TORONTO–(BUSINESS WIRE)–lt;a href=”https://twitter.com/search?q=%24MENE&src=ctag” target=”_blank”gt;$MENElt;/agt; lt;a href=”https://twitter.com/hashtag/earnings?src=hash” target=”_blank”gt;#earningslt;/agt;–Menē Inc. (TSX-V:MENE) (US:MENEF) (“Menē” or the “Company”),
an online 24 karat investment jewelry brand, today announced financial
results for the first quarter ended March 31, 2019 (“Q1 2019”).
All amounts are expressed in Canadian dollars unless otherwise noted.
IFRS Revenue of $2.7 million, a $1.7 million (163%) increase
year-over-year (“YoY”). Non-IFRS Adjusted Revenue of $2.9
million, an increase of 151% YoY.
- Gross Profit of $0.7 million, an increase of $0.5 million (298%) YoY.
Gross Margin expanded by 900 basis points, from 16% in Q1 2018 to 25%
in Q1 2019.
Generated $0.3 million in Free Cash Flow in Q1 2019, the net of
operating cash flow less capital expenditures.
Reduced Net Loss by 26% to $1.1 million from $1.5 million in Q1 2018.
Non-IFRS Adjusted Loss decreased by 54% YoY to $0.6 million.
Sold 8,182 units of jewelry through 7,354 customer orders, an increase
of 7,242 units (770%) and 6,469 orders (731%) respectively compared to
Gold Weight Sold increased by 17 kilograms (115%) and Platinum Weight
Sold increased by 9.6 kilograms (685%) from Q1 2018.
Strong Tangible Common Equity of $17.8 million, with $17.5 million in
cash and cash equivalents and $16.2 million in short-term investments
as of March 31, 2019. Tangible Common Equity increased by 58% YoY,
demonstrating the Company’s ability to access cash to grow the
business and its high-margin and low fixed-cost business model.
- Introduced 99 new product designs during the quarter.
Launched “Menē x”, a new product category of limited-edition jewelry
collections designed in collaboration with select creators, artists
and tastemakers. Unveiled first collaboration with world-renowned
fashion photographers Inez van Lamsweerde and Vinoodh Matadin (“Inez &
- Raised $20 million in a Debt Financing Round with a strategic lender.
IFRS Consolidated Income Statement Data
|FY 2019||FY 2018||
July 11, 2017
|Gross profit (CAD)||678,814||983,840||208,408||229,461||170,486||12,143|
|Gross margin (%)||25%||28%||10%||16%||16%||19%|
|Total comprehensive loss||(1,166,288)||(2,681,362)||(1,691,124)||(919,106)||(1,348,026)||(1,702,048)|
|Non-IFRS Adjusted Revenue (CAD) 1||2,914,297||3,948,113||2,346,622||1,891,608||1,162,777||67,114|
|Non-IFRS Adjusted Gross Profit (CAD) 2||723,686||1,106,524||246,287||311,623||190,806||12,752|
|Non-IFRS Adjusted Loss 3||(577,218)||(469,487)||(1,136,242)||(758,895)||(1,251,091)||(1,639,950)|
|Total Shareholders’ Equity (CAD)||17,833,109||18,516,087||10,077,520||11,251,166||11,878,195||13,192,937|
|Inventory balance (kg of gold) 4||222||244||135||131||90||54|
|Units of jewelry sold||8,182||9,111||6,168||2,920||941||80|
|Jewelry weight sold (total kg)||43||51||35||23||16||1|
(1) The Company adjusts its revenue by adding back the value of jewelry
that the Company bought back from customers, or was returned by
customers, and discounts given to customers. These adjustments are made
to assess the gross revenue before deducting these items from revenue
per IFRS. See Non-IFRS Measures for a full definition.
(2) The Company adjusts its gross profit by adjusting for Non-IFRS
revenue and the attributable weighted average cost of sales for the
value of jewelry that the Company bought back from customers, or was
returned by customers, and discounts given to customers. See Non-IFRS
Measures for a full definition.
(3) The Company adjusts its total comprehensive loss by adjusting for
Non-IFRS Adjusted Gross Profit, and removing the impact of non-cash
expenses, consisting of depreciation and amortization, stock based
compensation, and a one-time listing expense, the fair value of
5,984,750 shares issued for the amalgamation with Amador Gold Corp.’s
subsidiary in Q4 2018. See Non-IFRS Measures for a full definition.
(4) Inventory balances in kilograms of gold are calculated by taking the
total Canadian Dollar (CAD) inventory value at each quarter-end date,
and dividing the value by the CAD gold spot price per gram.
(5) The period July 11, 2017 to December 31, 2017 and the fiscal year
ended December 31, 2018 are audited figures. The period Q1 to Q3 2018
have been reviewed by the same independent audit firm, KPMG. Q1 2019 has
not been reviewed.
(6) The Company began generating sales to an invite-only group in
October 2017. The Company began selling to the general public in January
Statement from Founder & CEO Roy Sebag:
Menē continues to show compelling organic growth and sales momentum. In
Q1, we generated over $2.7 million of sales, $0.7 million in gross
margin, and $0.3 million in IFRS Free Cash Flow. It is important to
remind our shareholders that this business has only been in operation
for 15 months at the quarter-end date. Following the completion of our
debt-note funding and a repayment of a portion of the historic loans
from Goldmoney Inc., our balance sheet is strong and well-positioned for
the next few years. We remain focused on building our brand equity
within the fashion, art, and jewelry cultural segments, seeing that with
each passing day, our brand is being embraced by popular thought leaders
and tastemakers. As of today’s date, we have over 30,000 registered
customers from over 20 countries around the world. Inventory levels
remain strong and are being built up in anticipation of a strong
2019-2020 season (October-February). I am very proud of the hard work
and dedication shown by our team and the disciplined way in which we are
building this company and its business model. My personal focus this
quarter has been in setting the infrastructure for several C-level
executive hires in Paris and Toronto which will help the company scale
its operations and position Menē for sustained growth in the years to
come. I look forward to updating our shareholders on these developments
as they formally materialize.
This news release contains non-IFRS financial measures; the Company
believes that these measures provide investors with useful supplemental
information about the financial performance of its business, enable
comparison of financial results between periods where certain items may
vary independent of business performance, and allow for greater
transparency with respect to key metrics used by management in operating
its business. Although management believes these financial measures are
important in evaluating the Company’s performance, they are not intended
to be considered in isolation or as a substitute for, or superior to,
financial information prepared and presented in accordance with IFRS.
These non-IFRS financial measures do not have any standardized meaning
and may not be comparable with similar measures used by other companies.
For certain non-IFRS financial measures, there are no directly
comparable amounts under IFRS. These non-IFRS financial measures should
not be viewed as alternatives to measures of financial performance
determined in accordance with IFRS. Moreover, presentation of certain of
these measures is provided for year-over-year comparison purposes, and
investors should be cautioned that the effect of the adjustments thereto
provided herein have an actual effect on the Company’s operating results.
Non-IFRS Adjusted Revenue1 is a non-IFRS measure. The Company
adjusts its revenue by adding back the value of jewelry that the Company
bought back from, or was returned by customers, and discounts given to
customers. These adjustments are made to assess the gross revenue before
deducting these items per IFRS revenue.
Non-IFRS Adjusted Gross Profit2 is a non-IFRS measure. The
Company adjusts its gross profit by adjusting for the additional revenue
and associated cost of sales added back for the value of jewelry that
the Company bought back from, or was returned by customers, and
discounts given to customers.
Non-IFRS Adjusted Loss3 is a non-IFRS measure. The Company
adjusts its total comprehensive loss by adjusting for Non-IFRS Adjusted
Gross Profit, and removing the impact of non-cash expenses, consisting
of depreciation and amortization, stock based compensation, and a
one-time listing expense, the fair value of 5,984,750 shares issued for
the amalgamation with Amador Gold Corp.’s subsidiary in Q4 2018.
For a full definition of non-IFRS financial measures used herein to
their nearest IFRS equivalents, please see the section entitled
“Non-IFRS Financial Measures” in the Company’s MD&A for the three months
ended March 31, 2019.
About Menē Inc.
Menē crafts pure 24 karat gold and platinum jewelry that is
transparently sold by gram weight. Through mene.com, customers may buy
jewelry, monitor the value of their collection over time, and sell or
exchange their pieces by gram weight at prevailing market prices. Menē
was founded by Roy Sebag and Diana Widmaier-Picasso with a mission to
restore the relationship between jewelry and savings. Menē empowers
consumers by marrying innovative technology, timeless design, and pure
precious metals to create pieces which endure as a store of value.
For more information about Menē, visit mene.com.
This news release contains or refers to certain forward-looking
information. Forward-looking information can often be identified by
forward-looking words such as “anticipate”, “believe”, “expect”, “plan”,
“intend”, “estimate”, “may”, “potential” and “will” or similar words
suggesting future outcomes, or other expectations, beliefs, plans,
objectives, assumptions, intentions or statements about future events or
performance. All information other than information regarding historical
fact, which addresses activities, events or developments that the Menē
Inc. (the “Company”) believes, expects or anticipates will or may occur
in the future, is forward looking information. Forward-looking
information does not constitute historical fact but reflects the current
expectations the Company regarding future results or events based on
information that is currently available. By their nature,
forward-looking statements involve numerous assumptions, known and
unknown risks and uncertainties, both general and specific, that
contribute to the possibility that the predictions, forecasts,
projections and other forward-looking information will not occur. Such
forward-looking information in this release speak only as of the date
Media and Investor Relations Inquiries:
of Investor Relations
+1 647 494 0296
Chief Financial Officer
Strainprint™ Technologies Welcomes Organic Medical Cannabis Producer to Growing List of Subscribers
Strainprint Technologies Ltd, the leader in cannabis data and analytics is pleased to expand their nation-wide coverage with the addition of Stewart Farms.
Stewart Farms is a late-stage applicant headquartered in Alberta that is building a 100,000 sq. ft. vertical aquaponics farm in Saint Stephens, New Brunswick. They will be utilizing automation, vertical farming, and land-based aquaculture to produce medical grade organic cannabis for both recreational and medical markets in Canada. All of their products will be free of herbicides, pesticides and synthetic nutrients. At full capacity, they will reach more than 10,000 kgs per year of organically farmed cannabis and more than 200,000 kgs of organic tilapia.
Stewart Farms’ long term vision is to deliver tools and products to support future customers and patients during their health and wellness journeys. “Through our partnership with Strainprint we gain direct access to their incomparable data and analytics tools. This will aid us in educating a wider audience on the medical benefits and best practices of cannabis-based medicine,” said Tanner Stewart, Co-founder & CEO of Stewart Farms. “We know our customers are looking to consume more than dried cannabis. We know Canadians of all legal ages are trying to sort through their personal engagement with cannabis as a medicine. What we want to know, on an ongoing basis, is what is and is not working for people. Strainprint’s patient-led data services will give us the insight needed to create custom products and further understand the benefits of our existing products. Finally, our goal is to partner with companies and teams that truly have people’s best interest at heart. Caring about a patient’s success is what makes up the core of the Strainprint team. We couldn’t be happier to move forward with them at our side.”
Stewart Farms will access the Strainprint Analytics web platform, the most sophisticated cannabis analytics platform available to improve product development. Strainprint Analytics is built on top of the largest and most granular scientific data set of its kind in the world, with more than 1.3 million anonymized, patient reported medical cannabis outcomes and more than 65 million data points on strain efficacy.
“We’re thrilled to provide our real-time, crowd sourced data to an environmentally conscious company that uses innovative, cutting edge farming technology to produce top-tier medical cannabis,” said Strainprint CEO, Andrew Muroff. “Our organizations are equally committed to improving lives using cannabis therapy, and our shared core motivation is to provide guidance and support to help cannabis patients achieve their health goals.”
SOURCE Strainprint Technologies Ltd.
Field Trip Ventures Inc. Retains KCSA Strategic Communications as Public Relations Counsel
Greenery Map, the world’s first and only cannabis search engine to allow users to search for cannabis products based on their desired mood, medicinal use, and method of consumption, is now offering users – both businesses and consumers – the opportunity to register and connect to the app in an entirely mobile way. Previously, businesses were required to access GreeneryMap.com from a desktop in order to manage accounts and connect to the inventory API system, but that road block has been removed, allowing for an entirely mobile experience through the Greenery Map app.
App users turn to Greenery Map to help them decide on the strain of Cannabis and product that is right for them depending on their desired mood or medicinal effect. Following the matching, consumers are given a detailed history and description of the strain so that they can make educated purchases, cutting down on time that budtenders need to spend with patrons and speeding up the purchase process. Consumers can then see all of the ways to purchase said product, including the nearby dispensaries that have it in stock, online options, and even delivery options through the app. Greenery Map fully integrates with a dispensary’s inventory API system to update in real-time and give consumers real-time information on dispensary stock for the item they are looking for. The Greenery Map system also provides cannabis businesses in both the B2B and B2C sectors a private database to share and sell information and products. For Cannabis businesses, Greenery Map also offers the opportunity to open bank and merchant accounts through their partnership with PayHouse Consulting Group.
SOURCE Greenery Map
WeBank, IBM and Other Organizations Jointly Held the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019
Once a concept, AI is now ushering in a key stage of application. What’s the solution to the data silos among businesses? Given the enhanced regulation on data at home and abroad, what’s the solution to data privacy and security concerns? What’s the status quo of Federated Machine Learning and how to establish an ecosystem for FML in the future?
WeBank, IBM and other organizations jointly held the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19) in conjunction with the 28th International Joint Conference on Artificial Intelligence (IJCAI-19) on Aug. 12, 2019, to further discussion on these issues.
President of IJCAI, Chair of FML Steering Committee, Chief AI Officer of WeBank Professor Qiang Yang delivered opening remarks at the workshop. Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google presented keynote addresses. In the panel discussion, top scholars from WeBank, Bar-Ilan University, IBM, Squirrel AI, Google, Huawei, Clustar, Sinovation Ventures and many other renowned enterprises and universities shared and discussed their findings and experience in FML as an emerging AI technology.
This workshop received 40 papers, of which 12 were presented during the workshop, 19 presented via poster. Awards include Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award, Best Presentation Award. Selected high quality papers will be invited for publication in a special issue in the IEEE Intelligent Systems journal. All these attracted numerous scholars to engage in discussions and join efforts for building the FML ecosystem.
Experts from IBM and Google Share Groundbreaking Findings with a Focus on the Theory and Application of FML
Privacy and security are becoming a key concern in our digital age. On 25th May last year, the implementation of General Data Protection Regulation (GDPR) by the EU, the toughest Act on data privacy protection, stressed that user data collection must be open and transparent. A series of laws and regulations from China and overseas also pose new challenges to the traditional way of handling data and model for cooperation. Seeking ways for AI to adapt to this new reality became top priority, a demand that led to this workshop on FML.
A wealth of solutions and breakthroughs were shared by Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google in their speech on FML.
Besides how FML can help tackle challenges in the business world, Dr. Shahrokh Daijavad also shared the concept of Fusion AI, which means to train models on widely distributed data sets, but fuse them to produce one equivalent to what centralized training would yield. “Unlike traditional machine learning, in Fusion AI, model parameters are shared and data is not transferred, which makes Fusion AI model better than models that moving data centrally.” Given the widely distributed data, the development of Fusion AI and FML became ever important and imminent.
“FML enables machine learning engineers and data scientists to work productively with decentralized data with privacy by default,” said Dr. Jakub Konečný from Google. He also shared with us how FML works and its use cases at Google. In the case of Gboard, as on-device data is privacy sensitive or large or is more relevant than server-side proxy data, and labels can be inferred naturally via user interaction, the application of Federated RNN compared to prior n-gram model can increase the accuracy of next-word prediction by 24%, and the click rate of prediction strip by 10%.
Major Figure Panelists Discuss the Way Ahead for FML
The moderator of the panel discussion, AI Principal Scientist of WeBank Dr. Lixin Fan joined panelists including Professor Benny Pinkas from Bar-Ilan University, Dr. Shahrokh Daijavad of IBM Academy of Technology, Chief Architect of Squirrel AI Dr. Richard Tong, Research Scientist of Google Dr. Jakub Konečný, Dr. Baofeng Zhang from CTO Office of CBG Software in Huawei, Executive VP of Clustar Dr. Junxue Zhang, VP of AI Institute in Sinovation Ventures Dr. Ji Feng and other experts in a host of in-depth exchanges with attendees, to shed light on the way ahead for FML.
Experts shared thoughts in the panel discussion on questions including but not limited to: How to meet the security and compliance requirements? Is there a way to extend the value of data while observing user privacy and data security? Given the classic trade-off between data regulation and development of AI, how to achieve the long-term goal of establishing a stable and win-win business ecosystem?
List of Award-Winners
Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award and Best Presentation Award selected by all attendees were announced at the closing of the workshop.
Best Theory Paper Award:
Preserving User Privacy for Machine Learning: Local Differential Privacy or Federated Machine Learning? By Huadi Zheng, Haibo Hu & Ziyang Han;
Best Application Paper Award:
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. By Yiqiang Chen, Jindong Wang, Chaohui Yu, Wen Gao & Xin Qin;
Best Student Paper Award:
Quantifying the Performance of Federated Transfer Learning. By Qinghe Jing, Weiyan Wang, Junxue Zhang, Han Tian & Kai Chen;
Best Presentation Award:
Federated Generative Privacy. By Aleksei Triastcyn and Boi Faltings.
President of IJCAI, Chief AI Officer of WeBank Professor Qiang Yang, Chief Architect of Squirrel AI Dr. Richard Tong, andVP of AI Institute in Sinovation Ventures Dr. Ji Feng presented the awards.
“The mission of this International Federated Machine Learning Workshop is to facilitate further understanding in the academia, business community as well as legal and regulatory institutions by promoting the establishment of FML ecosystem in the hope that more businesses will join and build a platform for students aspired to work in FML to find research teams that suit them,” said Professor Qiang Yang.
Held Aug. 10-16, 2019 in Macao, China, IJCAI-19 is one of the leading International Academic Conference on AI, attracting over 3000 AI research personnel and experts. The 1st International Workshop on Federated Machine Learning (FML’19) was a highlight for experts joining this event. Visionaries in the academia and industrial sector expressed the willingness to be part of the effort for academic research, application of FML in the future, and the development and boom of AI ecosystem.
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