by Linda Rawson | Aug 6, 2025 | Artificial Intelligence, Federal Government Contracting
At DynaGrace Enterprises, we believe that clarity is the cornerstone of ethical contracting. As President, I’ve seen how vague language, misaligned expectations, and rushed scopes can derail even the most promising projects.
Technology moves fast. Contracts must move with intention. — Linda Rawson
Clarity in contracts means:
- Defining deliverables with precision
- Aligning scope with mission
- Embedding ethical boundaries and feedback loops
- Ensuring all parties understand their role in the system’s impact
We use the Soulprint Framework to guide our approach:
- We begin with intention
- We protect stakeholders through clear terms
- We reflect shared values in every clause
- We evolve agreements to meet real-world needs
- We tell stories that empower—not confuse
Why It Matters
In federal contracting, clarity isn’t just operational—it’s energetic. It sets the tone for collaboration, trust, and transformation.
Whether we’re building AI systems, deploying cloud solutions, or crafting public-facing platforms, our contracts must be as thoughtful as our technology.
A contract is a container. What we put inside it shapes the future. — Linda Rawson
Our Commitment
At DynaGrace, we don’t just sign contracts—we shape them. We work with agencies and partners to ensure every agreement is clear, conscious, and aligned with purpose.
Let’s lead with clarity. Let’s contract with care.
🔗 Learn more at https://dynagrace.com
Listen to the Podcast at: https://govcon-biz.com/episodes/
by Linda Rawson | Jun 11, 2025 | Business
DynaGrace Enterprises is proud to be recognized on the 2025 Women Tech Council Shatter List, an honor that underscores its dedication to fostering diversity and inclusion in technology. This annual list highlights companies that actively foster opportunities for women in STEM, promoting leadership representation, mentorship, and equitable career growth.
Empowering Women in AI and Emerging Technologies
At DynaGrace Enterprises, we believe innovation flourishes when diverse perspectives are embraced. Led by Founder & CEO Linda Rawson and Vice President Jennifer Remund, DynaGrace combines AI-driven data science, autonomous sensor networks, and trusted AI systems with a culture of collaboration, empowerment, and strategic leadership.
Our team excels in data-driven intelligence, predictive analytics, and AI-enhanced decision-making, bridging both government and commercial applications while ensuring women have leading roles in these transformative fields.
What the Shatter List Means for DynaGrace
Making the Shatter List reinforces our commitment to:
✅ Hiring, mentoring, and elevating women in AI and data science
✅ Creating a workplace that champions innovation, inclusion, and leadership growth
✅ Expanding opportunities for women in government contracting and defense technology
✅ Pushing boundaries in AI-driven decision support systems
Looking Forward
This recognition is more than an award; it’s a testament to our ongoing mission. As AI and data science continue shaping industries, DynaGrace remains dedicated to ensuring women are at the forefront of innovation.
We invite others to join us in building a future where diversity drives progress.
Learn more about the Women Tech Council Shatter List here
Want to explore career opportunities with DynaGrace? Check out our employment page and connect with us to be part of the change!
by Dr. Jane Fitzgerald | Jun 17, 2023 | Business, Data Science
The Truth About Data Scientists.
Data science is the latest buzzword in the business sector. Everyone, from business professionals to beginners, is talking about it. Data science is one of the most popular and promising career paths for skilled and up-to-date professionals.
So, what the heck is data science in business?
Image Credit: Flickr
Traditionally, data science is researching, collecting/acquiring, and analyzing a vast volume of data, including data mining and data programming skills. Data scientists are skilled professionals that organize and analyze a vast amount of data for businesses/organizations.
Since the dawn of the 21st century, data science has evolved far beyond its traditional threshold. Rapid changes in modern business management with regards to the birth of the internet motivates organizations to uncover more useful information, stay competitive, and ensure customer satisfaction. Organizations must learn how to improve functions and optimize processes through data science. The only way data-scientists can make this possible is by mastering every stage, making up the data science life-cycle.
Five Stages of the Data Science Life-Cycle
Data scientists seeking to deliver results in an insightful, understandable, and compelling way must be efficiently skilled in every stage.
Technically, there are five stages in the life cycle, and these are:
Stage #1. Capture
Under this stage, data scientists must be vast and efficient in data acquisition/collection, professional in data entry, skilled in signal reception, and data extraction.
Stage #2. Maintain
Skill sets required for this stage include data warehousing, good knowledge of data cleansing, effective data staging, excellent data processing skills, and data architecture.
Stage #3. Process
At this stage, the data scientist initiates data mining, and then proceed to cluster/classification. The last two-know-how skills required in this stage are data modeling and data summarization.
Stage #4. Analyze
This phase requires extreme exploratory/confirmatory data analytic skills, and expertise in predictive analysis, solid regression skills, text mining, and superb qualitative analysis.
Stage #5. Communicate
Communication is the last stage of the life cycle. The skill sets the data scientist is expected to have here are;
a. Data reporting (vital to organizations in meeting their overall goals and objectives),
b. Data visualization (a roadmap for businesses module implementation), and lastly,
c. Business intelligence and decision making for an overall business drive.
How Does Data Science Impact a Business?
Businesses use data science to improve product quality and day-to-day operations diagnostically.
Data scientists with high-level technical skills help businesses identify vital questions and collect
Image Credit: Wikimedia
data from multiple data sources. Next, they organize the information obtained, and translate the extracted results into actionable solutions, and communicate the same to management for positive business decisions.
These positive skills account for why data scientists have become prominent and sought after in all industries. Their ability to build and analyze algorithms with strong programming knowledge, quantitative skills in linear algebra and statistics, plus excellent communication skills makes them the knight in shining armor for any organization.
The vitality of data scientists in modern business practice cannot be overemphasized.
Data science and data scientists are a crucial aspect of every business setup in the 21st century. They do not just help businesses achieve their operational, strategic, and financial goals, but also help optimize acquisition, growth, and retention in customer success through the information it provides.
However, poorly utilized data science provokes huge financial loss in an organization.
Conclusion
Data scientists have the power to shape how organizations conduct business in line with customers’ changing needs and the rapid development of technology. There is a growing need for data scientists in the business sector. The job is ranked as the best profession in the United States for three consecutive years—2016-2018—and still growing. Also, the demand for experts in data science has increased by 28% in 2020, and from all indications, there is a sign of it slowing down soon.
by Dr. Jane Fitzgerald | Jun 17, 2023 | Artificial Intelligence, Emerging Technology, General, Information Technology
What is Artificial Intelligence (AI)?
Artificial intelligence (AI) is a computer-controlled entity’s ability to perform cognitive tasks and respond flexibly to
Image Credit: Piqsels.com
its environment to increase the probability of achieving a specific goal. The program can learn from data, from experience, and can mimic actions similar to human beings. But it does not generally use biologically measurable methods.
AI describes the computer’s ability to execute tasks to accomplish the desired objectives. It also means that a computer can effectively reduce or even eliminate human activities.
Popular examples of chess, go, and dota intelligence demonstrates computers in certain areas and outperform human capabilities. Natural language processing (NLP) and machine learning algorithms are currently the most known fields for AI.
Types of AI
- Artificial General Intelligence (AGI)
- Artificial Narrow Intelligence (ANI)
- Artificial Super Intelligence (ASI)
What is Artificial General Intelligence (AGI)?
AGI is still a concept of theory, recognized as Ai with a human-level cognitive capacity in a broad range of fields, including language processing, image processing, computational functioning, reasoning, etc.
We’re quite a long way from AGI system development. The AGI program will need to involve thousands of tandem-working artificial narrow systems that interact with one another to mimic human thought. Also, in advanced computing systems and facilities such as Fujitsu’s K or IBM ‘s Watson, a single second of neural activity simulates within 40 minutes. It applies to immense complexity and interconnections between the human brain and the scale of the task of constructing an AGI with the current resources.
What is Artificial Narrow Intelligence (ANI)?
ANI is the most popular type of AI on the market now. Such AI systems are designed to solve a single problem and can perform one function very well. They have limited capabilities by design, such as recommending a product for e-commerce users or weather prediction. It is today’s only form of artificial intelligence. They can approach human functioning in particular situations and, even in many cases, surpass it. But only in highly controlled environments with a limited range of parameters.
What is Artificial Super Intelligence (ASI)?
Here we are almost into science fiction, but ASI alias as the logical development of AGI. An Artificial Super Intelligence system (ASI) could exceed all human capacities; this will involve decision-making, sound decisions, and even issues like improving the art and developing interpersonal ties.
When Artificial General Intelligence succeeds, AI systems could quickly boost their ability and push into fields that we could not foresee. While the distance between AGI and ASI is relatively narrow (some say just like a nanosecond because that is how quickly Artificial Intelligence will learn), the long path to AGI itself looks like a dream far into the future.
Image Credit: Wikimedia
What is the Purpose of AI?
Artificial Intelligence aims to aid human performance and help us make high-level decisions with far-reaching consequences. That’s the response from a technical standpoint. From a philosophical viewpoint, Artificial Intelligence can help humans lead more fulfilling lives free of hard labor and help manage the vast network of interconnected individuals, businesses, states, and nations to work in a manner that’s beneficial to all humanity.
Currently, Artificial Intelligence aims to simplify human effort and to help us make better choices by all the different tools and techniques that we have invented. Artificial intelligence, similarly marketed as our Last Innovation was a development that would create breakthrough technologies and services. It would also transform our way of life exponentially and eventually eradicate conflict, injustice, and human stress.
That’s all in the far future, though – we’re quite a long way from those kinds of outcomes. At present, the primary purpose of AI is to improve companies’ process efficiency, automate resource-intensive tasks, and produce business predictions based on hard data rather than good feelings. As with other technology, companies and government agencies must finance research and development expenses before they are available to laypeople every day.
The application of AI?
AI is used in different fields to provide insights into user behavior and to provide data-based recommendations. For example, the predictive search algorithm used by Google previous user data to predict what a user would type in the search bar next. Netflix uses past user data to decide what movie a user wants to see next, link the user to the app, and maximize watch time. Facebook uses previous users ‘ data to automatically suggest tagging your friends based on their facial characteristics in their images. AI is used in large organizations to simplify the life of an end-user. In general, the applications of artificial intelligence will come under the category of data processing, including:
- Data filtering and search optimization to give the most relevant results
- Logical chains for if-then reasoning serves to execute command strings based on parameters.
- Pattern detection to recognize essential trends for useful insights in large datasets.
- Applied probabilistic models to predict future results
Why does it matter?
The Internet-enabled global communication for all and impressively changed our way of working, living, and interacting. With process automation, AI is supposed to do the same; this will affect the consumer sphere, but it will also influence more repetitive business processes or simple decisions.
Some of the effects most discussed would be autonomous driving, but analysis, customer service, regulation, legal, and management are fields in which AI can improve productivity significantly. This cross-industry influence makes it critical that nearly everyone participates in AI.
Where are we today?
Even though AI has been around for over 50 years, we are still in the early stages. Factors such as processing power, worldwide networking, and cloud technology have just begun to open up artificial intelligence opportunities.
With the large enterprise data sets available – massive data hype, the three main phases of artificial intelligence segments into pattern recognition. The second phase currently involves commercializing deep learning algorithms, which enable real learning systems through neuronal networks. There are still a few years of abstract and reasoning intelligence, but its development is now beginning.
First experiments already were undertaken, but business applications currently focus on phase two-giving technology plenty of room for performance and adoption.
by Anika D | May 18, 2023 | Business, Emerging Technology
by Anika D | May 12, 2023 | General
