Digital Asset Management Product Development

  • Service MVP development for a startup
  • Customer Founders experienced in media business
  • Year of implementation 2018 – ongoing

Case

Loovatech team was initially called on to develop tailor-made IT solutions and automations for a well-known photo agency.

Then, the photo agency owner decided to turn these solutions into a separate B2B-oriented IT product. This is how this product, a Digital Asset Management system, was born.

Startup founders felt the demand for such a solution in the local market. Thousands of companies regularly produce media content. These are the media, the tourism industry, arts or sports clubs, various communities and associations, and so on.

There was a need to translate the idea into a DAM-class product ready for scaling. Our team was brought in to develop the first version of the product.

The product had to meet the demand of the target audience for a media library that a major content-producing company could use.

Customer

Our customer's product is a Digital Asset Management (DAM) class system designed to centralize, organize, search and share all kinds of digital multimedia files, including images, video, audio, and other materials.

The startup was founded in 2019 with a mission to bring order to corporate photos and videos, organizing them into quick-access collections, as well as storing media content in a historical perspective.

Business goals

  • #1 Form the concept of the product and roadmap of its development.
  • #2 Launch MVP in a short time, rationally using the available budget.
  • #3 Get first sales.
  • #4 Attract investments.

#1

Our approach

Work on the project started in late 2018. First, Loovatech analysts delved deep into the subject of the product and assessed the market for DAM systems and technologies.

At the beginning of the project, a product concept was created and roadmap for one year of development was drafted. This system was to be developed as a B2B product.

Then, designers and programmers got down to work. The architecture was developed in parallel with interface design. First version of the service appeared within 3 months of the project launch, and in May 2019 system was deployed on the customer's infrastructure.

After the first release, a plan for functional improvements until the fall was agreed with startup founders. The update came out by September 2019, and then an MVP was assembled. Loovatech team is still responsible for technological development of this startup.

All the basic functionality required from such systems was implemented in the MVP, including:

  • a single place for data storage on cloud servers;
  • convenient live search by metadata, tags and collections;
  • permissions & access management;
  • convenient media upload and export.

One of the service's first customers was a leading hockey team. Face recognition functionality based on machine learning and artificial intelligence was implemented for this customer. Later on, it was included in the basic version of the product and became available to all customers.

Over the past 2 years, a SaaS version of the service in the cloud appeared, in addition to the classic on-premise version.

The initial project team comprised 10 people. By mid-2020, it expanded to 20. Most are members of the production team, including developers, testers, analysts, and UX designers.

#2

Result

The service was successfully launched. 9 months after the start of development, first paying customers were attracted. Loovatech involvement was not limited to the technical aspects and developing the service's MVP. The management team of Loovatech also helped founders get their first sales.

As regular customers appeared, Loovatech team began to provide customer support and assisted deployments of on-premise products. Loovatech team took over the process of compiling training materials for users and maintaining support portal. Our team also provides second line of technical support.

In 2019, our customer was selected for 500 Startups acceleration program.

CTO of Loovatech went through acceleration together with service founders, and also pitched the solution to venture investors. As a result of this acceleration, startup received pre-seed investments and attracted new customers.

As of 2021, over 20 clients regularly use the system, including sports teams, media, news companies and PR departments of large business.

#3

Face and object recognition based on AI / ML

The face recognition function based on machine learning (ML) and artificial intelligence (AI) was implemented for one of the first clients — a local hockey club. Later, this functionality was included in the basic version of Picvario and became available to all customers.

Face recognition is one of the system’s main functions. Based on AI, identification numbers are assigned to each face found when uploading media materials to Picvario. Neural networks additionally control the accuracy of the found matches.

The system generates a reference face graph for each person. The reference graph contains all the faces recognized by the service. For each face found in the photo, the system recognizes age and emotions using ML.

Picvario performs automatic tagging and object recognition in real time. When uploading an illustration, the system independently suggests correct tags, understanding the composition, subject and other metrics of photo recognition. According to the statistics of the service, the success rate of matches is above 90%.

#4

Startup history

Startup founders initially had an understanding of the demand for a DAM system in the local market. The first founder had many years of experience in photography, running a major photo agency whose services are used by many media companies. The second one was a top manager for a large IT company handling top-level issues in the Enterprise solutions segment. The third partner is a business angel and one of notable venture investors in EMEA.

Startup founders were all experienced businessmen and managers who have achieved many successes and reached top positions. Here's the hypothesis and insight they had: in many cases, neither news agencies nor the media and other content producers know how to do systemize work with media materials or exchange visual data properly, and existing tools for these tasks are either prohibitevly costly or outdated.

This insight was the starting point for the creation and development of the startup. Founders saw the product as a simple media library that would help company employees quickly find the right media file among thousands of others.

It was decided to hire developers from Loovatech to implement the idea. Step by step, we have been getting deeper into the product. Courtesy of the Loovatech team, the service progressed from MVP to release and first sales.

Now, the startup has contracts with more than 20 enterprose customers. Investment after 500 Starups acceleration had then arrived, along with new contract opportunities. Loovatech still helps develop the product and provides full technical support to the startup's clients.

The result is a successful symbiosis. The startup was built upon the right ideas and hypothesis from the founders, and brought to life by custom development experts from the Loovatech team.

A single place for data storage on cloud servers. Convenient live search by metadata, tags and collections. Permissions & access management. Convenient media upload and export.

The service was successfully launched. 9 months after the start of development, first paying customers were attracted. Loovatech involvement was not limited to the technical aspects and developing the service's MVP.

As regular customers appeared, Loovatech team began to provide customer support and assisted deployments of on-premise products. Loovatech team took over the process of compiling training materials for users and maintaining support portal.

As of 2021, over 20 clients regularly use the system, including sports teams, media, news companies and PR departments of large business.

Business result

  • MVP ready for sales and testing of hypotheses was created in three months.
  • First Enterprise sale was made six months after the development started.
  • $2,000,000 — an estimate of the company's value a year after the launch based on the results of obtaining investments.
  • 20 Enterprise deployments of the product within the first two years. Received government grant funding for the development of AI functions.
  • 40 deployments three years after launch, entering markets of North America and Western Europe.

Technology stack

  • Backend Python, Django, Celery
  • Middleware nginx
  • Interface Nuxt.js, Vue.js
  • Storage MySQL, Redis
  • Hosting Cloud hosting, Deployment at the customer's facilities
  • DevOps TeamCity, Docker, Kubernetes, Ansible
  • Tools JIRA, Confluence, Bitbucket, Sketch, Zeplin
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