We gave Pearl and Claude the exact same task on a live deal. Each one produced an A+ answer, but one took 11 seconds, while the other took almost 18 minutes and cost 50 times more. When scaling business solutions, that cost makes a huge difference.
Quick highlights
- Same answer. Both agents produced an accurate pre-call brief and both correctly identified a blocker. But Pearl's brief was tighter.
- ~99× faster. 11 seconds for Pearl vs. 17 minutes 55 seconds for Claude.
- ~50× fewer tokens. ~97k vs. ~5M.
- ~50× cheaper. ~0.11vs.~6.06 per query, on equivalent frontier models.
- The real lesson. A sales rep running Pearl all day would only use ~6 minutes of compute time, versus ~10 hours without Pearl on a raw-agent setup.
Why we stepped into the octagon: vibe-coded agents vs. a system of action for revenue
In this everything-can-be-done-with-AI era, companies are salivating to use AI to improve efficiency and cut costs. This is especially true when it comes to sales, where my fellow cofounders and I have over 25 years of experience. CEOs tend to have the following line of thought:
- "We'll just build it ourselves." More and more companies think they can vibe code their way through their revenue challenges: plug Claude into the CRM, Slack and Gmail, write a good prompt, done. No new tool added to an already bloated sales tech stack.
- "Wait, how much does this cost?" Sales leaders, co-founders and CFOs are horrified when they figure out what it actually costs to run these agents.
So I wanted to answer a question: how does Claude, prompted directly on raw company data, compare with an AI sales workspace built specifically for complex B2B sales teams?
That’s why I put Pearl in the octagon against Claude to see how the two stacked up. This was a “friendly” fight (we love Claude), and I didn’t expect it to be evenly matched, but I was surprised at just how uneven the outcome was: the gap is measured in orders of magnitude.
Before anyone cries foul and accuses us of match fixing, let me share some context. Yes, we built Pearl, a system of action for revenue teams: an AI sales workspace that helps reps prep meetings, prioritize pipeline and know the next best action on every deal. And we run our whole company on it. So this isn't a lab demo with a toy dataset. We ran this test on a real deal, with real data, and asked the kind of question our team asks Pearl every day.
And yes, this isn’t a formal benchmark. We gave it one task, on one deal, with just one run each (if anyone is interested in running their own comparison, we’re happy to set that up). It’s an honest experiment, with real numbers in and real results out. As I said, we love Claude. Pearl runs on Claude. That’s part of why we wanted to test it.
The setup: one pre-call brief on a complex B2B deal
We kept everything identical except the architecture. Same prompt, same access, same frontier-model class.
| Pearl | Claude | |
|---|---|---|
| Data access | CRM, Slack, Gmail, Google Calendar | CRM, Slack, Gmail, Google Calendar |
| Prompt | Identical | Identical |
| Model class | Frontier model | Equivalent frontier model |
| How it reads the data | Pre-computed company brain | Raw sources, read at query time |
The task: a pre-call brief on a live deal, in four parts.
- Deal status, in one line
- The single biggest risk
- Each key stakeholder's take
- The next best action
Sales meeting prep is a predictably ordinary request. Every rep in complex B2B sales wants it before every call. This is where costs add up quickly. And if a routine request at the heart of your business is expensive, it can’t scale.
The results
Quality: both spotted why this B2B deal could be lost
Both agents delivered a great brief. Both nailed the status, the stakeholders and the next step. And both caught the blocker that could have derailed everything: the deal was gated on a security sign-off, buried across Slack and email.
Finding those kinds of details is essential to a pre-call brief, but it shouldn’t take hours of computer time and millions of tokens to find. Pearl was just as good (and gave a slightly tighter answer in our opinion) as Claude, but at a fraction of the time and cost.
Efficiency: what it really costs to run AI sales agents
Pearl internal experiment · one live deal, one run per agent · costs at list price for equivalent frontier models
Pearl answered in 11 seconds. Claude took 17 minutes and 55 seconds. Same story for tokens (about 97k vs. about 5M) and cost (about $0.11 vs. about $6.06): Pearl used roughly 1 to 2% of what the raw agent burned to reach the same answer.
Why such a gap? Raw sales signals vs. a pre-computed company brain
We used the same model class and the same data. So why was there such a massive gap in compute time and cost? The difference lies in what happens between the question and the answer.
How each agent answered the same question
Claude re-derives reality from scratch. Every question triggers a full re-read of the raw corpus: CRM records, Slack threads, email history, calendar. Then it fans out into sub-agents, and each one reloads its own context. You pay for the same reading again and again, to rediscover facts that never changed.
Pearl reads once from a pre-computed company brain. We ingest every sales signal a revenue org generates (calls, emails, calendars, CRM data) and turn it into a structured, connected model of the business: entities reconciled and deduped, relationships mapped, facts extracted and sourced. At question time, Pearl reads what it needs from that brain, then answers. It takes the messy signals as input, and delivers clarity as output.
The raw model is brilliant either way. But making it re-read your entire company to answer the same question 500 times a day? Pearl knows the context, and focuses on making sure everything is up to date, without re-reading everything from scratch with each request (and without racking up your compute costs, either).
Zoom out: one business day for a whole sales team
Numbers are great, but what does this look like for real users? On average, our users spend ~4 hours a day working inside Pearl, equating to a very reasonable 6 minutes of inference time.
Now run the same day on the raw-agent setup. At ~99× the compute time, those 6 minutes become ~10 hours of nonstop compute.
- Pearl: 4 hours of work → ~6 minutes of inference
- Raw agent: the same 4 hours of work → ~10 hours of inference
That doesn't fit in a business day. By the time the brief is ready, your rep has had the call and gone home. Speed isn't a nice-to-have here. It decides whether the agent is usable at all, for one rep, let alone for a whole sales org chasing quota attainment.
What this experiment is (and isn't)
Let's be honest about the limits, so nobody has to do it for us in the comments.
- One task, one deal, one run each. This is an anecdote with a stopwatch, not a statistically significant benchmark. Your numbers will vary.
- The brain isn't free. Pearl pays an ingestion cost up front to build the company brain. That cost is amortized across every question, every rep and every agent. It's not counted in the 11 seconds, but the final cost is still drastically lower than with the Claude agent.
- Quality was judged by us. We checked both briefs against what we knew about the deal. Obviously, we’re partial to our own product, but we tried to be as objective as possible. If you’d like to test it yourself, we’d be happy to schedule a call and walk you through it in detail.
- Costs are list-price estimates for equivalent frontier models at the time of the run.
What the experiment does show is the root of the problem. And that doesn't change much from one deal to the next.
Verdict: sales teams need a system of action, not a vibe-coded agent
So, back to the two questions that started this. Can you vibe code your revenue stack on a raw model? You'll most likely get good answers. You'll also get a 10-hour compute day and a bill your CFO won't enjoy. A purpose-built revenue execution platform gets you the same answer at a cost that scales across the whole team.
Our test isn’t about “us” vs “them.” We’re using the same engine, but with wildly different outcomes.
- Claude: A great model, thrown raw data every time it wakes up.
- Pearl: A great model, built around a brain that already did the reading.
The future of sales AI isn't a bigger model reading more. It's a smarter memory that already read it, once.
(Still love you, Claude. We just built you a brain.)
I’m curious to know what others are seeing when they put agents side by side. If you've run your own octagon, I'd love to hear your thoughts.