TitanX
Joey Gilkey didn't start TitanX from a blank whiteboard. Instead, he took a contrarian approach: he bought the intellectual property for $200K in 2023 rather than building a sales intelligence platform from scratch. This decision reflected his background in enterprise sales and multiple previous ventures, where he'd learned that acquiring proven IP could be faster than reinventing the wheel. The fundamental insight was clear—the sales intelligence market was crowded with data providers, but there was an opportunity to sit between data sources and execution layers, using proprietary signals and AI to genuinely improve outbound performance.
After acquiring the IP, Joey made a critical decision: he shut down a profitable services business to go all-in on TitanX. This wasn't a side project—it was a complete bet on the SaaS model. By focusing entirely on the product and market, rather than maintaining legacy revenue, he created organizational clarity and urgency. The platform was designed to serve enterprise sales teams with contracts ranging from $24K to $250K annually, with some of the largest deals exceeding seven figures. The business model relied on high ACV sales, expansion revenue, and strategic acquisitions to accelerate growth.
TitanX grew through a combination of inbound interest, outbound sales efforts, and referrals. The sales intelligence market had clear demand signals, and Joey's enterprise sales background meant he understood how to position the product and close deals. The pricing structure was carefully designed to scale: from $24K entry points to $250K+ contracts, with a credit-based pricing model that captured consumption dynamics.
The core insight that worked was the positioning—not as another data provider but as an intelligence layer that sits between data sources and execution layers. By building defensibility through proprietary data and AI capabilities rather than competing on raw data availability, TitanX avoided a race to the bottom. Expansion revenue proved critical to the growth model, as customers who started at lower contract values expanded significantly over time. The company also pursued strategic acquisitions to accelerate ARR growth, showing that Joey understood acquisition economics.
Less than two years after launching, TitanX had reached $9.7M ARR with a $100M valuation. Joey had raised $27M and taken secondary cash, giving him capital to continue scaling. The business demonstrated the power of combining high-ACV sales, strong unit economics, and strategic capital allocation. What started with a $200K IP investment had become a substantial SaaS company serving enterprise customers with mission-critical sales intelligence needs.
- •Buying IP instead of building from scratch compressed the time to market and avoided reinventing solved problems, allowing Joey to focus capital on sales and distribution rather than R&D.
- •Positioning as an intelligence layer between data sources and execution, rather than as another data provider, created defensibility through proprietary signals and AI instead of competing on raw data availability.
- •The high-ACV model ($24K–$250K+) with expansion revenue built strong unit economics that justified enterprise sales infrastructure and created compounding growth through customer success.
- •Shutting down the profitable services business forced organizational focus and urgency on scaling the SaaS product, preventing the distraction of legacy revenue streams.
- •A combination of inbound, outbound, and referral channels created balanced pipeline generation that served both as customer acquisition and product validation signals.
- 1.Evaluate your market for undervalued IP assets that have proven product-market fit but lack aggressive go-to-market execution; negotiate a strategic acquisition that costs 2-5% of your target ARR valuation.
- 2.Design pricing tiers with clear ACV anchor points ($24K, $50K, $150K+) and a consumption-based credit model that allows expansion revenue as customers increase usage without raising prices proportionally.
- 3.Build positioning around being an intelligent layer that sits between upstream data sources and downstream execution tools, emphasizing AI-driven insights and proprietary signals rather than raw data.
- 4.Allocate 60-70% of early capital to enterprise sales infrastructure (AE hiring, GTM playbooks, deal support) rather than product development, since the IP is already proven and customer acquisition is the bottleneck.
- 5.Combine inbound marketing (thought leadership, content), outbound prospecting (direct sales outreach), and referral programs to diversify pipeline and reduce reliance on any single customer acquisition channel.
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