Helsinki, 22nd May 2024 – Digiole, a leading provider of innovative AI solutions for startup ecosystems, is thrilled to announce a pioneering collaboration with Economic Development Lethbridge (EDL) and the Regional Innovation Network of Southern Alberta (RINSA) to deploy the first AI Assistants designed specifically to support entrepreneurs within their region.

​This initiative marks a significant milestone in our mission to optimize entrepreneurship journeys by making use of artificial intelligence solutions.

The AI Assistants will serve as a robust 24/7 resource for entrepreneurs, providing them with critical support in relevant areas for entrepreneurs. For needs such as business planning, financial management, and market analysis.

This initiative will help to streamline entrepreneurial processes, enabling business owners to focus on growth and innovation.

By working closely with EDL and RINSA, we are creating a tailored solution that truly resonates with the entrepreneurial community and providing business retention and expansion solutions to SME’s.

​EDL and RINSA are equally enthusiastic about the collaboration:

Digiole is a global technology firm specializing in advanced digital interoperability solutions that facilitate ecosystem business models across various industries. With over a decade of expertise in fostering innovation and entrepreneurship ecosystems worldwide, Digiole offers consulting and training services to over 30 ecosystems. Digiole solutions are designed to seamlessly connect ecosystem actors, platforms, and data, enhancing ecosystem interoperability in a cost-effective and sustainable manner.

​Economic Development Lethbridge (EDL) is an independent, not-for-profit organization dedicated to fostering economic growth and prosperity in Lethbridge, Alberta. With a mission to strengthen and develop the local economy, EDL initiates and facilitates proactive economic development strategies, promoting Lethbridge as an excellent place to live, learn, invest, and do business. The organization collaborates with a wide range of stakeholders, including local businesses, government bodies, and educational institutions, to create a vibrant and diversified economic base that supports both traditional industries and emerging technologies. EDL also owns and operates Tecconnect, a technology incubator that provides vital support to tech startups and entrepreneurs, further bolstering the region’s innovation ecosystem.
 
As a founding member and active participant of the Regional Innovation Network of Southern Alberta (RINSA), EDL plays a crucial role in providing resources, mentorship, and connections to help startups and small businesses grow and succeed. RINSA is a collaborative network that brings together various partners, including post-secondary institutions, government agencies, rural and Indigenous economic development organizations and industry leaders, to create a supportive ecosystem for innovation. It focuses on fostering a culture of entrepreneurship, providing access to funding and expertise, and promoting the commercialization of new technologies and business ideas.

The Founders Shareholders' Agreement & Team Building Workshop by Startup Commons, which has been extensively consumed globally for over a decade, has been consolidated now into the first AI Assistant, covering the the team building and shareholders agreement AI topic.

​What to Expect from Team Building & Shareholders Agreement AI Assistant from an entrepreneurship support organization perspective

• Quick Setup: Deployed in less than a week
• Focused Expertise: Directly built from Startup Commons webinar's transcription, it offers specialized knowledge on the discussed topics.

Pros: 

✔️ Instant access to the knowledge base
✔️ On-demand, interactive support to explore specific queries in multiple languages.
✔️ Based on the given information, the advice provided is well-grounded
✔️ Easily integrate the AI on any website.
✔️ Gain insights about the questions asked by the audience

Cons:

❗Limited scope, as it only contains information from one webinar.
❗It does not cover queries beyond the discussed topics.

​What to Expect from Team Building & Shareholders Agreement AI Assistant from an entrepreneur perspective

Pros: 

✔️ Time-saving
✔️ 24/7 availability
✔️ Immediate Access to Information
✔️Consistent Advice:

Cons:

❗Limited Range of Topics
❗Lack of Depth in Content
❗Inability to Handle Complex Queries

​Key Statistics and Insights For The Entrepreneurship Supporting Organizations Derived From Users Interactions

One of the key features of the Team Building & Shareholders Agreement AI Assistant is that you will also be able to know which questions this AI has been able to answer adequately and what the gaps are in the knowledge, which will allow us to improve it and provide better mentoring to entrepreneurs.

The video below is a screen recording of the detailed records of interactions in chronological order, showing:
​

• Date/Time (timestamp of the interaction),
  
• Statement (user's query or statement),
  
• Answer (the response provided to the user) and
  
• Score (a numerical score that helps determine which pieces of retrieved information are most likely to contribute meaningfully to generating a useful and accurate output for the user. It ensures that the generative model focuses on the most relevant data, improving the quality of the responses).

Additionally, in order to guarantee privacy in this test mode for the AI Assistant, relevant information from the user such as IP address, geolocation, browser type, operating system, device type, cookies, time zone or language preferences has been disabled.

In just over 10 days, some fundamental actionable insights can be extracted that will immediately contribute to improve the AI Assistant performance and therefore enhancing the user experience.

Let's do, as a first step,  a score summary:

And then, let’s analyze common phrases to understand the actual questions or themes better. ​​

Here are some of the most common phrases found in the "Statement" column, which hint at recurring topics or questions:

• "empresas emergentes" (emerging companies) - Indicates frequent discussions or inquiries about startups.
• "company culture" and "foundational values" - Suggests a focus on establishing strong cultural foundations in early stages of business development.
• "qué medidas" (what measures) - Likely relates to what actions or strategies can be implemented in specific scenarios.
• "strong company" and "early stages" - Points to concerns about building a robust company from the beginning.
• "cultura empresarial" (corporate culture) - Indicates interest in how to develop or maintain a good corporate culture.

These topics may suggest that users are often interested in strategic advice for startups, particularly about building a strong foundational culture and implementing effective business strategies.

Actions to consider:

• Resource Development: Based on these common themes, we would consider reviewing the content addressing those questions and developing focused content in any format (guides, FAQs, or webinar transcription) that address these frequently asked questions more comprehensively.
• Feedback System Improvement: If these questions are repeatedly asked, it might indicate that existing responses or resources are not sufficiently addressing user needs or that the questions are globally common. Reviewing and enhancing these could be beneficial.

​Conclusions

By simply putting out this Team Building & Shareholders Agreement AI Assistant for some days, we are now able to capture insights that ​offer a multitude of strategic advantages:

1. It enhances entrepreneurs knowledge, allowing for a deeper understanding of their needs and preferences specifically on the topic matter, which in turn informs better resource allocation and this AI Assistant development.
2. Such a system improves customer service by refining response quality and efficiency, providing personalized experiences at scale, and maintaining high service levels.
3. This not only gives a competitive edge by identifying market trends and adapting to them proactively but also boosts operational efficiency through a real-time feedback loop.
4. Moreover, it enables scalable solutions that manage increasing interaction volumes cost-effectively

Therefore, this approach not only improves entrepreneurship support operations but also fundamentally enhances how support organizations engage with and serve their entrepreneurs, driving growth and satisfaction through test-measure-learn loops.

​​In a world where innovation, entrepreneurship, and startup ecosystems are celebrated as the dynamic powerhouses driving our societies forward, a glaring paradox emerges. These hubs, brimming with technology, innovation, and brilliant minds, are paradoxically mired in outdated operational practices. Why, in 2023, do our ecosystem operations and technology strategies resemble relics from the 1980s? Across the globe, every nation, irrespective of its development status, faces this anachronism.

The disconnect is stark and unsettling. The AI revolution has transformed industries, yet the technological underpinnings of our economic development initiatives remain woefully archaic. This chasm exists at every level, from international financiers to local economic development units. The private sector, while innovative in other realms, lags in ecosystem development, constrained by the necessity of public-private collaboration in establishing foundational infrastructure.

But there's a shift on the horizon. The era of multi million-euro barriers to advanced digital platforms is ending. A new paradigm emerges, one that synergizes the agility and global reach of the SAAS model with the bespoke, lock-in-free freedom of custom-built solutions. This approach isn't just strategically smarter; it's a cost-effective, time-efficient leap into the future of ecosystem development.

In the realm of startup ecosystems, effective data management is not just a necessity – it's a game changer. Existing tools for startup ecosystem development have offered partial solutions, but often at the cost of narrow view, flexibility, integration, and real-time data utilization. These limitations have led to disjointed experiences, data silos, and inefficient resource utilization, hindering the true potential of entrepreneurial ecosystems, and what worries the most, making startup progress slower.

​As an ecosystem grows and evolves, more digital resources and services are created to support startups and entrepreneurs in their different phases, new people decide to initiate the startup journey, more support is offered, more interactions among ecosystem actors are generated, etc. All of them are scattered in different tools, applications and platforms, increasing the ecosystem builders’ needs to connect them to help ecosystem users navigate in this complexity. Doing this manually simply limits the scalability factor to orchestrate the ecosystem and therefore its capacity to provide and serve startups and entrepreneurs with the right answers.

In this webinar, we are presenting the opportunities around machine learning to enable more automation and intelligence in your ecosystem. We will give attendees a behind-the-scenes glimpse to get to know the use case we are working with for automated ‘startup development phases’ -labelling for startup events. Plus, you will see our current prototype work of this first machine learning use case.

If there is a data model you would like us to improve or add, leave your request here and we will review to add it to our development plans to make it ecosystemOS compliant for global connectivity with other ecosystems. Or you can also subscribe to our ecosystemOS newsletter to keep you updated about the new data model releases.

The ecosystemOS tech team has started the formal process of iteratively releasing open standard data models for the context of innovation entrepreneurship ecosystems. We are making public the r​oadmap for open standard data model, covering domains and objects that are part of the innovation entrepreneurship landscape, providing details about entity types, attributes, relationships, integrity rules, and the definitions of those objects so that any ecosystem can iteratively use them for:

• Helping build common understanding of business data elements and requirements
• Providing foundation for designing databases
• Facilitating avoidance of data redundancy and thus prevent data & business transaction inconsistency
• Facilitating data re-use and sharing
• Decreasing development and maintenance time and cost

A first use case enabled with this first simple model is a global startup events portal to aggregate and distribute startup events data​. It provides a standards-based representation that can be used to encode and exchange events data in the context of innovation entrepreneurship format.

if there is a data model you would like us to improve or add, leave your request here and we will review to add it to our development plans to make it ecosystemOS compliant for global connectivity with other ecosystems. Or you can also subscribe to our ecosystemOS newsletter to keep you updated about the new data model releases.

It is the smallest possible unit of bundled items, that make logical sense to be combined together, viewed as business, designers and developers alike.