Technology

Synergy's novel AI Application Platform solution includes an AI-powered, with machine learning, Dev-Ops methodology integrated with an innovative and unique Program Structure to guide the user to develop and operate Intelligent Applications.

Other options using AI simply generate code for programmers, so the result is a traditional program, with all of the traditional resources and activities performed by IT developers to make it work. While it speeds up traditional coding, it does not employ AI to create smarter programs, nor support non-technical users.

To harness the full power of AI we needed to figure out a better way to apply AI to applications and we did. We are the first to use AI and natural language such as English to specify, develop, and operate applications.


Synergy sets the groundwork for composable AI systems—where natural language builds, updates, and orchestrates multiple models across domains.

Why it matters: It aligns with the emerging trend toward AI agents, multimodal pipelines, and human-AI collaboration. This is done with prompting and workflows that walk the user through a program structure even if they are not familiar with the concept. We've also added innovation to the way a user interacts with the program, and its various visual displays.

LLM-based Natural Language Interface (NLI)

Accepts only natural language as input

Generates visible programming structure (e.g., a flowchart, logic graph, or editable pseudo-code)

Automatically builds and trains a neural network behind the scenes

Requires no coding knowledge and eliminates the need for traditional programming languages and compiling.

Allows human users to review, edit, and update the system in natural language.

As the demand for applications is projected to outpace IT personnel capacity by 500%, the Synergy System emerges as a game-changing solution:

1. Massively Expanded User Base

Advantage: Opens AI development to non-coders—business analysts, domain experts, educators, creatives.

Why it matters: AI coding platforms (like GitHub Copilot or Code Interpreter) still require technical understanding. This new model breaks that barrier.

2. Faster Prototyping and Iteration

Advantage: Instant design → test → iterate cycles with no manual coding.

Why it matters: LLM-generated code still requires debugging and deployment; a natural-language-first system could abstract that away and reduce cycle time dramatically.

3. Transparency & Explainability

Advantage: Users can see and understand the logic via visual programming structures.

Why it matters: Current AI coding often produces black-box solutions. Visual structures are crucial for compliance, audits, and trust—especially in finance, healthcare, and education.

4. Editable by Anyone, Anywhere

Advantage: Allows live updates and improvements by people closest to the problem.

Why it matters: AI developers often misunderstand domain-specific requirements. Empowering end-users to tune models themselves leads to better outcomes.

5. Lower Cost of Development and Ownership

Advantage: Reduces reliance on expensive ML engineers.

Why it matters: AI coding platforms still require hiring or contracting skilled developers; this model could slash costs across industries.

6. Competitive Differentiation

Advantage: A company that can make neural networks visibly understandable and editable gains an edge in industries demanding interpretability.

Why it matters: As regulations increase around explainable AI (e.g., EU AI Act), this feature may become not just an advantage—but a requirement.

Our unique Application AI Assistant uses natural language requirements to tell the AI Application and learning neural net what to do that bypasses the need for complex technical programming.

We figured out how to train our own AI which includes a neural net that simulates a human brain and learns as it goes. To get started, you create a document that describes the high-level requirements using our user-friendly method, and follow the menu choices to get your application.

The requirements include key data inputs to organize the application, English names of the processes to perform that get translated into algorithms behind the scenes, and the sequence or order of how you want the information to be processed.