Research
Most people hear "AI brand research" and assume we mean SEO research with a new coat of paint. We don't.
Research in Trakkr is sampling. We take the questions your buyers are actually asking AI, ask those questions to the real models on your behalf, and capture exactly what comes back: which brands get recommended, in what order, with which sources cited. The output isn't a keyword report. It's a snapshot of how AI talks about your category right now, with you in it (or not).
That distinction matters because the old playbook breaks down here. Typing a few queries into ChatGPT yourself is not research, it's anecdote. The model gives different answers on different days, in different sessions, with different phrasings. You need volume to see the pattern, and you need that pattern across all the models your buyers use, not just the one you happened to open. That's what a Research run does in five minutes.
Research is not SEO research
The unit is different. The mechanic is different. The decision it informs is different.
If you walk into Research expecting a keyword tool, you'll under-use it. Walk in expecting a market-position scanner and the page makes immediate sense.
How a research run works
You hit Run Research. About five minutes later, you get a complete picture of your category.
Behind the scenes, we generate roughly 500 discovery-style prompts tuned to your brand, your industry, your competitors, and your description. Then we ask those questions across multiple AI models, parse every response, and stitch the results into a single view.
A few things worth knowing about how the sampling works:
- The prompts are generated, not curated. Trakkr writes them from your brand description, industry, and known competitors. The more complete your brand profile, the sharper the prompts. Thin profile, generic prompts.
- It's a one-shot scan, not a daily tracker. Each run replaces the last. If you want continuous monitoring on specific questions, that's what your active Prompts list is for.
- One run, one model lane. A Research run picks the best generation model for the job (Gemini by default) and uses it across the batch. Your daily Prompts page runs every active prompt against all eight models, which is a different question being answered.
What you actually get back
When the run finishes, you land on a page with four things stacked together. Read them top to bottom and a story tends to emerge.
Your position distribution
A donut chart breaks every prompt in the run into three buckets: Top 3, #4-10, and Missing. The shape of the donut tells you what kind of market you're in.
| What the donut looks like | What it usually means |
|---|---|
| Mostly Top 3 | You're an established leader. Defend, don't churn. |
| Big #4-10 slice | You're on AI's radar but not first-pick. Lots of room to move. |
| Big Missing slice | AI doesn't know you, or knows you but doesn't recommend you. The biggest lever. |
| Split evenly | Different parts of your category treat you differently, worth slicing by topic. |
Click any slice to filter the table underneath, so you can read the prompts that produced that bucket.
Market leaders
Next to the donut, a ranked list of the brands AI mentioned most often in your run, with a share-of-voice bar and your own count alongside. If a competitor you've never heard of shows up at #2, that's a thing to know.
The insights strip
A thin strip above the table highlights statistically interesting patterns from this run. Trakkr looks for slices where your performance diverges meaningfully from the baseline and surfaces them as chips you can click into.
| Chip | What it means |
|---|---|
| Strength | A topic, persona, or intent where you over-perform the baseline. |
| Weakness | A slice where you under-perform, often because of a content gap. |
| Opportunity | High-volume prompts where you're close but not over the line. |
| Topic | A theme worth knowing about, even if it's neither strong nor weak. |
| Competitor | A rival who's winning a specific slice. Usually the most actionable chip. |
| Pattern | A general statistical signal that didn't fit the other buckets. |
Click any chip to filter the table to the prompts behind it. This is usually faster than scrolling.
The prompt-by-prompt table
Every row is one prompt, with your rank position, the estimated AI search volume for that question, and (on hover or expand) the competitors that appeared in the response. The four tabs at the top of the table are the same filters as the donut slices, plus an All view and a Close view (your #4-10 prompts, sorted by how close you are to breaking into the top 3).
Position color reads at a glance:
| Position | Read it as |
|---|---|
| #1 | Top recommendation, AI is leading with you |
| #2-3 | Recommended, in the headline list |
| #4-7 | Mentioned but not prominent |
| #8-10 | Buried in a long list |
| - | Not mentioned at all |
Turning research into tracking
The whole point of a Research run is to decide what to start monitoring. Two paths.
Quick Wins. When a run completes, the Quick Wins button in the header shows you the prompts where Trakkr thinks the smallest amount of work moves you furthest. These are the high-volume, close-but-not-winning questions. One click reviews them in a modal, another adds them to your active list. If you only do one thing after a run, do this.
Pick by hand. Browse the table, click the + on any row to add that prompt to active tracking. Select with checkboxes for bulk-add. Filter the table to the Missing tab if you want to focus on coverage gaps, or Close if you're hunting for movable rankings.
Whatever you add starts running through Trakkr's daily pipeline the next morning. The Research view itself is one-and-done, but the prompts you promote out of it run forever.
Topic Snapshots
Sometimes you don't want to scan your whole market. You want to look hard at one slice of it: enterprise pricing, open-source alternatives, a new vertical you're moving into, a specific competitor's territory. That's what Topic Snapshots are for.
A snapshot runs the same machinery as a full Research scan, but narrowed: you give it a topic and optional context, it generates 50-100 prompts focused there, and finishes in about 3-5 minutes. Snapshots live in their own tab alongside the main run and stack up over time, so you can build a small library of focused scans.
| Plan | Snapshots per month |
|---|---|
| Free | 0 |
| Growth | 3 |
| Scale | 10 |
| Trial (Scale, 14 days) | 1 |
Snapshots are the right tool when you've already done the broad scan and you have a specific question. "How do I look against Salesforce in mid-market?" "What does AI say about open-source alternatives in our space?" A topic name and a sentence of context is usually all the steering it needs.
Research vs the Prompts page
These get confused a lot. The shortest version: Research is discovery, Prompts is monitoring.
| Research | Prompts | |
|---|---|---|
| Purpose | Find what to track | Track what matters |
| Scope | ~500 generated prompts | Your curated list (5-50, plus packs) |
| Cadence | On-demand, manual | Daily, automatic, 3am UTC |
| Models per prompt | One, picked for the run | All eight |
| History | Latest run only | Full historical timeline |
| What you do with it | Add the keepers to Prompts | Read scores, trends, citations, competitors |
You don't pick between them, you use them sequentially. Run Research to widen the field. Promote a handful of prompts to your Prompts list. Watch those prompts daily. Re-run Research when the market shifts.
When to run it (and how often)
Research is a periodic activity, not a continuous one. Run it when something material has changed:
- Setting up a new brand. Always start here. You don't know what to track until you've seen the market.
- After adding or losing a major competitor. Their presence changes which prompts are interesting.
- After a launch, rebrand, or pricing change. Verify AI has caught up to your new positioning.
- Quarterly, even if nothing changed. AI training data shifts, models update, your category moves. A quarterly scan catches drift.
Each run replaces the previous one for a given brand, so re-running is non-destructive in the sense that you don't lose your tracked Prompts, but you do lose the previous Research snapshot. If you want to preserve a point-in-time view, use a Topic Snapshot instead, those persist.
Common questions
How does Trakkr decide which 500 prompts to generate?
It reads your brand description, your declared industry and competitors, and your website. It then asks an AI model to generate discovery-style prompts across a balanced mix of intents (recommendation, comparison, best-for, etc.) and focus areas (general, budget, enterprise, features, integrations, and so on). Fill out your brand profile properly and this gets sharper.
Why is my Research position different from my Prompts score?
Because they're measuring different things. Research runs each prompt against a single model. Prompts runs each prompt against all eight models and averages. Same prompt, different lens. If you add a prompt from Research to your active list, expect the score to shift once it starts running across the full model fleet.
Does running Research affect my tracked prompts or quota?
No. Research is a separate run with its own machinery. Your active Prompts continue running their daily cycle untouched, and Research doesn't consume prompt slots.
Can I re-run Research without losing my last one?
Not in the main scan, no, each run replaces the previous. If you want to preserve a specific view, run a Topic Snapshot on that slice instead. Snapshots accumulate.
Why did Quick Wins suggest a prompt I'd never add?
Quick Wins ranks by demand and proximity, not strategic fit. A high-volume prompt where you rank #4 will surface, even if the prompt is tangential to what you actually sell. Treat the list as a short list, not a checklist.
Topic Snapshots aren't available on my plan, what's the gate?
Snapshots are a paid feature: Growth gets 3/month, Scale gets 10/month, and Scale trials get 1 to try. Free accounts see a teaser. Quota resets on the 1st of the month.