The go-to-market AI sector has become a proving ground for a new reality in startup competition: markets a product’s overall quality doesn’t guarantee success. Both well-funded startups and long-time players alike are racing to capture enterprise sales teams, compressing differentiation windows and forcing founders to reconsider how they value and measure growth, meaning the rules of startup competition are being rewritten in real time.
Anmol Chaman and Snehil Saluja, co-founders of Overlayy, see the way forward in a different light. Their B2B sales intelligence platform was built on the premise that accuracy and execution speed are the two crucial aspects to make a proper impact in the middle of a crowded market. Instead of competing on features that competitors could replicate within months, they bet on architectural decisions and constant iteration as the foundation for standing out.
A Market Defined by Capital and Compression
The go-to-market AI sector has become one of the most crowded verticals in enterprise software. Companies like Gong and Clari now compete alongside dozens of well-funded startups, all pursuing the same enterprise sales teams. The intensity stems from a visible and valuable problem: sales organizations have invested in technology stacks that can create data but can’t turn that information into tangible advice and strategy for frontline representatives.
Additionally, many venture funds are placing bets in the space, which means there’s a sudden cohort of startups with significant capital reserves competing for a finite number of enterprise customers willing to adopt new sales technology. And AI’s continual technical improvements have only accelerated the pressure, as development cycles have compressed dramatically, meaning any feature advantage is temporary. Competitors can ship similar capabilities within months, eroding differentiation faster than in previous technology generations.
For new entrants like Overlayy, this meant building a strong product, while necessary, wasn’t enough. Capital availability, distribution advantages, and incumbent relationships created structural barriers beyond pure product quality.
The Architecture of Differentiation
Before fully dipping their toes into the field, Overlayy co-founders Anmol Chaman and Snehil Saluja conducted extensive market research, speaking with former product managers at Apollo, as well as CROs and CEOs across fast-growing companies like Brex, Sprout Social, and Drata, to understand the competitive dynamics they would face. “We started talking to a bunch of people already in the industry,” Chaman recalls. “And that’s when we got the feeling: the market is insanely competitive.”
As they conducted research, they found two sustainable sources of competitive advantage: execution speed for companies operating on volume, and depth of customer relationships for companies pursuing enterprise contracts. Surface-level capabilities could be copied, but underlying technical approaches that enabled meaningfully better outcomes were proving harder to match.
Overlayy chose to compete on accuracy. Instead of processing sales data through single LLM calls, Chaman and Saluja built proprietary machine learning algorithms and agentic workflows that handled long-context sales data through chunked, recursive processes. Where a typical tool might analyze a 90-minute sales call in one pass and miss key objections buried in the middle of the conversation, Overlayy’s system broke the transcript into segments, analyzed each independently, and then synthesized findings to find details that single-pass approaches routinely overlooked.
The technical approach addressed a complaint the founders heard consistently from sales leaders and CXOs, which was that existing tools would typically miss critical context or simply create hallucinated information not present in the source material. A sales rep might receive a suggested follow-up referencing a pricing discussion that never actually occurred, which would all but erase trust in the tool entirely.
To tackle this problem systematically, the team developed SalesQA, the first benchmark for AI understanding of B2B sales conversations, defining over 100 methodology-grounded questions across ten capability categories and introducing explicit hallucination testing on real multi-document deal records including emails, calls, and notes. That rigorous approach to accuracy is what Overlayy set out to build on the technical side.
Velocity as a Survival Mechanism
The founders also dealt with the issue of execution speed as a main determinant of survival in hypercompetitive AI markets. The ability to ship improvements, deal immediately with customer feedback, and iterate on product direction at a faster rate than similar product with larger teams and more resources was a must-have for the company.
Overlayy scaled from founding to approximately $100,000 in annual recurring revenue in seven months, earning a top 10% placement at both Y Combinator and a16z Speedrun in the process. These velocity metrics signaled execution capability to investors and potential customers alike.
Their speed advantage was partially structural: a five-person team operating from Bangalore could make decisions and ship code without the coordination overhead that slows larger organizations, and it allowed them to run fast product improvements with minimal turnaround times. “Execution speed is what’s solving a lot for mid-market companies operating on volume,” Chaman said. “Investors want to hear about initiatives like reducing a six-month sprint to a one-month sprint, or having a novel approach to development.”
As Aquibur Rehman, CEO of Mailmodo, observed of Overlayy’s approach: “Their edge has always been their speed of execution, while being extremely customer obsessed: reducing the timeline between feedback and seeing it improve and compound their platform.”
The founders point to companies like Lovable, which reached $100 million in annual recurring revenue in under a year, as evidence that the most successful AI startups are those that compress traditional development timelines dramatically. These companies change their approach to product-market fit monthly rather than quarterly.
For founders entering competitive AI markets, Chaman points out that the planning horizons that worked in previous technology generations are now too slow. The question is whether teams can sustain a fast pace without impacting the product’s quality.
What Hypercompetition Signals for AI Markets
The GTM AI market offers a preview of dynamics that will likely play out across other markets seeing to incorporate this technology. Feature commoditization happens rapidly, differentiation windows have compressed significantly, and investors place more value on teams that can execute quickly and consistently.
Market selection has become as critical as execution for founders considering entering this sector. Even exceptional teams face structural headwinds when well-funded incumbents and dozens of startups are already fighting for the same customers. Those without defensible technical differentiation or extraordinary execution speed must make difficult strategic choices. They can raise aggressively to compete on capital, carve out underserved niches within broader markets, or step back and look deeper into whether the market is winnable at all.
Overlayy’s founders believe that relationships and community integration will play a more defining role in determining which companies succeed. The questions that matter, then, are less exclusively about pure product capability and more about softer metrics: how many doors can a startup open, how deeply can it embed in customer workflows, and how strong a brand can it build before competitors close the technical gap.
Anmol Chaman and Snehil Saluja built Overlayy to test if technical precision and constant execution speed could carve out a space for them in one of enterprise software’s most crowded markets. The moat has moved. Their trajectory so far seems to suggest that in the current stage of the AI industry, the price of entry has never been higher, but for teams willing to meet it, the opportunities remain real.
It’s no longer about who builds a feature first, but who builds the trust and distribution loop that keeps compounding while everyone else catches up. Feature differentiation is temporary; compounding is not. The only durable edge is a system that grows stronger as the market gets noisier: distribution, retention, and learning loops that accelerate precisely when competitors flood in. And that’s what the founders of Overlayy are banking on.
