Makpar’s AI/ML and MLOps journey has been both difficult and rewarding . Like many other organizations the lack of a singular proven path to machine learning success left us unsure of how to take the next (or even the first) step on our journey.  We decided to use the pressure tested templates, and lessons learned, of our DevSecOps journey as a lighthouse to guide us through this fog. This prior experience showed that business and technical teams must work together and share the same priorities.

Veridat offers a Trust-as-a-Service (TaaS) solution for B2B contracting, where blockchain technology aggregates a catalog of immutable, timestamped, and verifiable data records. Our light-weight, cloud-native service (RESTful API) inserts into existing research workflows. Without exposing the underlying data themselves, unique hashes are used to confirm whether records are complete and accurate. Veridat integrates the needed payment systems, obviating the need for customers to own or handle native tokens.

KPMG's LEAP includes an intelligent chatbot, built on Microsoft Azure QnA maker and Azure Bot services, that anticipates user needs and is prepopulated with questions and answers surfaced directly in the application to provide end user support. There is no constraint on the type, content of, or number of questions and answers that can be included.

Agencies have accumulated mass quantities of structured and unstructured data and content over many years. At each interaction with your agency, citizens create data and that data needs to be managed.

What if you could predict any one person's best, next role?

Candidates and employees today rely on personal networking, professional relationships, and self-advocacy to find and get picked for their next role. But how can we be sure they're the best person for the role, at this time in their career?

How do we even the playing field when everyone is using LinkedIn as their resume? Some people may over-state their abilities, while we know that under-represented communities like women often are shy about showcasing their true talent.

From internal meetings to external briefings to speeches at congress and all across the government, many agencies today are drowning in video.  They can post on YouTube but it’s often just too much for people to watch.  People want to people to find what they are looking for quickly.

As government agencies try to adopt AI to accelerate their digital transformation, there is a need for services that enable faster application of AI to common scenarios, without requiring any machine learning expertise.

As government agencies try to adopt AI to accelerate their digital transformation, there is a need for services that enable faster application of AI to common scenarios, without requiring any machine learning expertise.

CALI is an automated machine learning evaluation tool built to streamline the evaluation of vendor proposals against the solicitation requirements to support the Source Selection process. The tool covers four (4) components:compliance, form compliance, reps and certs compliance, and requirements compliance.