Startup Vic’s EdTech Pitch Night

EdTech or EduTech? Even Startup Vic can’t seem to decide. Whatever, this education-themed pitch night was the latest event in their highly popular monthly events, held in conjunction with Education Changemakers, and EduGrowth.

Apart from the naming convention, there is also some clarification needed around the scope and definition of “education(al) technology”. First, because it’s a very broad spectrum (does it include e-learning, e-books, MOOCS, LMS?). Second, is it more about the “delivery” than “outcomes”? Third, is it only about formal pedagogy, or does it also include discretionary, self-directed and non-curriculum learning?

And so to the pitches, in the order they presented:


With the aim of “teaching kids to explore, design and navigate their future“, Become is essentially a platform for early-stage career coaching. While their app is still in development (although there is a bot in use already?), Become has been running in-person workshops and other programs to test and validate the concept. The solution uses AI and machine learning technology, but it wasn’t very clear how this will actually work – maybe there are some core profiling and preference tools, some career mapping based on proprietary algorithms, and recommendation engines drawing on the data analysis?

Using a freemium model, the full service will cost $40 per student per annum. The core audience are years 5 to 8, and part of the schools adoption strategy will focus on getting high school career advisers on-board, with additional parent advocacy.

I’ve no doubt that career advice is an important part of the syllabus, but just as important are life-long learning, resilience, adaptability, and developing self-awareness and a sense of purpose. But if nothing else, in the words of the founder, Become puts the “why” back into learning.


This digital reading log is all about “inspired independent reading“. Supplementing the paper-based records widely in use, the app enables children to record their reading activity, and helps teachers to assess pupils’ reading progress, based on the titles and numbers of books read, and their associated word counts and vocabulary. (In future, the app may deliver content and instructional aids.)

Using a machine learning algorithm (“like a fitness tracker”), the app can set reading challenges, and measure reading growth. Tests may be another add-on, but from what I can see, the app does not test for comprehension or context-based reading and interpretation skills. (After all “reasoning” is the 4th “R” of education – along with reading, writing and arithmetic.)

Currently launching with an ambitious social media and outreach campaign, MoxieReader already has paid sign ups from teachers, many of whom are paying with their personal credit card, and is enjoying a 30% conversion rate, and 30% referral business.

Priced at $7 for teachers per class per month, plus $100 per school/building per month (individual teachers who already subscribed will get a rebate), there is also an opt-in donation model for parents to recycle used books.


This is a development platform and market place for education apps. Built on game based learning and rewards packages, it also makes use of analytics and data insights to help teachers and designers build their own products.

Having seen a demand among health and well-being users, the platform is also suited for apps designed to support behavioral change, workplace learning and social learning.

Access to the platform involves a $500 set up fee, plus $50 per month per app (plus scale rates by number of users and advanced add-ons).

The platform also supports micro-transactions, for downloaded content and apps. At present, there is no formal process for teachers to embed pedagogy into the game structure. Content vetting is also a manual process, combined with experience sharing and peer ratings – but a content certification process is in the pipeline.

Revision Village

Helping students to prepare for external exams (specifically, the IB maths) this product replaces traditional in person and in class programs, with an online resource.
Also, although revision practice largely relies on past test papers, the founders have identified a chasm between the concepts taught, and the questions asked.

Developed in response to teacher demand, this subscription-based learning resource has
translated into higher results and fewer fails.

The platform is looking to extend the curriculum beyond maths, but this will largely depend on being able to license content from the relevant examination boards and syllabus providers, such as the IB.

Access is not dependent upon being logged into a school network or intranet, as it is only a web app (with individual and site licenses).

The Revision Village website claims the product is used by “More than 32,000 IB Students and 710 IB Schools”. However, it would seem that not all of these are paid-for subscriptions, as the pitch mentioned a critical mass would be 100 schools (out of a total of 2,500 IB schools) paying $2,000 each (although this is separate to the parent market).


Overall, I liked the tone and format of the pitches –  the products all seemed worthy endeavours, and the founders are no doubt passionate about education and learning. But I was left feeling underwhelmed, by both the content and the tech being deployed. (I guess I needed more than just passing references to “AI, machine learning and algorithms”.) All of these products rely on significant adoption rates among schools – which are some of the hardest institutional customers to sell to – and to be successful in international markets presents a further challenge, given differences of language, content and educational systems.

In the end, even the judges found it hard to pick a winner, as there was a tie for 1st place, between Become and MoxieReader. I would probably concur, as they had the edge in terms of both individual learning outcomes, and broader educational benefits.

Next week: Copyright – Use It Or Lose It?

StartupVic’s Machine Learning / AI pitch night

Machine Learning and AI are such hot topics, that I was really intrigued by the prospect of this particular StartupVic pitch night. First, this was a chance to visit inspire9‘s recently established Dream Factory – a tech co-working facility, maker space, and VR lab in Melbourne’s western suburb of Footscray. Second, the Dream Factory, housed in a landmark building owned by Impact Investment Group, was a major beneficiary of LaunchVic funding, and this event could be seen as a showcase for Melbourne’s tech startup sector. Third, with so many buzzwords circling AI, it offered a great opportunity to help demystify some of the jargon and provide some practical insights.

Image sourced from StartupVic

Instead, the pitches felt underdone – probably not helped by the building’s acoustics, the poor PA system, and the inability of many of the audience to be able to read the presenters’ slides. I wasn’t expecting the founders to reveal the “secret sauce” of their algorithms, or to explain in detail how they program or train their “smart” applications. But I had hoped to hear some concrete evidence of how these emerging platforms actually work and how the resulting data is specifically analyzed and applied to client solutions.

With a tag line of “powering the future of mental health” the team at are hoping to have a positive impact in helping to reduce suicide rates. Unfortunately, judging by the way some key statistics are presented on their home page, the data (and the methodology) are not as clear as the core message.

Using technology to help scale the provision of mental health and well-being services, combined with mixed delivery methods, the solution aims to offer continuity of care. Picking up on user dialogue and providing some semi-automated and curated intervention, the presentation was big on phrases like “triage packages”, “customer journey”, “technical architecture”, “chatbots” and of course, “AI” itself, but I would have like a bit more explanation on how it worked.

I understand that the platform is designed to integrate with third-party providers, but how does this happen in practice?

Only when asked by the judges about their competitive advantage (as there are similar tools out there – see Limbr from a previous pitch night) did the presenters refer to their proprietary language models, developed with and based on user trials. This provides  a structured taxonomy, which is currently English-only, but it can be translated.

There were also questions about data privacy (not fully explained?) and sales channels – which may include workplace EAPs and health insurers.


According to the founder, “dashboards and KPIs only diagnose pain, Businest fixes it“. In short, this is intelligence business analysis for SMEs.

With a focus on tracking working capital and cashflow, as far as I can tell, Businest applies some AI on top of existing third-party accounting software. It identifies key metrics for a specific business, then provides coaching and videos to change business behaviour and improve financial performance. There is a patent pending in the US for the underlying algorithm, which prioritizes the KPIs.

Again, I was not totally clear how the desired results are achieved. For example, are SMEs benchmarked against their peers (e.g., by size/industry/geography/maturity/risk profile)? Do clients know what incremental benefits they should be able to generate over a given time period? How does the financial spreadsheet analysis assist with improving structural or operational efficiencies that are outside the realm of financial accounting?

Available under a freemium SaaS model, Businest is sold direct and via accountants and bookkeepers. A key to success will be how fast the product can scale – via partnering and its integration with Xero, MYOB and QuickBooks.


I must admit, I was initially curious, and then totally bemused, by this pitch. It started by asking some major philosophical and existentialist questions:

Q: How do we define “intelligence”?
Q: Are we alone? Or not alone?

No, this is not IBM’s Watson trained on the works of John-Paul Sartre (cf. Dark Star and the struggle with Cartesian Logic). Instead, it is an analytical and predictive app for Amazon sellers. It claims to know what products will sell, where and when. And with trading volumes worth $2.5m of goods per month, it must be doing something right. Serving Amazon sellers in the US and India (and Australia, once Amazon goes live here), AiHello charges fees based on fixed licences and transaction values. The apparent benefits to retailers are speed and savings.

Asked where the trading data is coming from, the presenter referred to existing trading platform APIs, and “big data and deep learning”. It also uses Amazon product IDs to make specific predictions – currently delivering 60% accuracy, but aiming for 90%. According to the founder, “Amazon focuses on buyers, we focus on sellers”. (Compare this, perhaps, to the approach by Etsy.)


A new service from the team at Pax Republic, this latest iteration is designed to avoid some of the policy and reputation issues involved with managing, supporting and protecting whistleblowers. Understanding that whistleblowers can pose an internal threat to brand value, and present a significant human risk, C-SIGHT provides a psychologically safe environment for the Board, C-suite and workforce alike, and can act as an early warning system before problems get out of hand.

Sold under a SaaS model, C-SIGHT analyses text-based and anonymous dialogue, with “real-time data sent to different AI apps”. I understood that C-SIGHT combines human and robot facilitation, while preserving anonymity, and also deploys natural language processing – but I didn’t fully understand how.

In one client use case, with the College of Surgeons, there were 1,000 “contributions” – again, it was not clear to me how this input was generated, captured, processed or analysed. Client pricing is based on the number of invitations sent and the number of these “contributions” – what the presenter referred to as an “instance” model (presumably he meant instance-based learning?).

Asked about privacy, C-SIGHT de-identifies contributions (to what degree was not clear), and operates outside the firewall. There was also a question from the judges about the use and analysis of idiom and the vernacular – I don’t believe this addressed in much detail, although the presenter did suggest that the platform could be used as a way to drive “citizen engagement”.

Overall, I was rather underwhelmed by these presentations, although each of them revealed a kernel of a good idea – while in the case of AiHello (which was the winner on the night), sales traction is very promising; and in the case of Businest, industry recognition, especially in the US, has opened up some key opportunities.

Next week: Bitcoin – to fork or not to fork?