Several of us from IEEE ICICLE Corporate L&D Group had a chat with Christopher Lind, Head, Global Digital Learning, GE Healthcare to talk about what it’s like to drive digital transformation in an iconic enterprise like GE — a very old company founded by famous Thomas Edison but turned very aggressive in transforming itself into a software company in the past decade or so. (reference: GE’S BIG BET ON DATA AND ANALYTICS)
Chris hosts a cool community called Learning Shark for L&D industry professionals, and also Learning Tech Talks on Friday mornings aiming to “Demystify the landscape of learning tech through unbiased conversations with technologies from around the world.“
Jessie: GE Healthcare and the whole GE group have been viewed as the leader of corporate innovations, often seen in management case studies such as in HBR. What role is the L&D department playing in driving innovations and the transformation of GE?
GE has operations in many industries, and different businesses have different ways to innovate and develop talent. Our role is to challenge the way the organization can reach and develop each employee. The old way of training has been there for decades and it’s not easy to change. You need to find a pain point and push on it really hard to change their way of doing things.
The L&D function is decentralized. L&D is supporting business units’ performances, solving their pain points, and helping them think differently for innovations to get to their goals (for example, selling better by thinking differently about the way they interact with people). The role of L&D is being invisible — people don’t feel we are doing L&D to them; they are just doing their jobs.
Jessie: Could you share the experiences of the digital transformation of L&D in GE Healthcare?
People will say we need to go digital, but their behaviors don’t change. For me, it’s about starting small. We have a long term plan and a short term plan. What are the low-hanging fruits that we can get? Even just getting people to think differently. For example, how can we get people to create digital content and deliver content in digital form? Also, this could threaten the identities of some people in L&D because they used to facilitate in-person training.
I’ve been doing a lot for people’s development and showing L&D colleagues the path forward in the transformation. It’s not just about technology; it’s more about resistance to change, fear of uncertainty, and the feeling of L&D’s identity being threatened by technology. There is a lot of change management involved. I tell a lot of stories to help people understand where we are going.
It needs to start with the executives, so we put executive education online. We have a model we call the “incubator” project to bring in new technology by starting with a small pilot group in a secret mode.
A data-based or data-driven approach is important, but storytelling is equally important. Two weeks ago, we got some data from a behavioral-based initiative we are rolling out. The data looked terrible, but I had an artful way of telling a story about it. Then suddenly, our team agreed that the data is a leading indicator of what we try to do and we should invest more in this project. Different perspectives and interpretations for the same set of data lead to different actions.
Zsolt: Based on your experience supporting digital transformation, what capabilities (or skills) do you think L&D professionals lack today? How did you address this barrier at GE?
Everyone has different digital capabilities, but they at least need to understand how technologies work so they can use new tools to do their job in a new way. That’s why I started Learning Tech Talks. Automation is coming to L&D really fast. I am working on a project so that machines can build interactive e-learning content at a speed much faster than people. L&D practitioners don’t need to worry they will be replaced by machines; they are still needed to architect the content, which the machine still can’t do.
Cheryl: How is your L&D team embedded within the departments? Do they have a data science partner they work with or are they expected to have that skill set?
The role of L&D is still a facilitator, but a digital facilitator now that they need to manage digital communities. There are new roles, but not every L&D person needs to do that. I have system architects and data analysts, so not every L&D person needs that kind of skillset. But they need to understand how data analytics work.
Our L&D department didn’t create another data scientist team (it would become a silo), it’s from a centralized team. The data scientists who work with L&D are like our business partners who understand L&D context and know how to interpret our data. But the data isn’t only from learning systems; we include data on what’s happening in CRM or in engineering works because they matter.
Zsolt: What does your digital landscape look like? What platforms, tools, applications, processes did you find most effective in digital learning?
I don’t have a top list for the tools. Based on the reality and nature of your business, your priority, and the maturity of L&D team, the answers will change. We do need the basic LMS functionalities, but not the corporate LMS. We need something where you can bolt on new things like social elements, discussion threads, and gamification, not to have them all at once. We have a roadmap to build an ecosystem step by step, and the system can meet where we are and get us where we want to go. Content management systems and content development tools are all must-haves. Then, it continues to evolve with animated videos, infographics, voice recognition plus Natural Language Processing (NLP) for giving feedback, AR, VR, etc. My goal is to implement adaptive learning to let AI help give personalized recommendations.
I built a capability map, not a tool map. On the map, we imagine if we can do everything we want, and also know what we have now. Then we need to prioritize. For example, adopting a cloud service won’t take long, but implementing adaptive learning requires preparation. Don’t bite off more than you can chew.
Go out and ask for help. And, technology vendors are our business partners, so we need to align with them well (on vision, perspective, etc.) first to work together well in the long term.
Brandt: Previously you said you have internal incubator projects for new technologies, and you also talk about bringing in outside partners, what’s the line between these two?
I never bring in any outside partner before we have a pretty solid proof of concept. If you bring them too early, people might resist them before they even understand what you’re trying to do. We explore people’s thoughts on new technologies, such as using voice recognition plus NLP to give sales feedback, to collect input before we decide anything.
We invite small groups of people to test new stuff and build prototypes, but they can’t talk to anyone. It works. People who join this secret project really engage with it and become our big advocates. I won’t say it always works perfectly. Sometimes people get too excited and share it out too early and it brings problems. Often I give these projects cheesy names, and we have had a lot of fun with this incubator method, which adds energy behind what we do.
Jessie: Start small, make changes gradually, never underestimate the difficulty of behavior changes, but go “experimenting with prototypes to collect data with users”, these are really great lessons from you.
One example is the VR project I do for soft skills. People looked at me like I have nine heads as I presented it the first time. I still went with my “asking for forgiveness instead of permission” way to give it a try. Then, it took off like a rocket!
Like Thomas Edison, it’s GE, right? We just try it, and sometimes the bulb lights, sometimes it doesn’t, sometimes it takes a long time. It’s scary, it’s exciting, and it’s fun all at the same time.
Cheryl: What Organizational Network Analysis tools are you using at GE? How is it informing management training programs and promotions? (Jessie: this article on MIT Sloan used GE collaboration analytics as a model case)
In terms of the L&D department thinking beyond L&D about the way people are operating and connecting and what’s happening in the organization, we aren’t quite there yet.
Cheryl: Have you explored AI tools that identify and aggregate generally available curriculum resources for use internally?
When I brought in LinkedIn Learning, people thought it was just another content provider; they didn’t see what I was getting at. What I was getting at was being able to do the skills benchmark with other industries and tie that back to our own talent ecosystem (recruiting or developing talents). We were just starting to think about it at about the same time as a lot of other organizations.
In terms of AI, throwing AI at bad data is the recipe for crisis, which is why I have been hesitant to implement AI on our own data. We don’t have a ton of data. LinkedIn can build better AI models than what we can build in-house because of the data they have.
We’ve thought a lot about leveraging automation. Automating the capturing of sales data, for example, because salespeople don’t have time to input data into Salesforce. We have tried a couple of ways to automate content development.
Jessie: Thanks for sharing a great story with us, Chris.
After 60 years of computing power growth supported by Moore’s Law, and 60 years of AI development, we’ve entered an Industry 4.0 era, with connected networks of everything including data. The speed of innovation, competition, and change is unprecedented. The pace of iteration can be as short as hours with automated iterations by Machine Learning. (as below, image credit: Robin.ly) The question for every organization is: How fast are you iterating toward your goal?
In the midst of the COVID-19 crisis, it is more urgent than ever that modern organizations be agile and efficient in responding to a rapidly changing environment and competing to survive. A learning organization is a company that facilitates the learning of its members and continuously transforms itself. [1] Now the speed of organizational learning, which is an orchestration of an organization’s employees with structures and processes in place, needs to accelerate. Corporate L&D (learning and development) department plays an important role.
To facilitate that acceleration, iterations of data are required. In our interview with Nate Hurto, SAP, he emphasized the importance of iterations loops, macro and micro, needing to be as fast as possible, as being fundamental to an intelligent enterprise. Nate Hurto reflected that when these intelligent organizations start to “combine data from different systems, there is a transformation that happens.” [2]
Learning Engineering and Organizational L&D
In marketing technology (MarTech), highly granular personalized or persona-targeted recommendations have been changing user behavior for many years. Those techniques have often been used for political causes as well. In driving a learning organization to accelerate learning, by supporting every employee to learn faster, there is still much to be done. The macro and micro iterations in current enterprises, even in a lot of Learning Management Systems (LMS), usually aren’t built with the science of learning in mind.
Enter “Learning Engineering”.
“Learning Engineering is a process and practice that applies the learning sciences using human-centered engineering design methodologies and data-informed decision making to support learners and their development.” — IEEE ICICLE
Behavioral science interventions have been developed to promote a variety of prosocial behaviors, such as healthy eating habits, physical activity, getting medical check-ups, voting, and achievement in schools and colleges. Learning Engineering incorporates solid learning science, pedagogical best practices (based on a lot of research done in the past few decades), empirical approaches, and design thinking as well as teamwork. To learn more about cognitive principles for designing effective remote learning, check out The Science of Remote Learning by Goodell, J. & Kessler, A. (2020).
Learning Engineering combined with an algorithmic approach plus machine learning leads to true power and speed in pushing learning in an organization forward . This combination of engineering principles with algorithmic solutions and machine learning is already happening in other engineering disciplines. This combination could be called “AI for learning” for short.
Considerations for L&D in the Corporate Context
Let’s re-examine L&D in the corporate context, which is more complex than in K12 or higher education contexts.
L&D is a means to an end and is part of a bigger system.
Employees have no time to learn, and real learning often happens while working.
“Saving employee’s time is the golden KPI, and what is desired is enabling learning in the workflow”[3] (Interview with Alban Jacquin, Learning Experience & Innovation Director, Schneider Electric) The key question is how to enable that.
“L&D team needs to be able to measure the impact of L&D”, according to Alban Jacquin, his team measures and monitors 100+ metrics. [3] Being able to correlate L&D and business outcomes and know ROI of investment in R&D is desired by executives.
“Better skill assessments are much needed”, according to Alban. [3]
Issues about privacy, data integration, and safety can’t be neglected. Hopefully, we want to have more than data in learning applications. According to Nate, “We are a privacy conscientious company, so we don’t log granular behavior events”.[2]
New knowledge is constantly discovered and created at work or in interactions between people, across many tools or systems. How can this be captured? (It seems Microsoft Project Cortex is aiming to solve this problem, and is providing resources for developers).
AI has already started to make an impact in corporate L&D [4] (Interview with Avinash Chandarana, Head of MCI Institute, MCI Group),
L&D departments need to become the performance consultant and learning experience architect for the business, not only order takers, according to Alban Jacquin [3]. Businesses need to consider what the L&D departments need to catch up, or how the business can support and augment L&D’s work with technology.
Enterprises have been facing challenges of digital transformation as well as skill gaps for the AI/automation age, the COVID-19 is only worsening it.
AI for Learning
In an interview with Chris Littlewood, head of data science at Filtered, he explained how his startup created an “AI for Learning” solution for enterprises. Filtered needs to work closely with enterprise L&D teams to build a skill framework that’s aligned with their goals. The more specific each skill definition is, the better. L&D teams need to be able to curate and label a certain volume of content to the skill framework for training purposes. From there, Filtered can use Machine Learning to infer and make recommendations. The system interacts with users via questions to understand users, and tries to collect other kinds of data besides learning data for better modeling. Chris reflected on the lessons learned from their journey, and realized that although AI is fancy, if enterprises don’t realize that its impact links to business survival, the project will only be categorized as“innovation” (meaning not urgent). [5]
The value brought by Filtered is mainly content discovery. It’s not an intelligent tutoring system (or adaptive learning system) yet, but its impact has been much appreciated by its clients, among them the MCI Institute. Avinash Chandarana, Head of MCI Institute, mentions that their L&D team needs only two people now. The AI engine by Filtered increased the engagement of learners significantly, and enhanced the productivity of the L&D team. Avinash even uses Filtered to help MCI’s clients to engage their own clients in online events. It has been very much needed during the COVID-19 crisis, as almost all in-person events are canceled.[4] In a learning context, AI supports learner agency for self-paced learning and it’s especially needed now since remote work has become the new normal.
Alban of Schneider Electric notes that they have also used an AI engine to recommend learning activities for learners. A set of skill models for over 800 job roles is the foundation of their AI model. The AI engine inferencing can help learners more dynamically because the skill framework isn’t updated frequently. Both push and pull approaches are necessary. Learning in the workflow is a new thing for them, and they are trying to push in that direction (we can spend a whole day talking about this). Alban does hope to be able to better assess skills.[3] We see that on many CLOs’ wish lists.
What then is needed in order to build an intelligent tutoring system, for effective personalized or micro-targeted learning at scale? A good conceptual framework is to refer to the Total Learning Architecture (TLA) by ADL, which always pushes the boundary of human capability development because of its mission to train the military with efficiency. In short, there are four main pillars to Total Learning Architecture (TLA), listed below, plus pedagogical engineering. There are also quite a few relevant works being done and ongoing at IEEE society (such as IEEE AIS Standards)
A competency framework (aligned with business goals in the corporate context)
Knowledge modeling (for better sequencing and structuring of the learning process)
Learner modeling (to model many dimensions of a person)
Experience tracking across relevant systems (this is where Experience API (xAPI) plays its special role. xAPI enables us to collect behavior data at work in order to link learning and performance).
Remember, learning technologists need to look outside of learning scope, use technologies to level up organization performance, and be able to measure its impact on business outcomes.
Management Strategy is an Important Dimension
To drive the acceleration of learning for employees, there is another dimension of management strategy we need to consider. Andrew Saidy, VP Talent Digitization of Schneider Electric, implemented Open Talent Market (OTM) a year and a half ago. OTM has had a big impact on retaining talent, matching dynamic needs in the organization with available talent, driving mobility in the company and motivating learning. OTM links to an LMS and this implementation helps employees own their learning.[3] OTM is the management strategy which creates a drive for learning when people want to change or level up their career. To make OTM work, there must be a good modeling of skills for jobs and employees, so the AI engine can do match-making in less than 12 seconds. A great strategy needs the support of good engineering.
Think Bigger about What’s Possible with AI
In a survey done by Wiley, 55% of employers surveyed want to rely on AI to close skill gaps. There are several possibilities.
“AI for learning” can help with learning/training efficiency (1st thought),
AI can augment worker performance (there should be a good integration of learning and work, imagine an AI assistance integrating learning, performance support and actionable data ),
AI/automation can replace human workers in completing aspects of their jobs, so where there is not enough talent, maybe let’s have AI help.
From a business perspective, if a task can be done by a machine, it could result in significant savings and extreme speed. What machines are capable of is growing every day. In certain kinds of tasks, machines perform much better than humans. All that machines need are great algorithmic modeling and problem-solving strategies. That’s why we should keep in mind the opportunity of collaboration between humans and AI.
Repetitive tasks can be done by Robotics Process Automation (RPA), but higher level tasks can be automated too. For example, there is not enough talent capable of building and deploying Machine Learning models now, so many “AI builds AI” tools, based on AutoML, are emerging. They reduce the demand for data scientists and enhance the efficiency and quality of modeling and AI deployment. Machines can carry out trial-and-error steps, workflow of best practices, and logical reasoning on data and knowledge, among others. Now AI can assist with the highly-skilled and complicated IC design process, and beat experienced IC designers in place-and-route circuit layout because there are too many possibilities and too complicated.
AI Enabled Learning Organization
How fast an organization can effectively iterate toward goals is the decisive factor for thriving in these uncertain times. Key factors for that success are (1) data & integration (2) modeling & a problem-solving approach and (3) valid measurement. We have talked a lot about the opportunity of a Learning Engineering approach to drive a learning organization, but what is also important is the collaboration between AI and humans for overall performance. There are still a lot of opportunities left at the intersection of learning engineering, AI/automation, and data-driven enterprise operations.
Invitation for Case Study Submission
As enterprises are facing the pressure of digital transformation right now, we invite you to submit your case studies for this program intended to surface best practices or solutions from problem-solvers, especially bold startups — Corporate Digital Transformation Enabled by L&D or Augmented Workers. We also like to invite enterprise leaders and investors to join our panel discussions that build knowledge together. Enterprises can put out their specific quests.
Email for participation: Contact[at]WiseOcean[dot]Tech
References:
[1] Pedler, M., Burgogyne, J. and Boydell, T. 1997. The Learning Company: A strategy for sustainable development. 2nd Ed. London; McGraw-Hill.
Jessie Chuang, Co-founder of Wise Ocean & Classroom Aid, Vice-chair of IEEE ICICLE
With a background in Science and Engineering, high-tech. Industry R&D and consultancy experience with corporations on learning technologies and AI, Jessie has been dedicating to build knowledge networks for big challenges and “AI for Executives programs” for AI digital transformation.
Barish Golland, Training Lead for Enterprise Resource Planning (ERP) implementation at the University of British Columbia
With a 20 year background in education, teaching, educational technology support & learning technology ecosystem management, Barish is passionate about enabling organizations to leverage the best-in-class learning technologies to empower employees with the training and readiness they need to succeed.
Jim Goodell, QIP, Vice-chair IEEE LTSC, IEEE ICICLE Steering Committee
Jim is a thought leader in the world of learning engineering and data standards, Vice-Chair of the IEEE Learning Technology Standards Council, and Senior Analyst at Quality Information Partners. He chairs the IEEE Industry Consortium on Learning Engineering Competencies, Curriculum, and Credentials SIG, the IEEE Competency Data Standards Workgroup, Adaptive Instructional Systems Interoperability Workgroup, and serves on the ICICLE Steering Committee. He leads the development of CEDS.ed.gov data standards with QIP and AEM. With the U.S. Chamber of Commerce Foundation, he co-led the development of the T3 Innovation Network’s LER Wrapper specification.
Climate tech companies just had their best quarter for fundraising in almost two years, drawing $16.6 billion. Venture capital investment into climate tech is doubling YoY, but the funding gap is not closing fast enough.The first question about investing in climate tech comes into mind – Is this category a capital-intensive play? So, many investors choose only climate software. Actually, there are many variables in answering that question.
One of questions we’ll ask startups is – what’s your capitalization plan? It will impact life or death, speed of growth, capital efficiency for startups, and returns for investors. There are strategies for partnerships and business models to consider for a less capital-intensive growth path. And, when to exit and how? In this panel, we like to cover capitalization and financing strategies in different stages for climate tech startups.
About ClimateTech Investor Panels – This is for accredited private equity angel investors, venture capitalists, and corporate/institutional investors to share insights and investment opportunities and catalyze collaboration to help ClimateTech startups.
Speakers
Paul Burgon, Co-chair of Keiretsu Forum CleanTech Committee, GP of Exit Ventures – Paul is a dynamic operations and investment executive who has invested over $3 billion in almost 100 different companies at high rates of return. Paul balances 1) strategic analysis and strategy development, 2) creating and implementing processes that map to company strategy and 3) team leadership, consistent execution and continuous improvement to create outstanding results.
Karen Sheffield, Finance Director at Visa, Founder & Managing Partner of Pachamama Ventures – Karen is a managing partner of a venture capital firm investing in US early-stage climate tech companies. She is also a Finance Director at Visa and has previously worked for PepsiCo and American Airlines. A self-described operator turned investor, Karen began angel investing 3 years ago and, ever since then, has dedicated much of her time to uncovering opportunities in unlikely places. Karen holds a double degree in Finance and Economics from Texas Christian University (TCU) and an MBA from The University of Texas at Austin.
Interviewed by Jessie Chuang, the topics cover 3 major parts:
Funding gaps and sources for ClimateTech startups and projects
Business and partnership models deciding capital intensity & plan
When to exit and how
Takeaways:
Karen:
I grew up in Peru, and in the country, climate impacts a lot of everyday decisions we make, that’s why I am so passionate about climate investments.
Early-stage startups going to build a proof of concept (POC) need to get initial funding from friends and families, angels (several angel groups focusing on climate tech or impact investing), and government grants – there are so many now for climate tech companies, including IRA and more. Talk to grant writers to help you. Getting grants is hard, but getting funding from VCs before POC is even harder. Non-dilutive funding sources such as grants from governments are crucial for early-stage companies. and now more foundations and family offices want to invest in climate startups. Catalytic capital funds emphasize more on impact instead of maximizing financial returns solely. After you have revenue, there are revenue-based financing mechanisms that can help.
I have 15 years of experience working in Fortune 500 companies, I’ve witnessed the interest in building corporate VCs (CVC) growing significantly. Visa and Pepsi all have CVCs. Most CVCs might take a board seat if they invest to guide the startups, they have very broad interests, not limited to their current business lines, and have lots of funding to experiment. The chief sustainability offices (CSO) in large companies want to partner with climate tech innovators and achieve more than ESG compliance. Of course, there are pros and cons to consider when working with CVCs or corporate partners.
Paul:
The best outcomes in climate tech happen when companies can align their development milestones with their capital strategy.
A big difference between the cleantech 1.0 era and now is that the capital stack for climate tech has been developed rapidly and much more mature in the past 5-7 years. There are billions of hardware or cleantech funds, we see CVCs exploding, we see customer-financing models (customers as investors to build win-win outcomes, be creative, and build partnerships with customers), DOE’s loan office can support commercialization stage projects, and there are growth funds and infrastructure funds looking to back hard tech, etc. These didn’t exist 5-7 years ago, all are narrowing funding gaps substantially for first commercial operations. Especially CVCs can become your potential acquirers, so, look at companies in the ecosystem, and build partnerships.
Funding first commercial productions is still the largest funding gap risk. Although not as fast as we like, we do see that large infrastructure funds are coming down and VCs are going up to narrow the gap and reduce the capital intensity of climate tech startups. Also, a lot of innovations aren’t really as capital-heavy as most people imagine.
Paul wrote the following supplementary notes for reference.
Quote from a large climate tech grant writer: Grants are the quintessential way of filling these valleys of death with patient, risk-tolerant, non-dilutive capital. Government grants have historically done a fairly good job in earlier stages with technology risk by supporting R&D. More recently, with the massive flood of public funding into the climate space, there is sufficient funding for the government to put tens and even hundreds of millions of dollars behind a single project, enabling climate tech companies with high CapEx to undertake the large-scale demonstrations or first-of-a-kind deployments that bridge the commercialization valley of death.
Examples of investors with deep pockets for growth and physical assets are starting to raise and deploy investment vehicles earmarked for climate:
Mega-firms: Mega-shops like Brookfield, TPG, Apollo, KKR, Carlyle, Stonepeak, and Blackstone are also flocking to climate raising tens of billions to finance the net-zero transition.
DOE Title 17 Clean Tech Loan Program:
The Title 17 program can support technologies at each deployment milestone—first-of-a-kind deployments that solve applied engineering challenges; follow-on deployments that establish engineering, procurement, and construction excellence and lower total project costs; substantial scaling of deployment and manufacturing capacity to drive advancement along the learning curve; and education of commercial debt markets to enable broadly available debt financing.
Commercially ready technology has been demonstrated at near commercial-scale under expected process conditions with results supporting the expected performance of the proposed deployment. Performance data from testing at pilot and demonstration scales (confirming at least a Technical Readiness Level 6) must have been performed and be available for review in order to confirm commercial readiness. Applications will be denied if the proposed project is for research, development, or demonstration.
No minimum loan amount. It can usually cover 50-70% of eligible project costs, and loans for up to 30 years. Low interest rates. Check out the criteria here.
Thoughts on how and when to exit:
There’s no perfect answer for when to exit. Some people sell too early and leave a lot of money on the table. There are also many, many stories of startups and boards wanting to hold on and try to go public or sell for a billion dollars, and they end up losing a lot of money or almost everything a few years later due to new competitive technology, the team falling apart, or whatever.
In my opinion, you should consider a few basic things in deciding the right time to sell:
The required return of the founders and the key investors/board. Have they made enough money to justify the time and investment?
What are the technology, market and other risks that could derail the future success of the startup and create a loss scenario? You need to be very honest with yourself on this point, most startup teams are way too optimistic about their near-total invincibility in the next several years. There are always big things that can go wrong, even the unknown factors that aren’t visible today.
Do you have a realistic exit opportunity to pursue in the near to medium term? Is an exit a viable option?
Is the team ready for an exit? Sometimes people factors can preclude a successful exit. The team has to be ready, presentable, and complete.
Is the business ready? Are major customers happy, is the business litigation-free, large problem-free? Are major contracts all renewed, not about to expire? Get due diligence issues cleared up before starting the exit process.
If all of these issues are a go, then I would probably err on the side of exiting sooner rather than waiting of a perfect scenario and perfect valuation. Don’t be greedy, take a win, and stay on with the new owner for a while, maybe a long while, or go to your next big thing in my opinion.
How to exit
How to exit is a completely different podcast. I have an hour podcast on just how to exit. If you are ready to exit, you need great advisors and a board that is very experienced in the tactical best practices of exits.
About Global League
Global League is a vetted network of accredited and professional investors collaborating on We select startups from top seed investors(VCs/angel groups) and build a disciplined process to get collective intelligence for investments and venture building. Collaboration and co-investing are our core strategies to connect silos and help the most impactful ventures.
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