Filtering Spam and Abusive Tickets
Public contact forms and open email addresses attract a steady stream of promotional spam, scraper-bot submissions, and occasionally genuinely abusive messages, all of which land in the same queue as real customer issues. Agents waste time opening and closing tickets that are obviously not support requests, and worse, abusive messages sometimes sit in a shared queue where an agent has to deal with hostile content with no warning or support. During promotional pushes or after a public incident, spam volume can spike enough to bury genuine tickets several pages deep in the queue.
STARTING PRICE
From €99
Starter tier · Single-workflow automation, one core integration, fast turnaround.
Get a quote →Saves roughly 1-3 hrs/week, more during spam spikes.
How the automation works
We add a filtering layer that runs before a ticket reaches any queue, classifying incoming messages as genuine, promotional spam, bot-generated, or abusive based on content patterns, sender reputation, and submission behaviour (rate of submission, missing account association, known spam-domain senders). Spam and clearly bot-generated tickets are auto-archived with a log kept for audit, abusive tickets are routed to a senior agent with a content warning and de-escalation guidance attached rather than the raw hostile text being the first thing a junior agent reads, and everything else flows through to the normal queue untouched.
Process flow
- 01
Ticket submitted trigger
Every new ticket from a public-facing channel (contact form, open email) is scanned before it's added to any agent-visible queue.
- 02
Classify content type ai
The model checks for promotional/spam patterns, bot-submission signatures, and abusive or threatening language, distinct from normal customer frustration.
- 03
Check sender signals integration
Submission rate, account association, and sender-domain reputation are checked to corroborate the content classification and reduce false positives on genuine urgent messages.
- 04
Archive, escalate, or pass through output
Spam and bot tickets are auto-archived with an audit log; abusive tickets route to a senior agent with a content warning; everything else passes through untouched to the normal queue.
Inputs
- Incoming ticket content
- Sender email/account association
- Submission rate and history
Outputs
- Spam ticket archive with audit log
- Abuse-flagged tickets routed to senior agents
- Clean agent-facing queue
- Reduced noise in ticket volume reporting
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- A genuinely angry customer using strong language about a real problem is not the same as abuse — over-aggressive abuse filtering risks routing legitimate high-frustration tickets away from normal handling instead of prioritising them, which makes a bad experience worse.
- Spam filters trained mainly on promotional content miss targeted bot abuse (fake refund-request floods, credential-stuffing-adjacent account queries), which look structurally different from generic spam and need their own detection pattern.
- Auto-archiving without a retained, searchable log removes the ability to spot a coordinated abuse campaign or a spam pattern change over time — archived items need to stay queryable, not deleted outright.
Frequently asked questions
Could this accidentally archive a real customer ticket?
It's tuned conservatively — anything ambiguous passes through to the normal queue rather than being archived, and the archive log is reviewable so misclassifications can be corrected and fed back into tuning.
How does it handle abusive tickets differently from spam?
Abusive tickets aren't hidden — they're routed to a senior or trained agent with a content warning and suggested de-escalation approach, since these still need a human response, just not a junior agent's first exposure.
Does this need training on our specific spam patterns?
It starts with general spam/bot/abuse patterns and improves as it sees your actual ticket stream, particularly useful for catching recurring abuse patterns specific to your product or community.