The written case study How LeadForce Solutions found SaaS leads that don't exist in other databases with SaasyDB

Aaron Gusinov designs, builds, and runs outbound systems in-house for AI and software companies looking to grow. His own client acquisition runs on the same cold email infrastructure he builds for others. If the lead source is weak, his pipeline dries up.

The difficulty was finding contacts who weren't already drowning in outreach. General-purpose databases classify companies by self-selected industry labels that are too broad to reliably filter for actual SaaS companies.

if I'm a company and I put myself under computer versus internet versus SaaS versus whatever on LinkedIn, that's what it's going to funnel into in these other databases.

Beyond the filtering problem, Aaron describes a "mental cost" in not knowing whether the leads in his database were current, relevant to his niche, or already receiving dozens of competing pitches each week. For someone whose business depends on standing out in an inbox, that doubt hung over every campaign. "If they're already in a public database that everyone has access to, you are competing, even if your offer is amazing, by virtue of having 50 other offers that are not good."

While building LeadForce Solutions alongside a position at J.P. Morgan, Aaron was studying go-to-market engineering from practitioners who had already built outbound agencies. One of them was Taylor Heron, a prominent figure in the field.

He contacted Stuart, SaasyDB's founder, started a month-to-month subscription to sample the data, and ran his own scoring process. His framework looked at whether leads were B2B, what vertical they operated in, and whether the right decision-makers were present. The majority of SaasyDB contacts scored as "perfect fit" or "good fit," validating that the database matched his target persona.

he was talking about how he sources leads. And this was one of the, he said, listen, if you want a great source for AI SaaS companies, there's none better than SaasyDB.

Aaron is candid about what SaasyDB was not when he joined approximately six months before the interview. It did not have the slickest interface. Export times were slow. There were UI issues. But the data quality overrode those friction points.

Two factors drove his decision. First, niche specificity — SaasyDB focuses exclusively on SaaS and software companies. With other databases, Aaron would export a broader list and then run AI scripts to prune it. With SaasyDB, the gap between the raw export and a qualified list was smaller because the database was already filtered to his market. Second, the contacts did not appear elsewhere. In cold email, reaching people who aren't already getting dozens of other pitches matters.

I would cross-reference, I'm like, can I find this contact in another database? And I couldn't. So for me, that was very attractive.

Stuart, SaasyDB's founder, also proved responsive to feedback. When Aaron flagged slow export times, Stuart fixed them. When he asked for the ability to exclude previously exported contacts, Stuart added an "export only net new" toggle.

Aaron uses SaasyDB primarily to source leads for his own client acquisition, running cold email campaigns to AI and software company founders. His outreach is deliberately unscalable — custom video research for each prospect — trading volume for relevance.

He doesn't need high volume. Aaron targets five high-end clients and approximately ten smaller engagements, so each qualified response counts. SaasyDB also doubles as a sales tool: one client needed to target SaaS companies, and Aaron pitched his access to a proprietary database that most competitors don't know about.

He committed to a lifetime deal after validating the data on a monthly subscription.

it's very satisfying to know that if I email a thousand people here, I'm more likely going to get in touch with people who aren't inundated with emails than a thousand over here.

The risk is low, Aaron says: "the risk to reward trade-off is very asymmetrical in your favor." Start with a monthly subscription — approximately $90–$100 at the time of the interview — test a sample of leads against your own scoring criteria, and decide from results.

Right now, it's good enough that Taylor Heron, who's like a big boy, called him out. And this is the worst that it will ever be because it's only going to improve.