AI Filtering
Sift's AI filtering automatically evaluates every inbound message for relevance and actionability, so your queue only surfaces conversations that actually need a response.
At a glance
| Auto-evaluates every inbound message for relevance and actionability | ✅ |
| Closes noise: generic praise, off-topic chatter, emoji-only, spam, duplicates | ✅ |
| Disabled strategy | ❌ no auto-close, all manual review |
| Conservative strategy | ✅ closes only at very high confidence |
| Standard strategy (recommended) | ✅ closes low-relevance content at high confidence |
| Aggressive strategy | ✅ closes broadly at lower confidence |
Exempt conversations with the SiftIgnoreActionability tag | ✅ |
| Affects historical items | ❌ new inbound only |
How it works
Messages that are clearly noise - generic praise, off-topic chatter, emoji-only reactions, or low-signal likes - are closed automatically. Anything that looks like a genuine question, complaint, or request is left open for your team.
What gets filtered
- Generic positive reactions with no actionable content
- Off-topic mentions that reference your brand but need no reply
- Duplicate or near-duplicate messages in a thread
- Spam and bot-generated content
How sensitivity is controlled
Sift offers four filtering strategies, configurable per org:
- Disabled: no auto-close; every item requires manual review
- Conservative: only closes when the AI has very high confidence the message is irrelevant
- Standard (recommended) - closes low-relevance and low-actionability content at high confidence
- Aggressive: closes more broadly with lower confidence requirements
You can also tag specific conversations with the SiftIgnoreActionability tag group to permanently exempt certain message patterns from filtering - useful for VIP accounts or escalation queues where you always want human eyes.
Auto-close patterns
Above the strategy selector, Sift surfaces learned auto-close patterns - recurring message shapes it's noticed getting closed the same way over time - so you can review and approve them as an additional, targeted auto-close rule alongside the strategy-driven one.
There's also a separate Auto-close spam and scam content toggle under Additional options, distinct from the four strategies above: it targets spam/scam specifically rather than general noise.
Setting it up
Go to Settings → AI → Triage. Choose the strategy that fits your team's volume and risk tolerance. Changes take effect immediately for new inbound messages.
Beyond auto-close: user rules and signal flags
Triage also holds two related signal-detection tools that don't affect auto-close:
- User rules tell Sift what to watch for on people. When a rule matches, Sift tags the user during its nightly analysis so you can filter on it. A built-in impersonator-detection rule ships on by default; add your own to watch for anything else.
- Signal flags tell Sift what to watch for in content. A match applies a signal tag during synthesis for filtering and reporting - flags never affect scoring or auto-close.