This past year has been one to remember for us at Neurolabs. Despite challenging market conditions, we successfully transitioned from a high-end research lab to a functioning business with deliverable products and a strong recurring customer base. We have achieved incredible success in such a short span of time, and it would not have been possible without the hard work and dedication of our team.
In this post, I’d like to reflect on the achievements of the past year and look at what’s ahead for 2023.
From Proof to Product
As we entered 2022 we had a clear set of challenges to overcome: mainly, we needed to better prove the value and potential behind synthetic data, and we needed to showcase that either can be made accessible as a product.
At the beginning of the year, we had a sort of “ivory tower” proof, based on delivering an inventory management for supermarkets solution using fixed cameras. The issue with this use case was that it was very constrained in terms of complexity and hardly showcased the necessity for synthetic data, let alone its potential when put up against real data and manual annotations. Our priority thus became to tackle more challenging scenarios that pushed the limits of what was thought to be possible in retail automation — we adapted our tech to work with more diverse products and environments and made it compatible with simple mobile phone cameras that any field marketing agency would have access to.
As our product and platform developed, we began to enter into a wider range of partnerships. We went from dealing with one-off requests to forming long-term relationships and gaining recognition from influential players in the industry.
Finding the Ideal Customer
Garnering more market attention allowed us to reconsider who our ideal clients might be and how we hoped to interact with them. To give an example, my favourite success story of the year was our work with Sagra, a Polish company that needed a way to audit pharmaceutical products on in-store shelves. Their challenge was that these products, which were packaged in cardboard boxes, were essentially identical, with the only variance being the number of pills per box. The team at Sagra had access to PDFs of their product’s packaging labels, which is all we needed to quickly create accurate 3D models to train our algorithms with.
This was, to put it mildly, extremely exciting for us. Not only were we able to train and deploy image detection algorithms for these boxes in no time, but Sagra had just the right challenge and materials to perfectly integrate into our pipeline. We realised that probably nobody else out there was going from PDFs to production-level computer vision algorithms this quickly, and that maybe nobody else could. This made me extremely proud and also gave us a clear direction to move forward in terms of our services and tech.
Partnerships like this also helped us better understand our position within the retail space. Previously, we had found it extremely hard to interact with retailers and CPGs themselves, often due to our strong tech focus and background. By taking a step back and instead working with solution providers, we were able to lower this transactional friction and more easily communicate with people who have their own developers, are building their own apps or smart carts or autonomous stores, and who understand the complexity of AI. In short, people who speak our language.
Our Next Goals
This puts us in a great spot to fulfil our three main objectives for 2023: Firstly, we want to find more clients like Sagra who we can perfectly integrate into our pipeline and for whom our platform is an ideal fit — and with whom we can work in the long-run, on a recurring basis. We will focus the first six months on this effort, mainly looking at businesses in Europe and some in the US.
Secondly, for the first six months of the year, we will be exploring ways to expand our tech's reach and make it more accessible to a broader customer base. We come across many potential clients that have the same end problem as Sagra, but don’t have packaging labels at hand or are working with products that are more complex to model as 3D assets.
Thirdly, we want to expand our marketing and online presence to make it easier for a broader range of businesses to learn about our tech. We’ve been in touch with a lot of companies facing similar problems as our current clients, but who are working on slightly different forms of retail automation — self-checkouts, autonomous stores or warehouse management for instance. Our platform is pretty much plug-and-play for these use cases and we are looking to organically increase our presence in these areas.
Other Predictions for the Year Ahead
I would go amiss not to mention the current market conditions. On the one hand, smaller consumer budgets do heighten the need for perfect in-store execution. On the other hand, we’re seeing a clear hit in terms of fundraising, valuations, investments, and so on. Because of this, I believe that near-future advancements in retail will be likely pushed for by big and established players. Ideally, we want to integrate these companies’ resilience into our own DNA, while still maintaining the agility and tech focus that makes us stand out.
The bleeding edge of automation and digitisation is always evolving, so we’re looking at a lot of organic improvements that have already started to become a part of our business. We are able to render increasingly realistic 3D models, taking into account factors such as opacity, refractions, and deformations. Our algorithms are constantly improved upon and we will continue to push for highest levels of detection accuracy while also expanding the range of products our algorithms can handle at a time.
That being said, we’re hoping for a true leap in capability when it comes to digitising products and objects (that is to say to easily turn real-world objects into virtual 3D models). For years now we’ve told ourselves that a breakthrough is just around the corner, that a new iPhone or Lidar or even the latest Nerf algorithm will provide a seamless end-to-end process to create digital twins of anything you want — but so far we’ve been left waiting to see the adoption barrier drop low enough for a mass adoption of this technology.
A Message for the Readers
Looking back at what we’ve achieved, I would like readers to understand the following: Synthetic data works on a production level — it’s not just some cool experiment that got funding and will live and die in a lab.
I’m convinced that computer vision is the next big tech to be democratised and made available at the fingertips of anybody interested. We consider this a crucial part of our work at Neurolabs. We are computer vision experts first and foremost and, much like Wix or Squarespace have lowered the barrier for building a website in the last 15 years, we aim to enable citizen developers to easily create and adopt computer vision algorithms for their own benefit — be it in the retail space or otherwise.
As we close out 2022, I'd like to extend a heartfelt thank you to our incredible team and loyal customers for all their hard work and support this year.
Wishing you all a happy, healthy, and prosperous new year!
Paul Pop
CEO, Neurolabs
About Paul: Paul Pop is a Co-Founder and CEO of Neurolabs, overseeing the company’s finances, fundraising and product management. To learn more about Paul, visit his LinkedIn profile here or book a demo to chat with a member of the team today.
