AI Design

AI Design Strategies

Design

AI systems and software requirements and design for public and private sector clients.

Build
Program and client stakeholder management for product or enterprise, cloud-based AI solutions, components, and test. Mobile apps.

Implement
On-site or remote systems imtegration and implementation; including data storage and data center, cyber, user training, and cloud host coordination.

Sell

Commercial pre-sales and sales support and negotiations; government capture and business development management.

We help US military, other government, and commercial clients choose the right AI solutions with optimized growth & flexibility included

Open source AI models and capabilities are skyrocketing so fast that everyone wants to climb onboard this new paradigm. But we can't afford this, as a world, due to the extreme data center energy stress that AI and crypto currency needs create. We help clients be environmentally responsible, balancing market 'needs' against market wishes, whilst optimizing energy savings.

Rethinking Large Language Models

LImiting parameters

6.7 billion v. 670 billion

Meta Llama vs. Deep Think search parameters - why the difference?

Button

Understanding the data-AI connection

Having access to a lot of data isn't the same as having useful information and actionable insight to support timely, critical decision making. Data Driven Architecture (DDA) is an IT architecture approach that creates systems and applications that are flexible, scalable, and responsive to changing data.

Real-world environments generate massive volumes of variable and high-dimensional data that challenges the deployment of ML algorithms. Systems seeking to incorporate IOT and social media data to pose significant parameter issues. Some social media data disappears, some transforms, some is allowed for certain uses but not use - it can be challenging.

Data-Oriented Architecture (DOA)

A DOA system stores data from different treatments (filters, parameters) to a data-set in the form of snapshots. An ML engineer can then reuse these snapshots to choose the most suitable ML model based on the available data. This is one affordable way of putting a loose organizational framework to asynchronous communication between system components; to ease integration of ML algorithms into large, real-world systems.