About us.
Counterfactual Technology is built on a simple belief that genuine intelligence learns. Our mission is to create speech systems that becomes better through experience, not just through larger models. We combine a counterfactual regret minimization policy engine that decides what to do next, a reward model that predicts what is most likely to work now, and a knowledge base that ensures every response is both factually grounded and on brand. The result is conversations that feel natural, helpful, and aligned with the clients standards.
Continuous learning is the core loop. We start by onboarding a few hundred hours of recorded calls and product or service documentation. From those examples the system learns vocabulary, tone, escalation paths, policies, and the subtle markers of a good outcome. Once live, the agent initially operates under supervision. Every interaction produces signals such as resolution, callbacks, sentiment, policy compliance, and customer follow up. These signals feed back into the training set and update the policy, the reward model, and the knowledge base, so the next interaction starts a little smarter.
Transparency and accountability guide how the system improves. We track changes as versions, compare results between models, and promote only those that improve key metrics such as first contact resolution, handle time, customer satisfaction, and conversion while maintaining compliance and brand standards. Knowledge keeps answers explainable because each response cites the product reference and style example it relied on. If guidance changes, updating the source documents immediately changes the agent’s behavior without the need for retraining.
We learn under governance rules that ensure privacy and security. Guardrails enforce escalation rules and phrasing boundaries, and supervisors can review conversations, annotate exceptions, and provide targeted corrections that the agent adopts on the next cycle.
From discovery to production, our approach is iterative. Pilot with supervision, measure, promote, and repeat. With each loop the assistant reduces variance across agents, captures best practices, and applies them consistently, delivering Intelligence That Learns Like We Do.
Vision
Intelligence that learns like we do, raising the quality of every customer conversation.
Mission
Deliver adaptive, grounded speech systems that improves with experience and earns user trust.
Customer Satisfaction
Teams adopt our agents to increase resolution rates, consistency, and customer satisfaction.