Improve decisions and reduce risks with trusted human-centered AI.
About Hawkrel By Vestun
Human-Machines Characterized
Developed by Vestun, Hawkrel is a cognitive system pairing encoded human expertise and data analytics to fully capture the real-world contextual nuance necessary to predict solutions and transform disorganized tribal knowledge into durable and scalable assets.
We place machines and people in harmony to deliver human-like logical reasoning with machine’s discipline, speed, and scalability needed to collectively interpret changing data, conditions, and events in real time.
Common Business Ontology
Unify business concept in a common ontology to turn fragmented knowledge into a human-readable representation that maps business concepts and encode them into data that best describes them.
Both models and human experience are feed back into existing processes to allow domain-experts building on top of one another's work so that collectively they act more intelligently than any person, group, or computer alone.
Align Incentives to Drive Collaboration
Define granular data access, security, and transparent decision governance to efficiently collaborate across domains and functions with all key stakeholders.
Our unique hybrid approach empowers domain-experts to solve critical challenges and extract key business value from both data and unlocked tacit knowledge.
AI You Can Trust with Auditable Decisions
Human-readable reasoning let users jump from insights to the logic that feed them enabling your technical people, subject-matter experts, and business executives to work on data and business logic in tandem.
Users always understand the why behind their decisions through quarriable, transparent, auditable, plain-language audit trails understandable by humans and readable by machines.
How it Works
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OUTSIDE THE BLACKBOX
Eight out of ten AI and ML projects fails in production today as they are inherently black boxes with minimal insights into their operation.
Revealing the knowledge surfaced within your data to understand your world more precisely is the difference between your organization’s success or failure to keep up with new technologies.
Hawkrel mitigate limits faced from contemporary AI by helping you deploy responsible AI systems with trust, and understand the “why” behind a problem to support your ethical AI policy and data privacy strategies.
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Tapping into the Bigger Mind Human Brain power with Machine Intelligence.
We are on mission to build technology that think like experts and produce operational efficiencies at scale. We believe the role of AI is to magnify the talents of people and not to replace them.
Knowledge is the key asset in any organization and mostly hidden away in expert’s minds. With Hawkrel, we help you untap it.

Contextual
Read moreHawkrel is a depository and an execution platform for heterogenous sources of intelligence. It is designed to enable new breakthrough in artificial intelligence by extracting the way humans solve problems and extrapolate experiences to apply new knowledge in foreign situations.

Privacy-Preserved
Read moreWhile collaboration is essential, companies and researchers need to protect their intellectual properties. We ensure this by integrating a privacy-enabled engineering architecture which enables companies to share secrets without giving them away.

Explainable
Read moreAll solutions are delivered with detailed explanation for how they arrived at their answers through complete audit trails. This helps organizations meet strict compliance requirements, improve productivity, and save cost by allocating budget based on delivered performance.
MAKING AN IMPACT
FOR TALENTS
Enabling subject-matter experts to contribute and get compensated for bringing their unique domain-knowledge toward addressing critical challenges.
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FOR BUSINESS
Empowering businesses with more transparent, compliant, and informed decisions to augment their intelligence at scale in their path to AI adoption.
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FOR INSTITUTES / GOVERNMENTS
Accessing breakthrough solutions with situational awareness by connecting critical challenges to a distributed network of heterogenous knowledge sources.
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