Most teams migrate to Freshdesk, turn on Freddy, and wonder why accuracy is low. The answer is almost always the same: the data went in the wrong order.
Why Freddy fails after most migrations
Freddy AI draws on two sources to do its job: your Knowledge Base and your closed-ticket history. Both matter, but the sequence in which they arrive on the platform matters just as much as the content itself.
If you migrate tickets before your KB articles are in place, Freddy starts indexing ticket content that references articles it cannot yet retrieve. The AI routes intent correctly but generates responses from a blank KB — or worse, hallucinates content that isn't there.
If you migrate unresolved or low-rated tickets, the problem runs deeper. Freddy's auto-triage and article suggestion features learn from the interactions you feed them. Open tickets have no resolution, so they add noise rather than signal. Low-CSAT tickets teach Freddy the patterns behind your team's worst responses — the apologies, the workarounds, the ones where something went sideways. That training data sticks.
The result is an AI that confidently produces inaccurate suggestions and triage classifications from day one, and accuracy doesn't improve until you intervene manually.
The 3-step fix
1. KB articles first — every article, every language version
Before you migrate a single ticket, your entire Knowledge Base must be in place on the target platform. Every article. Every language version. Every category.
This is the most impactful step in the migration because the KB is Freddy's primary source for retrieval. Ticket history informs intent detection; KB content determines what Freddy actually says in response. Miss this sequencing, and all the work you do on ticket quality is largely irrelevant.
The rule is simple: KB first, tickets second. No exceptions at this stage.
2. Filter tickets: resolved and closed only, CSAT ≥ 4 stars
Once your KB is in place, apply two hard filters to your ticket migration:
- Status: Closed and Resolved only. Open and pending tickets lack a confirmed resolution outcome. They're mid-conversation. Freddy cannot extract a successful pattern from an unfinished interaction.
- CSAT score: 4 stars and above. High-rated interactions represent your team at its best — the clean resolutions, the efficient routing, the answers that actually helped. When Freddy learns from those interactions, it learns your best practices, not your exceptions.
One caveat on CSAT: if your team collected ratings sporadically and you only have a thin sample at 4+, skip the filter. A solid dataset of resolved tickets without CSAT data beats a handful of filtered interactions that aren't representative.
Also, tighten your date range to the last 12–18 months. Tickets from three or four years ago reference legacy pricing, deprecated features, and old policies. Training Freddy on those resolutions produces accurate answers to questions your customers stopped asking.
3. Validate before go-live: run a 50-ticket spot check
Before you flip the switch, test what you've built. Take 50 tickets that were not included in your filtered migration dataset and run them through Freddy on the target platform.
You're checking two things:
- Intent detection accuracy: target ≥ 85%. Below 80%, pause and audit your KB before proceeding.
- KB hallucinations: zero tolerance. If Freddy references content that doesn't exist in your articles, the KB is either incomplete or hasn't finished indexing.
Only complete the cutover when you hit both thresholds. This spot check takes an hour and prevents weeks of accuracy remediation after go-live.
What to do after Freddy is stable
Once Freddy clears the accuracy threshold, run your full historical migration — the remaining tickets, contacts, companies, and attachments that didn't make it into the filtered dataset.
Historical records aren't there to train Freddy. They're there for your agents. Your team will encounter customers who reference conversations from two or three years ago, and without that history in the new platform, agents are reconstructing context mid-conversation. Compliance requirements often mandate retaining records for defined periods.
The distinction matters because it defines the sequence. Historical data is valuable — just not for AI training. Migrating it after you've validated Freddy's accuracy baseline means it doesn't distort the intent patterns you've already confirmed are working.
That's the whole logic: clean AI foundation first, complete historical record second. Run them in order, and Freddy inherits sharp training data on day one.
One tool worth knowing
If you're planning a Freshdesk migration, Help Desk Migration handles KB-first sequencing automatically — including all language versions with no manual re-import. Run a free Demo Migration to see exactly how your data maps before you commit.
Has anyone here run into Freddy accuracy issues post-migration? Curious what you found during your root cause audit.
