KoltrixCorrecting misclassified email: building a feedback loop
A playbook for fixing AI email sorting mistakes: move the message, spot the pattern, add rules only when they are exact, and keep a weekly misfile log.
On this page(9 sections)
Every AI sorting system misfiles mail sometimes. What separates a setup that gets better over time from one that slowly loses the team's trust is what happens after each mistake.
Misclassification usually gets handled the same way: someone notices, sighs, drags the email to the right place and moves on. The next week, the same sender lands in the wrong place again. This playbook turns those one-off corrections into a loop that actually reduces errors.
Play 1: Fix the message first
Start with the obvious. When you spot a misfiled email, move it to where it belongs right away. A customer email sitting in a newsletter pile is a customer waiting; the root-cause analysis can wait five minutes.
If the misfile delayed a reply, acknowledge it in your response if the delay was noticeable. A short "Sorry for the slow reply, this one got filed in the wrong place on our side" is honest and costs nothing.
Play 2: Ask one question: is this a pattern?
Before doing anything else, ask whether the mistake is a one-off or likely to repeat. Three quick checks:
- Is it the same sender as before? Search for other messages from that address or domain. Where did they land?
- Is it the same kind of message? For example, every failed-payment notice from your billing provider, or every reply from customers using personal addresses.
- Would a reasonable person have made the same mistake? Some emails are genuinely ambiguous. An investor update sent through a newsletter platform looks exactly like a newsletter.
If the answer to all three is "no," it was probably a one-off. Move on. If any is "yes," continue.
Play 3: Log it in a misfile log
Patterns are hard to see one email at a time. Keep a simple shared log, a spreadsheet or a doc, where anyone records misfiles worth noting:
| Date | Sender / domain | Was filed as | Should be | Cost | Notes |
|---|---|---|---|---|---|
| Mon | [email protected] | Updates | Primary | High: failed charge | Third time this month |
| Tue | [email protected] | Cold pitches | Primary | High: existing customer | Personal address |
| Wed | [email protected] | Primary | Newsletters | Low | New community digest |
The "cost" column matters most. A newsletter leaking into Primary is mildly annoying. A customer hidden in Cold pitches can cost you the customer. Fix high-cost patterns first.
Keep the log lightweight. If logging takes more than 30 seconds, people will stop doing it.
Play 4: Use a rule when the pattern is exact
Some patterns are deterministic: a specific sender should always land in a specific place. Those are perfect for rules.
Good candidates for rules:
- A known system address that sends important alerts (billing, security, uptime)
- A key customer or partner domain that must always be seen
- A vendor whose notifications always belong in one label
Rules are predictable and explainable, which is exactly what you want for mail where mistakes are expensive. In Koltrix, for example, labels and auto-label rules let you pin known senders to a label regardless of what the AI would have guessed.
Write rules narrowly. "Anything from payments.example goes to Primary" is fine if that domain only sends alerts you need. If it also sends marketing, the rule will drag newsletters into your main view.
Play 5: Don't use rules for fuzzy patterns
The temptation after a few misfiles is to write a rule for everything. That's how rule sets turn into an unmaintainable mess.
Avoid rules when:
- The pattern is about meaning, not sender. "Emails that ask about pricing" is a classification problem, not a rule.
- The sender varies. Customers on personal addresses can't be captured by a domain rule.
- You'd need exceptions to the exception. If a rule needs three "unless" clauses, it's the wrong tool.
For fuzzy patterns, the better fix is usually a habit: someone skims the lower-priority categories once a day for anything that looks human. That catches what neither rules nor models will reliably get.
Play 6: Review the log weekly
Once a week, spend ten minutes on the misfile log. Look for:
- Repeats. The same sender appearing twice or more is a rule candidate.
- Clusters by category. If most high-cost misfiles land in the same category, that category may be too broad, or someone should check it more often.
- New senders. A new tool or newsletter often causes a burst of misfiles in its first week. A single rule usually fixes it.
- Rules that caused problems. Sometimes the misfile was caused by a rule you added earlier. Narrow or delete it.
Clear the reviewed entries, or mark them done, so the log stays readable.
Play 7: Watch for overfitting
Overfitting means adding so many corrections that the system works perfectly for last month's mail and badly for next month's. Signs you're there:
- Dozens of single-sender rules nobody remembers creating
- Rules that contradict each other
- New team members can't predict where mail will land
When this happens, prune. Delete rules for senders who haven't emailed in months. Merge rules that do the same thing. Keep only the ones that protect high-cost mail.
A minimal loop to start with
If all of this feels like a lot, start with just three steps:
- Move misfiled mail immediately
- Log only high-cost misfiles (real people or action-needed alerts hidden)
- Every Friday, turn any repeated sender in the log into a rule
That alone fixes most recurring problems within a few weeks.
Key takeaways
- Fix the message first, then ask whether it's a pattern.
- Keep a lightweight misfile log with a cost column, and fix high-cost patterns first.
- Use rules for exact, sender-based patterns; use habits for fuzzy ones.
- Review weekly, and prune rules before they overfit.
- A small, consistent loop beats a big cleanup once a year.
Start with Koltrix
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