Studies: Run an AI Analysis Across Thousands of Support Tickets

Studies is Macha's tool for exactly this. You point it at a batch of records — Zendesk tickets, to start — define the columns you want filled in, and an AI model reads every record and fills the grid. You get a structured spreadsheet (one row per ticket, one column per question), a report with charts, a CSV export, or — the interesting part — a knowledge source your agents can search. It's bulk AI analysis of your support history, with a cost estimate before you commit and a bill only for what actually ran.

This isn't a demo on toy data. To write this, I ran a real Study over 57 tickets and I'll show you exactly what came back. Studies is available on Macha's Professional and Enterprise plans; the Report view is Professional and Enterprise too.

What a Study actually is

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

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

1. Source and scope

The first input source is Zendesk Tickets (more record types are coming). You scope it with a Zendesk search query and an optional date range, then choose which fields the AI gets to see — subject, description, status, priority, tags, custom fields, and the full comment thread. Narrowing the fields keeps the model focused (and the cost down): if a question only needs the subject and first message, don't feed it the entire thread.

2. Extract — define your columns

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

• Yes / No 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…)
• 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, and there's 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.

3. Review — see the cost before you run

Before anything runs, Studies makes you look at the bill. Click Estimate cost and it shows the exact matching record count, the credit cost per record (it depends on the model you picked), and the total — then asks you to confirm with an explicit "Yes, run now." Nothing is charged until you do.

A few guardrails sit around this:

• Test runs let you sample 10–50 records first to validate your schema and guidance before committing to the full batch — test runs are flagged so they don't clutter your 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 you split by date range.
• Every run keeps a frozen snapshot of the config, schema, and model it used — editing the Study later never rewrites past runs.

A real run: 57 tickets, 114 credits, ~3 minutes

Here's the worked example. I scoped a Study to the last 30 days of tickets on our test Zendesk and defined 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, I confirmed, and it streamed results in as it went.

It finished at 57 of 57 processed, 0 errors. You can sort any column, search the loaded rows, and click into any record to see the full ticket behind the AI's answer. But the grid is just the raw material — the Report view is where it pays off.

The Report: charts over your whole queue

Open the Report from a completed run and Studies aggregates every reportable column into a visual. Yes/No and choice columns become bar charts with counts and percentages; Number columns become histograms with min / mean / median / max. Click any bar to drill into the matching records.

Here's what 57 tickets actually said:

That's a real read on a queue in three minutes: billing is a big slice — 40% of this window touched payments, invoices, or charges, which is a strong signal that a billing-capable agent (or a Stripe connection) would earn its keep. Not a single ticket was an outright refund request, so a refund-automation flow would sit idle here. And urgency is low-to-moderate on average (2.04) but carries a real tail — 11% landed at the top of the scale — exactly the tickets you'd want routed fast. Those are decisions — staffing, routing, which agent to build first — that you'd otherwise make on gut feel. (There's also an "Ask anything about this analysis" Sidekick bar on the report, shown as a coming soon preview — study-scoped Q&A is on the way.)

Turn the analysis into agent knowledge

The step that closes the loop: 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 use on live tickets. Analyse the past, then let the present agents draw on it. That's the difference between a one-off report and a feedback loop.

Where Studies fits

Studies is analysis, not automation — it's the counterpart to Macha's live agents. Use it 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.

It's worth being clear about the trade-offs: Studies runs against your credit balance like everything else, so big batches cost real credits (the estimate-and-confirm gate exists precisely so that's never a surprise), and the Report view is a Professional/Enterprise feature. 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 a Study run on? Zendesk tickets today, scoped by a search query, date range, and the specific fields you let the AI see. More record sources are on the roadmap.

How much does a Study cost? 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 successfully process — cancelled and errored rows cost nothing.

Can I try it on a few tickets first? Yes — a Test run samples 10–50 records so you can validate your schema and guidance before the full batch.

What can I do with the results? Sort/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 is Professional and Enterprise. See the pricing page for current plans.

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.