Counterfactual Technology creates self improving speech systems that learn strategic behavior through Counterfactual Regret Minimization and rely on grounded knowledge to ensure accurate responses. With every conversation, they grow in competence and understanding.
Deploy adaptive voice and chat agents for support and sales. We train on the clients recorded calls and documentation, then roll out under supervision while the system continuously improves quality, compliance, and conversion.
Our learning engine employs Counterfactual Regret Minimization, a proven framework from game theory that teaches an intelligent system to make better decisions through real-world experience and self-correction. By evaluating every possible outcome and minimizing past mistakes, the system continuously improves its strategy and performance over time.
Counterfactual Technology turns every conversation into a learning opportunity. Our self improving speech system is built to scale across support and sales, delivering measurable gains in efficiency, satisfaction, and revenue. Learn how this technology creates lasting value for partners and investors.
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.
We provide a supervised rollout of speech systems for support and sales teams, delivered as a scalable licensed service. Each deployment is priced per active agent license, allowing flexible expansion as performance and confidence grow. Our process includes secure data onboarding, a knowledge base for factual and stylistic grounding, CFR policy training for decision strategy, and continuous learning from both recorded and live conversations.
Each licensed agent learns from every interaction while maintaining brand consistency, compliance, and tone. Performance improves incrementally as outcomes feed back into the training loop, raising first contact resolution, customer satisfaction, and conversion rates over time.
Because all agents share a unified learning architecture, knowledge is transferable across the entire network. If one agent achieves superior results by discovering more effective phrasing, timing, or strategic responses, the improvemed knowledge can be shared with all other agents. In a single update, every agent benefits from the best performer’s knowledge, aligning collective intelligence and maintaining consistent quality across the operation.
Our goal is to make every licensed agent a self improving asset that delivers measurable business value through genuine intelligence that learns, ensuring that progress made anywhere is available everywhere.
Contact us to discuss a pilot.
Rapid pilot setup with data onboarding and baseline models. Start learning from real conversations in days, not months.
The knowledge base keeps answers factual and on-brand, while CFR selects the best tactic. Quality rises with every call.
Standardized best practices at scale reduce training time and rework, lowering the cost per resolved customer interaction and increasing efficiency across both service and sales operations.
Counterfactual Regret Minimization, or CFR, forms the learning backbone of our self improving speech system. The technology enables our systems to make strategic decisions based on outcomes and continuously adjust those decisions as new data becomes available. Instead of relying on static training or predefined rules, CFR learns by listening and evaluating every possible conversational path, understanding what could have worked better, and using that insight to improve future interactions.
Our approach integrates Counterfactual Regret Minimization with learned knowledge and real world supervision to create an agent that learns through experience, much like a human listener. After analyzing hundreds of hours of recorded customer service and sales calls, the agent learns tone, phrasing, timing, and the strategies that drive positive outcomes. Each decision it makes is compared against alternative approaches, revealing which actions lead to greater customer satisfaction, faster resolution, and higher success rates. Over thousands of observed and live interactions, this process minimizes regret, deepens understanding, and produces more natural, context aware behavior that improves continuously over time.
When combined with our knowledge base architecture, CFR transforms every conversation into a self contained learning opportunity.
Together, this knowledge gives the agent both the understanding and the judgment to act with consistency, accuracy, and authenticity.
Cloud infrastructure supports secure vector retrieval, model deployment, and continuous embedding updates. As each new interaction occurs, the system refines its decision model, building a feedback loop that compounds intelligence and efficiency over time.
This is the essence of Counterfactual Technology, intelligence that learns like we do, improving itself through every decision it makes and every conversation it holds.
Counterfactual Technology is advancing self improving speech systems for customer support and sales. Backed by years of applied research and development, we are now preparing production pilots with enterprise organizations that can provide recorded conversations and service documentation for supervised rollout.
Our platform represents a shift from static automation to actual intelligence, systems that learn, adapt, and grow in competence over time. Each deployment builds a proprietary learning loop within the client’s own environment, turning every interaction into measurable value. As performance improves, labor costs decline, customer satisfaction rises, and conversion rates increase. The result is a compounding economic advantage that scales with use.
Our roadmap includes secure data onboarding, a knowledge base for factual and stylistic grounding, CFR policy training for decision optimization, and continuous evaluation across key metrics such as resolution rate, customer satisfaction, and revenue uplift. We expect each trained agent to deliver recurring efficiency gains and performance improvements long after initial deployment.
The commercial potential is significant. The global customer interaction market represents hundreds of billions of dollars annually. Even small percentage improvements in call handling, retention, or conversion translate into substantial recurring revenue for our clients and for Counterfactual Technology itself through our licensed model.
We welcome investor and strategic partner discussions with those who share our vision of building genuine intelligence that learns like we do, delivering compounding value over time.
Why partner with us?
Contact us to explore pilots, data partnerships, or investment opportunities.
Instead, it would treat that prior knowledge as a starting point, or perhaps dispense with it entirely, and then learn continually from its own experience in the world. It would have explicit goals, get feedback when it succeeds or fails, and incrementally become more competent.
When it is right, it's often by chance, a side effect of pattern matching rather than knowledge. This is why these systems often hallucinate. They don’t know they are lying. They don’t know anything at all. They are parroting and remixing their training data. If that data is thin, skewed, or mixed with fakes, the output will carry the same flaws.
Looking back with this perspective, I have seen this happen many times. I have often wondered why women sometimes abandon logic completely and make decisions purely based on emotions.