Purely conversational AI provides little business value. To be effective, AI agents must merge conversation with process automation, enabling them to take concrete actions and resolve issues end-to-end, rather than just answering questions.
A key sign of product-market fit in enterprise SaaS is when a product, initially adopted by one team, gets pulled into other departments organically. This internal virality, driven by demonstrated value, is a powerful growth engine and a clear PMF indicator.
Purely probabilistic LLMs are unreliable for critical business processes. GetVocal's architecture uses a deterministic "context graph" based on user intentions as the core decision-making engine. This provides traceability and reliability, while selectively calling generative models for conversational nuance.
The system ingests a company's knowledge bases to generate an initial "context graph." As the AI operates, it uses LLMs to explore new conversational patterns. Once a pattern becomes frequent, it's codified into the deterministic graph, making the system more efficient and reliable over time.
Founders shouldn't expect AI to automate a business function instantly. Real-world adoption is a gradual "glide path" where automation scope increases over time. This requires building systems that facilitate human-AI interaction, allowing humans to coach the AI and vice versa for a smooth transition.
The founding team's initial venture was an AI agent for Alzheimer's patients. Despite its personal meaning, they recognized that long clinical trial cycles made it commercially unviable. They pragmatically spun off the core technology to create GetVocal, targeting enterprise pain points.
While working with an early telecom customer, GetVocal's results were merely "okay-ish." After a quick iteration and launching a new agent on a Thursday, they saw a massive, unexpected spike in the "first-time resolution" metric over the weekend, providing concrete, data-driven evidence of product-market fit.