's secret sauce: How uses Auto-Pilot to scale outbound uses genAI and automation to make prospecting easier for businesses and better for buyers.
Software Development
San Francisco, California

Learn how uses to scale its outbound motion (without scaling resources) and deliver:

  • 1,000 hours saved list building;
  • 3x increase in prospect coverage;
  • 40% of all SDR-driven meetings;
  • Millions in qualified pipeline has been right at the heart of our go-to-market (GTM) strategy since Day 1: from the moment we launched our sequence engine, through to the launch of our artificial intelligence (AI) sales assistant product Co-Pilot, and right up to today with the introduction of Auto-Pilot, our 100% autonomous AI Sales Agents.

Throughout our company's history, we've deliberately assembled a team of GTM experts with deep expertise in sales and AI, who are all committed to helping sales teams win more. As a result, we're often asked, "How does use its own platform to scale GTM motions?"

Read on to get our full story on the evolution of Auto-Pilot, or use the links below to jump to the parts you're most curious about:

Blockers to scale

Like many other SaaS companies, we had to navigate some tricky market conditions over the past year. We took this opportunity to dig deep into our inner workings and scrutinize every business decision and GTM motion we had in place.

The process uncovered a lot of big questions that we needed to answer, including:

"How do we keep pushing for higher sales productivity and scale pipeline generation with our current team size and budget?"

When it came to driving consistent activity and meeting production, we faced major scalability challenges that impacted all parts of our GTM team:

Challenge #1: Lead sourcing

GTM team impacted: Operations

The monthly ops workload for running a prospecting engine was downright intense. First, we had to pinpoint which accounts to target that matched our ICP standards using historical CRM data. Then, we had to source leads within those accounts from third-party data vendors, clean them, and sync them to our systems. Finally, our SDR team lead, Dyer Whitt, had to get enough prospects into sequence every day to meet their activity goals. All this work meant that before our team could even begin prospecting, they had a 20 hour / week ops tax to pay.

Despite all this, our SDRs were always feeling like they didn't have enough leads to last them through the month; they were forever playing catch up, using tomorrow's leads to make up for today's shortages.

Challenge #2: Task management and prioritization

GTM team impacted: Sales

Social and call tasks were piling up, causing prospects to get stuck in sequence. Ironically, our SDRs and account executives (AE) were starved for leads, yet never had more tasks to execute on. That was because it was laborious for them to use intent signals to help them sort through which tasks to tackle first for the best return. The collective sales organization was finding it hard to balance prospecting time with supporting the entire deal funnel.

Challenge #3: SDR capacity

GTM team impacted: Sales Development

Using a sales engagement solution, the max our SDR team could manage at once was between 150-300 prospects. Given that — on average — only half of those ever responded, our team essentially would be spending hours per week working leads that weren't ready to convert.

Challenge #4: Personalization and sequence customization

GTM team impacted: Marketing

Send one bad piece of outreach and you could end up flamed on a Reddit thread. The marketing team cared deeply about protecting the brand and making sure each touch was on point. But the reality was that each prospect was receiving the same degree of personalization and touch pattern, no matter their intent level or CRM history.

Prospecting with Auto-Pilot

Around the same time as this internal reflection, we started pushing the limits of what generative AI (genAI) could be used for - from content generation to task execution. It was at that point that we saw an immediate opportunity for ourselves: we could offload all of the monotonous parts of the prospecting workflow — such as lead sourcing, personalization, and more — to tech that thrives on repetitive work.

That's when Auto-Pilot was born.

We began by setting up AI Agents for 2 audiences in our outbound strategy:

  1. Multi-threading ABM accounts; and,
  2. Cold outbound on accounts that may be a good fit for us but haven't been designated a target account.

We started with these 2 audiences since we were already seeing good results with our old motion there. Like fitting puzzle pieces together, our team had already figured out the process, so plugging in an AI Agent to hand off tasks like building lists and crafting messages was seamless.

ROI for the GTM team

Today, our entire prospecting strategy is powered by Auto-Pilot. Our AI Agents are self-sourcing and engaging thousands of new leads for our sales team, contributing to over 40% of all SDR-driven meetings (and climbing). We've produced hundreds of meetings and millions in qualified pipeline without investing in additional full-time staff.

Using Auto-Pilot, we've unleashed fully autonomous AI Sales Agents on our market, all while solving the 4 big scalability hurdles we mentioned earlier:

1. Lead sourcing

From lead sourcing alone, we've discovered 26,000 new leads and 800+ new accounts that we didn't have in our CRM previously. Historically, that would've required RevOps resources to source, clean, and load into CRM. Using agents to do this, we project that we'll save 1,000 hours per year thanks to auto-sourcing from Agents.

2. Task management and prioritization

Intent signals can be incredibly challenging for sales teams to operationalize. AI Agents use intent and engagement signals to determine who to target, what messaging to say, the next best action and channel to engage them in, and when to bring a human sales rep into the loop. Not only has this made better use of our reps' time, but the dynamic sequences have created a more personalized buying experience for our prospects.

3. Personalization

On average, it used to take our SDRs 2.5 - 3 minutes to write a personalized email. Now, it's fully automated for them. Similarly for LinkedIn touches, what used to be 2.5-3 minutes per message (or - even worse - skipped altogether) is now instant, thanks to our LinkedIn task automator.

4. SDR capacity

We've gone from about 25 prospects being added to sequence per day to between 60-100 with our Auto-Pilot Agents. This improved reach assists in driving brand familiarity, which is critical for a high-growth company.

The increase in volume hasn't compromised lead quality either. Check out some real responses our SDRs got from outreach generated by our autonomous AI Agents:

The GTM team gleans insight into key performance indicators (KPIs) straight from the Auto-Pilot dashboard. Specifically, they're looking at things like:

  • Account coverage, to make sure Agents are hitting enough accounts;
  • Prospect coverage, to make sure Agents are reaching out to enough people per account (and with a good mix per persona);
  • Prospect engagement rate, to see how many engaged prospects end up booking meetings and creating opportunities, giving us a handle on how effective agents are across various use cases; and,
  • Prospects added to sequences.

Elevating the SDR function

With this huge uptick in productivity, our SDRs are finding themselves with a lot more capacity to repurpose toward higher-value human-centric work like:

  • Following up on warm leads through the call and social channels;
  • Strategizing with AEs for key account planning;
  • Adding personalized touches for key accounts; and,
  • Having valuable coaching and one-on-one feedback sessions.

Unequivocally, genAI has changed the operational, execution, and experiential side of prospecting for our own team.

Thanks to this technology, is making prospecting easier for businesses and — most importantly — better for buyers.

Prospect with precision

Put your prospecting on Auto-Pilot, using