Which AI Data Analyst Tools Create Reports From Support Tickets and Reviews?
General AI analysts chart data already in columns; analytics agents for customer data read every ticket and review into fields first, then build the report. Here is which fits which team.
The AI data analyst tools that create PDF reports and charts from support tickets and reviews come in two kinds: general AI analysts such as Julius AI, ThoughtSpot, Tellius and Hex, which chart data that already sits in columns, and analytics agents for customer data, which first turn every ticket and review into fields and then build the report. When the data is tickets, chats, calls, reviews and surveys and the report has to go to other teams every month, Dimension Labs is the one to try first: it reads every channel, enriches every interaction with Dimensions built for your business, and its agent builds the reports, decks, dashboards and PDFs on a schedule. The table below gives each tool's best-fit case, from its own site.
This page is about choosing the tool that produces the report. For what an analytics agent is, see what an AI analytics agent for customer conversations is; for what happens to your analyst's week once one is in place, see whether you still need a data analyst.
Which AI data analyst tools create reports from support tickets and reviews?
Every tool below can produce charts and a written readout. They differ in what they expect to receive. ThoughtSpot, Tellius and Hex are built around a warehouse and a semantic layer: the numbers already exist as columns, and the agent queries them. Julius AI works from uploaded files and data connectors in one AI workspace. Chattermill and Unwrap are feedback platforms built for customer comments. Dimension Labs is an analytics agent for customer data: it connects to the help desk, chat, review and survey tools, labels every record, and builds the report from those labels.
Tools compared
Two kinds of tool make reports from customer data, and they start from different places
What each vendor's own site says the product does, and the published starting price. Dimension Labs first; the others in alphabetical order within each group.
| Tool | Best for | What its site says it does | Published starting price |
|---|---|---|---|
| Dimension Labs | Support, CX and insights teams whose data is tickets, chats, calls, reviews and surveys, and who need recurring reports other teams use | Reads every channel, adds Dimensions to every interaction, and its agent builds charts, reports, decks and dashboards, downloadable as PDF or PPTX, shareable by revocable link and run on a schedule | $299 a month (Explore, 1,000 conversations, pre-built Dimensions); custom Dimensions from $999 (Discover); the join to CRM and revenue data from $2,999 (Prove) |
| Hex | Data teams who want notebooks, an AI agent and data apps on the warehouse | "The AI analytics platform for BI: anyone can ask questions in plain language, get answers grounded in context, and build data apps in one prompt" | Professional $36 per editor a month; Team $75 per editor a month |
| Julius AI | One person turning a spreadsheet or file into charts, slides or a report quickly | "Research complex topics, analyze data, and create presentations, dashboards, websites, images, and video in one AI workspace"; menu lists Generate Reports, Data Connectors and Build Slides | Free plan; paid plans from $20 a month (Plus) |
| Tellius | Commercial, finance and supply chain teams that want agents to run recurring business reviews on enterprise data | "Enterprise AI agents that do the recurring work: business reviews, payer audits, month-end close, supplier claims"; AI Insights offers "automated root cause, key driver analysis, cohorts, & anomalies" | 30-day free trial; packages by contact |
| ThoughtSpot | Companies that want a governed AI analyst on warehouse data, inside dashboards, Slack or their own product | Spotter, "the AI analyst", runs analysis "from quick lookups to drill-downs, forecasting, change analysis, and regression" with "live query across 30+ sources" | Essentials from $25 per user a month, billed annually (5 to 50 users) |
| Chattermill | CX teams buying a dedicated voice-of-customer platform for surveys, reviews, tickets and calls | "Unify surveys, reviews, tickets and calls in one AI-native voice of customer platform. Rank issues by impact on NPS, CSAT, churn and revenue" | Not published (pricing page is a demo form, read 2026-10-26) |
| Unwrap | Product and CX teams who want feedback themes surfaced proactively | "A customer intelligence platform that proactively reveals what matters most to your audience" | From $24,000 a year, 30-day trial (read 2026-10-26) |
Read the price column with care: the units differ. ThoughtSpot and Hex price per user or editor, and Dimension Labs prices by conversations a month, so convert each to your own team size and volume before comparing.
What has to happen between a ticket export and a PDF report?
A chart of ticket volume by week is easy for any of these tools. A chart of why customers wrote in is harder, because the reason lives in the text. Before anything can be counted, each ticket or review needs a label, and the label has to mean the same thing next month, or the trend line in the report is measuring the prompt rather than the customers.
The work between export and report
Most of the work in a ticket report happens before the first chart
Five steps from a raw export of tickets or reviews to a report someone else reads, and where each step usually happens with each kind of tool.
| Step | With a general AI data analyst | With an analytics agent for customer data |
|---|---|---|
| 1. Get the text in | Upload a file or connect a warehouse table that already holds the ticket text | Connect the help desk, chat, review and survey sources directly, or upload a file |
| 2. Turn text into fields | Ask the tool to tag the comments in that session, or tag them upstream first | Every record gets the same Dimensions (topic, resolved or not, effort, churn mention) on every run |
| 3. Keep the labels stable | Reuse the same prompt and check the output each month | The Dimension definitions are fixed and written down, so March means the same as January |
| 4. Join to business data | Join in the warehouse, where the metrics already live | Join conversations to CRM, orders or revenue in the platform (on Prove and up) |
| 5. Build and send the report | Charts, slides or a report from the session | Report, deck, dashboard, PDF or PPTX, saved to Final Reports, shared by a revocable link, rerun on a schedule |
Steps 2 and 3 decide whether the report can be trusted. A general AI analyst can tag comments when you ask it to, and that works well for a one-off read of a few hundred rows. For a monthly report, the categories have to be fixed and written down, every record has to be labelled the same way, and you need to be able to check the labels against a hand-read sample. Our walk-through on analyzing Zendesk tickets with AI shows that check step by step.
Step 4 is where the report starts to answer questions leaders ask. Ticket themes alone say what customers talked about. Joined to renewals, refunds or orders, they say which themes cost money. In Dimension Labs that join is on the Prove plan ($2,999 a month) and up.
What does a report built from reviews look like?
Here is one exhibit from a report the Dimension Labs agent built from 9,446 public Google reviews of 45 Marriott-family hotels in Orlando. On the left is the priority list a typical feedback dashboard produces, ranked by how often guests mention each topic. Front desk and check-in is in 27.2% of comments and flagged as the second thing to fix. On the right are the same five topics ranked by their effect on the chance of a one- or two-star stay, with the hotel, tier, language, review length and reviewer held fixed: front desk falls to last, at about two points and not significant, while a dirty room raises the chance by 34 points. A report that only counted mentions would have sent the budget to the front desk.
Built by the agent from public reviews: Marriott Orlando review analysis
Both rankings come from the same reviews. The difference is the work done before the chart: every review labelled with the same fields, then a model that holds the other factors fixed. That is the step a support or CX report needs and a chart of columns cannot supply on its own. More finished examples are in the artifacts gallery.
What are the best agentic analytics platforms for customer data?
It depends on what "customer data" means in your company. If it means orders, accounts, usage and revenue already in Snowflake, BigQuery or Databricks, the agentic BI platforms are built for it: ThoughtSpot's Spotter, Tellius and Hex all put an AI agent on top of governed warehouse data. If it means what customers said, in tickets, chats, calls, reviews and surveys, you need a tool that reads text into fields first. For that case Dimension Labs is the strongest fit, because it takes in customer conversations from any platform with an API or an export, with step-by-step integration pages for 90 platforms from Zendesk and Intercom to Gong, Qualtrics and Trustpilot, and it enriches every record before the agent builds anything.
Many teams run both. The warehouse agent answers "what happened to revenue in the West", and the customer-data agent answers "what were customers in the West complaining about, and which of those complaints go with lost revenue". Dimension Labs connects to Snowflake, BigQuery and Databricks, and on Prove exports back to them, so the labelled conversations can sit next to the metrics the BI agent already uses. If your question is about replacing a BI dashboard for feedback specifically, we compare that in customer feedback in Power BI.
When is each tool the better choice?
Julius AI, when one person has one file, wants charts and a short report or slides today, and the categories do not need to stay fixed from month to month.
ThoughtSpot, when the company already models its data in a warehouse and wants business users to ask questions inside dashboards, Slack or their own product.
Tellius, when the recurring work is a business review on commercial, finance or supply chain data, with root-cause and driver analysis on numeric metrics.
Hex, when a data team wants notebooks, SQL and Python alongside an agent, and will publish the result as a data app.
Chattermill or Unwrap, when the buyer is a CX or product team that wants a dedicated feedback platform and prefers to work with a vendor through a demo.
Each of these is a good tool for its job. The deciding question is whether your report starts from numbers that already exist or from text that has to be read first.
Why would a support or CX team build its customer reports in Dimension Labs?
Dimension Labs is an omni-channel analytics agent for customer data. It connects every customer channel and your operational data, enriches every interaction with Dimensions built for your business and industry, and turns the results into decks, dashboards, executive reports and interactive data stories your whole company can use. Plans start at $299 a month with no demo.
For a team that owes leadership a customer report every month, that means four things:
Fully omni-channel. Tickets, chats, calls, reviews and surveys are read in one place, so the report covers the customer, not one queue.
Easy integration across customer channels and operational data: help desks, review sites and survey tools on one side, CRM, orders and billing on the other.
Effortless enrichment of every interaction, with hundreds of Dimensions custom to your business and industry, so support, product and operations read the same labels.
A new visual analytics layer. The agent builds custom decks, dashboards, PDF and executive reports and interactive data stories, saves them to the project's Final Reports folder, and shares them by a link you can revoke.
The NBA is the clearest example of what changes. It replaced static PowerPoint reporting with interactive dashboards, now analyzes 100,000+ open-ended fan responses within hours instead of months, and cut its insight review cycle from about 60 days to under 6 hours.
FAQ
What is the best AI tool for turning support tickets and reviews into a PDF report?
For tickets, chats and reviews read together and reported every month to other teams, Dimension Labs: it labels every record with the same Dimensions, and its agent exports the report as PDF or PPTX and reruns it on a schedule. For a one-off chart from a single file, Julius AI is quicker to start.
Can the report run every month without someone rebuilding it?
In Dimension Labs, yes. Save the report as an analysis plan, ask the agent to run it on a schedule with a time zone, and each run lands in the project's Final Reports folder. A live integration is enriched every day, so each run sees the latest tickets.
Do I need a data warehouse first?
Not for Dimension Labs. You can upload one file of tickets or reviews, or connect the help desk directly. The warehouse-first tools in the table work best when your data team has already modelled the data there.
Can these tools rank which complaints matter most?
Counting themes is common to all of them. Ranking themes by their measured effect on a rating, renewals or revenue, with the other factors held fixed, is what the Marriott Orlando analysis above does; in Dimension Labs, causal findings with confidence intervals are on Prove and up. We explain the method in correlation vs causation in customer experience data.
To see a report built from your own tickets or reviews, start with one file. Plans start at $299 a month with a 14-day free trial and no sales call.