Tag Archives: #StartupVIC

Startup Vic’s Professional Services Pitch Night

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For the first of Startup Vic’s monthly pitch nights for 2018, professional services were put under the spotlight. There is a public dialogue on the types and numbers of roles that will disappear due to automation (the professions are no different) and here were four startups seeking to engage in that conversation. Assuming that every industry and every occupation is vulnerable to disruption (and should be alert to the potential opportunities that presents), why should accountants and lawyers feel left out?

Image sourced from Startup Vic Meetup page

Myaccountant

With the promise of enabling users to lodge their BAS return from a smart phone, this app is aimed at micro businesses that struggle with bookkeeping and accounting tasks. Since accounting software packages do not support direct BAS lodgement (although expect this to change…), the app charges $39 per BAS, with no bookkeeping or accounting fees, and shares the fee with the accountants who do the lodgement.

The app is able to extract data from vendor APIs such as Expert360, Airtasker, Uber, etc., and connect to users’ bank accounts. Since launching in January, the app has generated 200 sign ups, with very little direct marketing or paid acquisition so far. The app is also aiming to achieve ISO 27000 (information security).

The panel of judges would have liked to have heard more about the acquisition strategy, and how the app deals with income and expense categorisation, different tax rates, zero rated items, and export sales etc. They also wondered about the competition, and overseas markets

Contractprobe

Developed by Neural Contract, this product uses machine learning to review contracts in 60 seconds. Using a scoring model, it rates documents according to established best practice and bench-marking, suggest sample text for missing clauses, and identifies problems found.

The service is available for ad hoc use, under a monthly subscription, or as custom packages.

According to the founders, the service can save 40% of the time usually spent on contract reviews. It offers a high level of privacy – the uploaded contract, report and transaction ID is deleted upon completion (although it wasn’t clear what records are retained for the purposes of clause analysis, data and analytics – including client profiling and user context.)

To reassure any lawyers in the audience, the product stills relies on human input to apply judgment to the choice of clauses, for example. However, a clear value of the review process is ensuring that phrases and key words are properly defined in the contract.

The judges wondered where this product fits in with open source documentation and pre-drafted documents, whether there are specific verticals more suited to this service, and what trust and liability issues might arise. Is it more of a “clause-spotter” rather than an expert system? How does it address statutory clauses, and the question of whether clauses are actually enforceable?

The service has about 40 clients, including law firms, and is now moving into corporate clients.

Businest

This product is designed to help with cashflow management, which the founders describe as an “iceberg” issue. They point to data that suggests 87% of SMEs have issues with cashflow.

Claiming to use AI to coach SMEs and accountants, the goal is to allow business owners to focus on what they do best, and move accountants from “compliance to advisory”. Applying its own algorithm to cashflow analysis, the service also provides training content to advisors.

Offering both SME and advisor pricing models, the founders have launched a pilot with MYOB. They also point to market research and commentary (CEDR, AFR, CPA, CA…) that indicates the market wants it.

The judges felt that the banks won’t rush to endorse the service (although under the open banking data protocol, they won’t be able to prevent customers linking their accounts) because they are used to the interest they charge on overdraft facilities and credit cards.

Brandollo

This is a marketing tech start-up, aimed at SMEs that struggle to access tailored advice. Targeting B2B clients, in the professional services sector,  with less than 80 staff.

Briefly referring to the use of AI and ML, the service claims to reduce marketing costs by 80%. It offers a brand gap analysis and makes recommendations, that can be implemented without external help. The process looks at execution issues, content requirements, and actual solutions.

Aiming for 200,000 clients in 5 years (currently standing at 200+), the main competitor is Benchmarketing. Brandello offers a freemium model, with a 3-tier paid-for service. They can connect clients to experts, provide a quote to execute and then take a commission on the resulting solution.

 

Based on the judges’ verdict, the winner was Myaccountant. While the people’s choice was a tie between Myaccountant and Contractprobe.

Next week: The General Taxonomy for Cryptographic Assets

VCs battle it out in the reverse pitch night

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As part of the Intersekt FinTech Festival, the organisers, FinTech Australia partnered with Startup VIC and NAB to host a “Reverse Pitch Night”.Turning the tables on the usual pitch night, four VCs were invited to pitch to a panel of startup founders.

Representatives from Rampersand, Reinventure, YBF Ventures and NAB Ventures battled it out on stage to demonstrate why founders should want to work with their firms. Since I have been involved in pitching or presenting to two of these funds, and I know people involved with all four firms, I will aggregate these reverse pitches, highlight the common themes and try and pick out some of the key points of differentiation and/or competitor advantage.

Following a similar startup pitch format (problem, solution, team, achievement and future growth), each VC stressed the importance of getting the “right money”, and identifying the ways in which VCs can help with growth and people as well as capital. So it’s as much about how VCs can add overall value, rather than just the size of the cheques they can write.

Despite the supposed differences, there were a lot of similarities. There was much talk about how the VC model is broken, yet I didn’t see much in the way of novel funding or structuring solutions. Also, with NAB and Westpac directly involved in two of the funds, and ANZ linked to a third, isn’t this compounding the problem – aren’t banks part of the problem?

While having access to a bank’s balance sheet may result in larger cheques, the average size of individual investments looks to fall within a similar range. And of the deals that were referenced, a number were co-invested by the same funds and/or the same international partners. So doesn’t that itself restrict or constrain the variety of deals that can be struck?

On the positive side, most of the VCs allocate a substantial proportion (50%) of their funds for follow-on rounds. Some funds actively help to incubate the companies they invest in, even though they may still only take a minority stake. So the focus is on building a portfolio, and helping to scale the right companies. In one case, the VC has only invested in five out of 1,000 opportunities, so clearly there is a challenge with the screening process, or we just aren’t seeing the right startups.

Or maybe the smart startups realise they don’t need/want VC money in the first place? Only one of the four VCs specifically mentioned working with a startup that has launched an ICO – surely the most disruptive development to hit traditional VC funding in a long while?

Finally, given this was a FinTech-related event, I didn’t see any evidence of how these firms are using better technology to manage VC funding.

Surprisingly, given the reaction from the audience, the panel judged Reinventure to be the winner.

Next week: FF18 pitch night – Melbourne semi-final

Startup Vic’s EdTech Pitch Night

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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:

Become

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.

MoxieReader

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.

Cogniss

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

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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.

Amelie.ai

With a tag line of “powering the future of mental health” the team at Amelie.ai 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.

Businest

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.

AiHello

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.)

C-SIGHT

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?