Introducing Studies: Bulk AI Analysis Across Your Whole Support History

That's the gap Studies closes. Shipped on May 30, 2026, Studies lets you point an AI model at a batch of records — Zendesk tickets, to start — define the columns you want filled in, and get back a structured grid: one row per ticket, one column per question. It's a clean, repeatable way to bulk analyze support tickets without a data engineer, a CSV export pipeline, or a fragile prompt you paste into a chat window a hundred times. And because it runs inside Macha — the agent layer already sitting on top of your helpdesk — the analysis doesn't end as a slide. You can push it straight back to your agents as knowledge.

This post walks through what shipped, how the workflow actually feels, and the honest limits. If you want the deep, screenshot-by-screenshot worked example with a real 57-ticket run, that lives in Studies: run an AI analysis across thousands of support tickets — this is the release tour.

What Studies is (and what it isn't)

A Study is a saved definition of three things: a source (which records), a scope (which subset of them), and a schema (the columns the AI fills for each one). Run it and Macha processes every record in parallel against the model you pick, producing a frozen results grid you can sort, filter, export, or feed downstream.

Two things to be clear about up front. First, Studies is analysis, not automation — it's the read-only counterpart to Macha's live agents. The agents handle tickets as they arrive; Studies reads the ones that already happened. Second, Macha is not a ticketing tool or a replacement for your analytics suite. It's the AI agent layer on top of your existing helpdesk, and Studies is the part of that layer that turns your back-catalogue into structured, queryable data.

The four-step build

The builder walks Source → Scope → Extract → Review, and the whole thing is designed so you never kick off an expensive run blind.

1. Source — pick what you're reading

The first input source is Zendesk Tickets (more record types are on the roadmap). This is the natural starting point: it's where the richest, messiest, most underused support data lives.

2. Scope — narrow it down

You scope the batch with a Zendesk search query and an optional date range, then choose which ticket fields the AI gets to see — subject, description, status, priority, tags, custom fields, and the full comment thread. That field picker matters more than it looks: narrowing what the model reads keeps it focused and keeps the cost down. If a question only needs the subject and the first message, don't pay to feed it the entire back-and-forth.

3. Extract — define the columns

This is the heart of it. Each column you add is a question the AI answers for every record, and you pick the answer type that matches the shape of the answer:

• Yes / No (boolean) for true-false flags — is this billing-related?
• Single choice / Multiple choice when the model must pick from a fixed list — category: shipping, returns, technical… (you add options as pills now: type a value and hit Enter, or paste a comma-separated list and Macha splits it)
• Number for counts or scores — urgency 1–5
• Short text / Long text for free-form answers — a one-line summary

Each column takes optional guidance so the model knows precisely how to decide, plus an overall instructions box for tone and edge cases. Typed columns are what make the output aggregatable later: a Yes/No becomes a clean bar chart, a Number becomes a histogram. Free text stays free text.

4. Review — see the bill before you run

Before anything runs, Studies makes you look at the cost. The review screen shows the exact matching record count, the credits per record (it depends on the model you chose — there's a credits/record badge right next to the model selector), and the total estimate. A confirmation modal then asks you to commit once, explicitly, before any work starts.

A few guardrails sit around that gate:

• Test runs sample 10–50 records so you can validate your schema and guidance before committing to the full batch. Test runs are flagged so they don't clutter your real history.
• Cancel anytime. Cancellation is graceful, and credits are deducted per successful record — never up front. Errored and cancelled records cost nothing.
• A hard 20,000-record cap per run keeps costs predictable. For bigger jobs, split by date range.
• Every run keeps a frozen snapshot of the config, schema, and model it used. Editing the parent Study later never rewrites past runs.

That estimate-and-confirm flow is the design decision I'd point to first. Credits in Macha are spent per AI action — so a batch over thousands of tickets is a real spend, and the gate exists precisely so it's never a surprise. (Need more headroom for a one-off big run? Top-up packs landed the same day — 1,000 credits for $99 is the entry pack, and top-ups never expire.) See the pricing page for the current plans and credit allowances.

What 57 tickets actually said

To make this concrete, here's a real run from our test Zendesk: the last 30 days of tickets, four columns — a one-line Summary, two Yes/No flags (Billing related, Refund requested), and a 1–5 Urgency number — on GPT-5 at 2 credits per record. The estimate came back at 57 records / 114 credits, and it finished at 57 of 57 processed, 0 errors.

The grid is the raw material. The Report view — which followed on June 5, 2026 — is where it pays off. Open it from a completed run and Studies aggregates every reportable column into a visual: Yes/No and choice columns become bar charts (label, count, percentage); Number columns become histograms with min / mean / median / max on top. Click any bar to drill into the exact records behind it, in a modal that reuses the same results table — so moving from a chart into the underlying rows feels continuous.

Here's what those 57 tickets actually said:

That's a usable read on a queue in a few minutes. Billing touched 40% of this window — a strong signal that a billing-capable agent (or a Stripe connection) would earn its keep. Not one ticket was an outright refund request, so a refund-automation flow would sit idle here. Urgency is low-to-moderate on average but carries a real tail — 11% landed at the top of the scale — exactly the tickets you'd want routed fast. Those are staffing, routing, and build-order decisions you'd otherwise make on gut feel.

(There's also an "Ask anything about this analysis" Sidekick bar at the top of the report. It ships disabled, as a "coming soon" preview — study-scoped Q&A is on the way, and Macha leaves the input inert until then rather than risk a confident wrong answer over your data.)

The part that closes the loop

Most ticket-analysis tools stop at the chart. The step that makes Studies different is that a completed run can be pushed to a Knowledge Source. Each result row becomes a document, indexed through Macha's existing embeddings pipeline — so the structured insights from your Study become searchable context your agents can draw on when they handle live tickets.

Analyse the past, then let the present agents use it. That's the difference between a one-off report and a feedback loop, and it's only possible because Studies lives in the same place as the agents — not in a separate analytics silo.

How this compares to dedicated analytics tools

If you've shopped this category, you've seen the pitch: tools like SentiSum advertise categorizing 100,000 tickets in minutes; Unwrap.ai surfaces systemic themes; NetBrain imports historical tickets as CSV for batch root-cause work. They're capable, and for pure voice-of-customer analytics at scale they go deeper on theme-discovery than Studies does today.

The trade-off is integration. Those are separate platforms you export into and read out of. Studies is a feature of the layer already attached to your helpdesk — so your scope is a live Zendesk search, your output can become agent knowledge in two clicks, and you don't add another login, another data export, or another contract. If your goal is analyse to act rather than analyse to dashboard, that proximity is the whole point.

Where Studies fits — and where it doesn't

Use Studies to:

• Quantify your queue before you build agents — what's actually worth automating, and what's rare.
• Backfill clean structure onto years of inconsistently-tagged tickets.
• Measure a change — run the same Study over two date ranges and compare.
• Seed agent knowledge from real resolved tickets.

Be honest about the limits, too:

• It runs against your credit balance like everything else, so big batches cost real credits. The estimate-and-confirm gate is there so it's never a surprise — but plan the spend.
• The Report view is Professional and Enterprise only. On Trial and Starter you'll still get the results grid and CSV export, but charts show an upgrade card.
• The first source is Zendesk Tickets. If your history lives somewhere else, you're waiting on more record types (on the roadmap).
• It's analysis, not real-time monitoring. A Study is a snapshot of a batch you scope, not a live stream — re-run it to refresh.

For most teams the first Study pays for itself the moment it kills one "I think most of our tickets are about X" assumption that turns out to be wrong.

FAQ

What records can Studies analyze? Zendesk tickets today, scoped by a Zendesk search query, an optional date range, and the specific fields you let the AI see. More record sources are on the roadmap.

How much does it cost to bulk analyze tickets? It's credit-based, priced per record by the model you choose, and you see the exact total before you confirm. You're billed only for records that process successfully — cancelled and errored rows cost nothing. See pricing for current plans, and note top-up packs are available for one-off large runs.

Can I test before running the whole batch? Yes — a Test run samples 10–50 records so you can validate your schema and guidance first. Test runs are flagged so they don't clutter your history.

What can I do with the results? Sort and filter the grid, view the Report charts, export to CSV, or push the run into a Knowledge Source so your agents can search the insights on live tickets.

Which plans include Studies? Studies is available on Professional and Enterprise. The Report view (charts) is Professional and Enterprise only. See the pricing page for details, or the changelog for the full release notes.

Try it

If you've ever wanted a straight answer to "what's actually in our support queue," a Study gives you one in minutes. Start a 7-day free trial, no credit card required, connect Zendesk, and run your first analysis — or read the Studies docs for the full walkthrough.

Written by Abbas (Customer Support & AI, Macha) · Reviewed by Ankeet Guha (Co-founder & CTO) · Published 2026-06-24 · Last updated 2026-06-24.