPi does.

Opens your chosen assistant with a ready research prompt. It reads the site live and answers.

ChatGPT Claude Gemini Grok Perplexity