Is your Help Center deflecting tickets, or quietly creating more work?
We read your support tickets, find the repeat questions your Help Center keeps missing, and hand your team review-ready FAQ drafts.
Upload 3 months of tickets. In 24 hours, get the repeat questions ranked, the missing customer wording surfaced, and review-ready FAQ drafts built from resolved replies.
Stop paying agents to act like search engines.
Repeat tickets keep coming back for one simple reason: your answers never reach the next customer. Gartner data is clear: 73% of customers attempt self-service, but only 14% succeed. The answer often exists, but it is not written in the words customers actually search for. So they miss it, open a ticket, and your team answers something your help center should have handled.
The loop destroying your queue
- The miss: A customer searches their exact problem, finds nothing useful, and opens a ticket.
- The waste: Your team answers it manually. The fix gets buried in a private support thread.
- The repeat: The same question comes back next week. Another agent repeats the work.
Industry benchmarks show that 40% to 60% of support inbox volume is repetitive questions. Every one of those tickets is more than a support request. It is evidence of the customer wording your help center is missing.
Repeat questions are bleeding your support budget.
That wording gap is not a content problem. It is a cost leak. Gartner benchmarks self-service at $1.84 versus $13.50 for an assisted contact. That is $11.66 more every time a repeat question your help center could have answered reaches a person instead.
Run that against your own repeat volume. The cost is real, not theoretical.
This is not just about money. It is what repeat work does to the team:
- Agents spend only 39% of their time actually servicing customers, per Salesforce.
- Five hours a week disappear into repetitive tickets, per Gorgias.
- The same grind contributes to burnout, low morale, and the turnover costs leaders keep budgeting around, per Insignia.
Customers feel the cost too. CEB found 94% of low-effort customers intend to repurchase, compared with only 4% of high-effort customers (The Effortless Experience). Every failed search adds effort before the conversation even starts.
The Support Tax Calculator
Estimate how much repeat Tier-1 work is quietly costing you each month, because your help center doesn't use the words your customers actually search with.
Tune the inputs
Move the sliders or type exact numbers. The math updates as you go.
Industry average assumptions
Repeat tickets
40%
Simple, repetitive Tier-1 how-to questions.
Avg. touch time
12 min
Equivalent to 0.2 support hours per ticket.
You probably already tried the obvious things.
More agents, a bigger knowledge base, macros, chatbots, outsourcing. None of those fail because the team is careless. They fail because they do not close the loop between what customers ask and what your help center actually says.
The current way
Scan tags. Poll agents. Chase the loudest complaint.
Support Ticket Deflection
Upload a CSV. Get a ranked fix list in 24 hours, ordered by how often each question reaches support.
The current way
Write docs from internal assumptions and product jargon.
Support Ticket Deflection
Write docs from the exact words customers already use in tickets and on Google.
The current way
Guess which fixes matter most.
Support Ticket Deflection
Rank fixes by queue frequency, not opinion.
The current way
Hope customers find the answer.
Support Ticket Deflection
Close the wording gap so Google and your help center can actually match the query.
The current way
Answers disappear back into the queue.
Support Ticket Deflection
Every recommendation links back to source tickets and quoted evidence.
The current way
Macros and bots go stale, then send the wrong article.
Support Ticket Deflection
You review every draft. Nothing goes live without you.
The current way
Support Ticket Deflection
The mechanism here is extraction, not automation. Support Ticket Deflection does not answer tickets, modify your help desk, or auto-publish anything. It reads the queue, finds the repeated questions, and hands you a prioritized publishing plan.
Three steps. No rollout. No new platform.
Export a CSV from Zendesk, Intercom, Help Scout, or any desk that gives you one. That file already contains the language customers use, the answers agents write, and the evidence needed to rank what should be fixed first.
Within 24 hours you get a ranked fix list: every repeat-question cluster ordered by frequency, customer-term-to-doc-term gaps, source-ticket evidence, a drafted FAQ for every gap your tickets already answer, and a flagged list of the ones they do not. You review, edit, and publish what you approve.
From inbox history to publishable self-service.
The report isolates the questions worth fixing, shows the exact wording gap behind each one, and returns drafts your team can ship without starting from a blank page.
Try the report logic on a sample repeat question.
Search the sample dataset to see a report-style finding: customer wording, the documentation gap, source-ticket evidence, and the FAQ draft your team would review before publishing.
Pick a question above. You'll see the kind of report finding your team would review: customer wording, term mappings, source-ticket evidence, and a drafted FAQ.
You get a list of data-backed fixes for your most expensive repeat questions.
The report pinpoints which repeat questions are draining your team’s support time, ranked by your own ticket history.
For each question, you’ll see:
- Customer wording: the exact phrases customers use.
- Documentation gap: where your current answers fall short.
- Source tickets: the evidence behind each finding.
- FAQ draft or no-proven-answer flag: for questions your tickets already solve, ready-to-review answers built from resolved replies. If tickets do not support an answer, the report marks “no proven answer yet” instead. The output is 100% deterministic, with no LLM-generated answers.
Best fit: support leads at 15–75-person B2B SaaS companies with an exportable help desk and a help center they control. Not a fit if you want a live-answering bot or an enterprise implementation program.
Every answer you publish is a page written in the words people actually search.
The phrases your customers use in tickets are the phrases they type into Google, and your own volume tells you which ones matter most. Today your help-center page misses on wording: it answers in your internal product language, not the words customers search. We hand you their own search wording, validated by your queue, so the answer finally gets published in the same words customers search, not your internal jargon.
Service leaders surveyed by Gartner estimate that as much as 40% of the issues reaching a live agent could have been resolved in self-service, if the answer existed and was findable. Findable is the whole game, and findable means matching the words people search.
And it is an asset, not a one-off reply. A published answer sits there for every future customer who searches that question, one page doing work your team would otherwise repeat by hand, so the next person with that question can find the answer instead of writing in.
You review and publish every word, so the visibility is yours to earn. We supply the keywords and the drafts; what gets found is the work you ship.
Proof comes from the queue first, and the benchmarks second.
Every recommendation links to a real ticket.
Not a model guess. Not a synthetic summary. The report points back to the original ticket language and the quote that produced the finding.
The queue sets the priority.
Fixes are frequency-ranked by how often they hit support, not by which complaint felt loudest in Slack this week.
The draft is grounded in answers your team already used.
The FAQ draft is assembled from real responses that already resolved the issue for a real customer. You are publishing proven language, not inventing it.
Industry benchmarks that support the argument
- Gartner: $1.84 self-service vs. $13.50 assisted.
- SQM Group: a 1% improvement in FCR reduces operating costs by 1%.
“While 73% of customers use self-service at some point in their customer service journey, it’s concerning to see that so few fully resolve there.”
Klarna’s public AI rollout is useful for one reason: the walk-back. The company proved that cost-focused deflection without quality creates a different kind of support problem. That is why this product stops at extraction and drafting. Human review stays in the loop.
The risk is deliberately capped.
- You control everything. Nothing goes live without your approval. The system does not publish or touch your help center.
- Every finding is verifiable. If it appears in the report, it is backed by a real ticket and a real quote.
- No hidden costs or surprises. No per-resolution pricing, no hidden rollout, and no AI talking to your customers.
- Simple workflow: upload a CSV, receive the report, review the drafts, publish what you approve.
Not a chatbot. Not a replacement for agents. Not a black-box content writer. This is a ticket-history analysis that turns repeated questions into reviewed self-service.
Find the gaps fueling your ticket volume.
Every day you wait, your team spends hours answering the same preventable questions. Upload your CSV today, and within 24 hours, you will see exactly which repeat questions and wording gaps are costing you the most time, complete with publishable drafts.
If the repetition is not there, the data will prove it. If it is, you will have a prioritized list of exactly what to publish first to start clearing the repeats.
Privacy: we delete your CSV after 30 days. No model training, no third-party sharing, no fine-tuning.
Start with the snapshot. Upgrade when the repeat pattern is clear.
The free snapshot shows whether your tickets contain enough repeated questions to justify the full report. If the pattern is real, the full report gives your team the ranked questions, customer wording, documentation gaps, source evidence, and review-ready drafts to publish first.
Deflection Snapshot
Delivered in 24 hours after CSV upload
Upload your last 3 months of tickets. We send back enough to show you the pattern: the repeat questions, customer wording, and one self-service answer so you can see if the full report is worth doing.
- Your top 5 repeat questions, ranked by how often they were asked
- Customer wording examples
- 1 sample self-service answer
- No card required, no contract
The free snapshot proves whether the pattern is there. It is not the full report.
Get the free snapshotFull Deflection Report
For the first 3 month batch. We turn the repeat questions into a full Support Ticket Deflection Report your team can use to decide what to fix and publish first.
- Every recurring question, ranked by how often it was asked (typically 50+)
- Customer wording clusters, the long-tail keywords needed to rank
- A drafted, publishable answer for every gap your tickets already solve, your team's own resolved replies, 100% deterministic, no AI
- A "no proven answer yet" list, the frequent questions you have not cracked
- Priority ranking and source ticket IDs on every finding
This is the paid version of the work: enough detail to actually update the help center.
Start the full reportQuarterly Refresh
Run the report every 90 days so your help center keeps up as customer questions change. Good for teams that keep seeing new repeat issues.
- Full Deflection Report every 90 days
- What changed since the last report
- Questions that are still coming back
- New self-service answers to review and publish
- Cancel any time after the next report
Best after the first full report proves the work is useful.
Keep it updated- No help-center integration. Your team publishes in the tool you already use.
- No auto-publishing. You review, edit, and approve every FAQ.
- No guaranteed deflection percentage. The report identifies the highest-priority opportunities first.
Questions support leads usually ask before uploading tickets.
The mechanics are deliberately simple: export the CSV, receive the report, review the output, and publish what you approve.
You get your top 5 repeat questions ranked from your ticket history, examples of the exact customer wording, and one sample self-service answer. It is enough to show whether the repeat pattern is real before you pay for the full report. It is not the full report.
You get the working list: every recurring question ranked by volume, customer wording clusters, documentation gaps, source ticket IDs, review-ready drafts for gaps your tickets already solve, and a "no proven answer yet" list for frequent questions without enough answer evidence.
Three to six months of closed tickets is the sweet spot. A few hundred tickets is usually enough for the snapshot to show whether repeat patterns are there; more history helps the full report rank the repeats more confidently. If the export is too thin, we will say so.
Messy is expected. Customers do not ask in clean tags or perfect categories. We group tickets by what the customer was trying to do and the words they used, not by how neatly the export is labeled.
If your export tool can remove names, emails, phone numbers, or other private details, do that first, we recommend it. We do not need PII to find repeat questions. Your file is deleted after 30 days. The analysis is 100% deterministic, no AI, no model training, no fine-tuning, no sharing.
Because customers search in their own words. If support tickets say one thing and your help center says another, the answer can exist and still fail to surface. Customer wording closes that gap.
Maybe not, and the free snapshot will tell you. If customers still ask questions your updated docs already cover, the gap is usually wording or findability. If the snapshot does not find a repeat pattern, it will say so.
Plan on light review. Most teams confirm the steps, adjust tone, add product links, and approve the draft before publishing. You are not starting from a blank page, and nothing goes live without you.
Then we will tell you. The report works best when repeat questions show up clearly. If the export is too thin or too scattered to support a useful finding, we will not pretend there is a pattern.
No. The report does not touch Zendesk, Intercom, Help Scout, or your live queue. It works from a CSV export and hands your team reviewed self-service work to publish in the help center you already control.
No. Start with the free snapshot. If it shows a useful repeat-question pattern, you can pay for the full Deflection Report. Quarterly refreshes are only for teams that want to keep updating the help center as new repeat questions appear.
We identify the repeat questions customers ask most, capture the words they use, and draft self-service answers from replies your team already used. You review and publish what you approve, giving the next customer a better path before opening a ticket. No percentage is guaranteed.
Usually yes. A struggling knowledge base often has a wording problem, not just a content problem. The answer may exist, but customers cannot find it because the article uses internal product language. We use ticket language to show what needs to be rewritten or added.
Often because the answer exists in the wrong words or is missing from the places customers search. Gartner found 73% of customers try self-service first, but only 14% fully resolve there. Your tickets show the terms and questions your self-service layer is missing.
A chatbot answers customers in the moment and can guess wrong. This does not answer customers. It is a 100% deterministic analysis of past tickets that gives your team FAQ drafts and evidence to review. No AI talks to your customers here.
Privacy: we delete your CSV after 30 days. No model training, no third-party sharing, no fine-tuning.