Is this the most underrated AI tool of 2026?
It runs 19 AI models in parallel, automatically picks the right one for each subtask, and stitches the outputs into a single finished deliverable.
Anthropic and OpenAI have gotten most of the attention this year, which has distracted other people from other really powerful AI solutions. Perplexity has always been known for the go-to tool for deep research, and I’ve been a pro user for almost a year. But I recently gave Perplexity Computer a try, and this Stack & Scale issue is all about why you should stop sleeping on one of the most powerful tools out there right now.
So, if you’re still only using Perplexity for research, that’s fine, but it’s like using a Formula 1 car to drive to the grocery store.
I’ve been using Perplexity Computer for two weeks. In that time, I built a working B2B pipeline diagnostic tool with five inputs, a stage diagnosis, twelve channel verdicts, and a 30-day playbook with specific dollar reallocations. The whole thing routes work across three AI models in the background. And, of course, I didn’t write a line of code.
Anyone who’s been doing this long enough has watched influencers on social media tout a new solution, saying how easy it is to use, but when you go to use it, nothing works as it should. And to be honest, I was slightly skeptical the first time I saw an influencer bragging about what he was able to build with Perplexity Computer. The more people I saw building cool things, the more I knew I had to try it.
Before I show you what I built, I want to walk you through what Perplexity Computer is.
What Perplexity Computer is
Perplexity Computer is a cloud-based AI that runs in the background, routes work across 19 different AI models, and connects to more than 400 tools to ship finished work end-to-end. The mechanic that makes it different is the input. You don’t have to do crazy prompt engineering or use a custom GPT or skill that gives you the most elaborate prompt. All you have to do is start with the outcome you want.
Tell it “compare Apple, Microsoft, and Google stocks, build me a dashboard on each, and write a short video script on which one to bet on” and it breaks the work into subtasks. It routes each subtask to the model best suited to it. Claude will handle the reasoning, Gemini will do the research, GPT will run the data work, and Perplexity Search will pull live citations. Then it stitches the result into a single finished deliverable.
The nice part that took me a minute to internalize is that it runs in the background. It doesn’t stop when you close the tab. It can keep running for hours, days, or weeks on a single goal (or so I’m told; my most complex build only took about 90 minutes). Perplexity Computer is operational, and the whole point is that it doesn’t stop until the work is done.
Why it’s a different kind of tool
I talk to marketing leaders every week about how they’re using AI, and I hear the same thing over and over again. They’ve got four ChatGPT tabs open: One for LinkedIn posts, one for ad copy, one for brainstorming, and one for prepping for the board meeting. What you see is AI is doing a lot of writing, but the CMO still wants to do the strategy and the delivery and piece all the systems together.
That’s fine, and I think it works for most people, but it’s time to use that F1 car for racing. It’s time to get more value out of the tools you have at your fingertips.
There are three main things that make Perplexity Computer different from the AI most marketers are using right now.
It does its own model routing. You stop picking which AI to ask. It picks for each subtask automatically.
It acts inside your stack instead of next to it (Gmail, Slack, HubSpot, Salesforce, Notion, GitHub, your calendar, your files). It doesn’t generate text and ask you to paste it somewhere. It executes inside the tools you already use.
It runs while you’re doing other things. The work happens whether you’re at your desk or not.
The GTM use cases that delivered
I’ve been stress-testing Perplexity Computer against the workflows I run frequently as a fractional CMO. Here are five that really delivered for me:
First up is a weekly pipeline brief that runs on its own. I told it to pull from HubSpot and Salesforce every Sunday night, write the narrative, flag the at-risk deals, and drop the summary into Slack before my Monday standup. I haven’t built that report manually in two weeks, and the version it ships is better than the one I used to build because it includes data I would have skipped to save time.
Next, campaign post-mortems. I gave it a campaign name and told it to pull the data from Google Ads, LinkedIn Ads, and HubSpot, identify what worked, and draft a board-ready summary. What used to be a half-day exercise became a 15-minute edit of work that was already done.
Then there are ABM target account briefs that update automatically. I dropped in my Tier 1 list and told it to pull the latest funding news, hiring signals, exec changes, tech stack additions, and recent LinkedIn activity from the buying committee for each account. The output was a one-page brief per account, refreshed every Monday morning. We’ve all been in the moment where an AE pings you on a Friday afternoon, asking for context on an account you know nothing about. I don’t have to worry about that ever again.
Next was my quarterly board deck drafts. I connected it to HubSpot, Salesforce, GA4, and the ad platforms, then told it to pull the quarter’s pipeline, marketing performance, channel breakdown, and CAC trend, write the narrative, and draft the slides. The before-and-after on my prep time was something like 12 hours down to 90 minutes. Most of those 90 minutes are now spent on the narrative I want to tell, which is the part that matters anyway.
And the one I didn’t expect to work as well as it did was my pre-event prep briefs. We are in the pre-summer event swing, so I dropped in a conference name and told it to pull the speaker list and the attendee company list, score each against my ICP, build research briefs on the top 25, and draft pre-event LinkedIn outreach in my voice. I walked into the event with warm context on 25 people I wanted to talk to. Three turned into real conversations. One turned into a deal that’s now in pipeline.
Of course, I don’t need to tell you that each of these would require a lot of work from many different people. And there’s some work on my own. Now, maybe an hour or two to set it up, then minimal ongoing maintenance.
Check out what I built – The Pipeline Reality Check
Now I can’t share any of those other builds with you because they have proprietary data, but I still wanted to build something for you, my Stack & Scale audience. I called it the Pipeline Reality Check.
I built it because I think most pipeline calculators are useless. They all do the same thing (revenue target divided by ACV divided by win rate equals the number of meetings you need). They tell you the number, but they don’t tell you whether your current channel mix can deliver it.
Mine does. There are five inputs in, and you get a stage diagnosis (where you are on the PMF arc), a verdict on twelve acquisition channels (scale, watch, cut, test, or skip), and a 30-day playbook with specific dollar reallocations tied to your numbers. The math runs on GPT. The narrative runs on Claude’s voice. The live channel benchmarks come from Perplexity Search with cited sources. Three models, one tool, one finished product.
The build itself is what I want you to pay attention to. Again, I didn’t write a line of code. I scoped the outcome in one prompt (the math logic, the channel framework, the design system, the email gate, all of it) and Perplexity Computer broke it into subtasks, routed each one to the model best suited for it, and shipped a working web tool. This would have been a four-week build with a developer six months ago, but I did it myself in an afternoon.
You can try it on your own numbers here.
And if you want to build your own, here’s the prompt that I used:
I want to build a B2B SaaS pipeline diagnostic web tool. Most pipeline
calculators tell you the number of meetings you need to hit your target.
This one should tell the user whether their current channel mix can
deliver it. The goal is to give marketing leaders clarity about what to
fund, watch, cut, test, or skip.
What it should do
Walk the user through a 5-step wizard:
1. Their numbers: annual revenue target, average contract value (ACV),
current win rate, sales cycle length, current ARR
2. Their funnel: demo show rate, demo-to-qualified rate, qualified-to-close
rate
3. Their stage: 4 multi-choice diagnostic questions covering referrals,
retention, channel repeatability, and customer urgency
4. Their motion: primary go-to-market motion (founder-led, sales-led,
product-led, or hybrid), current monthly marketing spend, channels
they're running today (multi-select from a list of 10)
5. Their reality check: the results page
What it should output
Compute and display:
- Annual deals needed, monthly deals needed, demos needed per month,
qualified meetings needed per month, and CAC ceiling (ACV divided by 3)
- A stage diagnosis (Stage 1 Searching / Stage 2 Developing / Stage 3
Repeatable / Stage 4 Scaling) based on the user's diagnostic answers
- A verdict on each of the 10 channels using this 5-state system:
- SCALE (running it, keep it)
- WATCH (running it, monitor for 30 days)
- CUT (running it, stop or rework)
- TEST (not running it, pilot it for 30 days)
- SKIP (not running it, wrong stage)
- A 3-paragraph strategic read in plain English covering the situation,
the diagnosis, and the recommended action
Format all numbers with comma separators ($500,000 not $500000).
Branding (fill in with your details)
- Primary color: [HEX]
- Accent color: [HEX]
- Background: white throughout, with light gray section breaks
- Font: [FONT NAME] for everything
- Brand name in header: [YOUR BRAND]
- Logo: [PASTE LINK or attach]
Voice
For any narrative output, match the tone of these samples:
[PASTE 2–3 of your LinkedIn posts, newsletter intros, or other writing
samples here]
End with
An email capture card with work email and name fields. Submission
triggers a webhook to my email tool (I'll add the webhook URL after
the build is done).
Build this as a clean single-page web tool. Skip demo polish. The
output should feel useful enough to publish.
Why you should be paying attention right now
Somewhere in your category, a marketing team is going to find Perplexity Computer this quarter and use it to outwork everyone else for the next four. I don’t know which competitor it’ll be. I do know it’ll happen.
Most marketing teams I talk to are still using AI for the easy stuff (writing emails, drafting LinkedIn posts, and summarizing calls). That’s a fine starting place, but it’s a tool that gives you back a few minutes a day. Perplexity gives you back a few hours each week while you’re doing something else.
Start here
Pick the workflow you dread on Monday mornings. The one you’ve been meaning to systematize for six months and haven’t. Block 30 minutes this week and hand it to Perplexity Computer.
That’s the whole test. If it works, you’ve built a piece of background infrastructure that runs forever. If it doesn’t, you’ve spent 30 minutes figuring out where the tool’s edges are. Either way, the time pays for itself.
Learn more about Perplexity Computer here.
And if you want to see what’s possible when you let Perplexity build something for you, run the Pipeline Reality Check on your own numbers here.
That’s it for this week. Play around and let me know what you build!
– Brandon