Case Studies

  • Joberty is a workplace community for IT developers. It is a combination of an employer review and job posting site. It focuses on the IT community and offers developers an opportunity to review companies, search for IT jobs, and join community discussions.

    The company’s HQ are based in Serbia, Europe, but the company operates in the Central and Eastern European region (e.g. Serbia, Croatia, Slovenia, Hungary, Romania, Bulgaria).

    Joberty represents one of the significant startup successes in the regions growing rapidly in reach and member sign ups since the site was launched in public in 2019. Comparable companies would be Glassdoor, Indeed, LinkedIn, etc.

    • Type: Early startup

    • Funding: Pre-seed round in 2021 of $350,000; currently raising Series A (Aug 2023)

    • # of Employees: 10-50

    • Ownership: Private

    • Active users: 20,000 / month

  • Joberty experienced revenue decline as a result of Covid. During and post Covid, less hiring meant less job postings, hence less revenue was generated.

    This also exposed one of the weaknesses of the company (which has also been a cause of investor concern) - one revenue stream.

    The company recognised the issue and the need to solve this problem, but as they were unable to solve internally, they reached out to us, [DataBlooz].

  • As a part of our solution offering to help Joberty increase the value and breadth of their services, and thus help diversify their revenue stream, we identified the following 3 key initiatives that we would help with, and their timelines:

    • Develop and implement into the existing site a matching algorithm between companies and applicants (est. time effort 2 months, immediate kickoff)

    • Development and implementation of a recommendation system for jobs (est. time effort 2 months, late 2023 kickoff)

    • Development and implementation of a recommendation system for content (est. time effort and timeline TBD)

    This was all included in the Data Strategy Package.

  • LeadDelta is dubbed as a LinkedIn Relationship Manager, aiding in sales, hiring, and fundraising by optimizing network utilization. It offers a suite of features like a Connections Manager, Smart Inbox, Sidebar, Workspaces, and Data Integration, allowing for organized contact management, effective communication, and CRM enrichment, among other functionalities. With over 10,000 satisfied users, it has a Chrome Store rating of 4.9/5 and offers a 10-day free trial.

    • Type: Small company

    • Funding: Bootstrapped/Seed - Around 800k

    • # of Employees: 10-25

    • Ownership: Private

    • Annual Revenue (not publicly shareable): 250k

  • Finding a competitive advantage and new features due to the decline in their growth

    What would happen if they didn’t solve these problems?

    • Run out of money and start firing their teams -> Their burnout rate was higher then the MRR in June 2023 with the runaway of only 8 months

  • Our Approach

    • Worked with the owner to recognize their needs

    • In March/April 2023 Luka created an AI improvement plan for LeadDelta that was approved by the board of LeadDelta

    • Designed labeling and data collection process to increase data quality

    • This process was put in place in May 2023 / No work needed from Datablooz’s end

    • Implemented a custom solution that became their core product

    • In August 2023 - their solution was ready from our end

    • Assisting their team in integrating their solution into their platform - October 2023

    Solution

    • An end-to-end Machine Learning solution that helps LeadDelta’s users to search for potential contacts, generate messages, automatically tag their connections

    • API that uses LLMs (GPT-4) to help LeadDelta’s customers in different way:

    • Generate outreach messages

    • Check grammar

    • Search LinkedIn database in a plain english language

    • “Find me all HRs working in SaaS from EU”

    • Message condenser

    • User’s auto-tagging

    • Make message punchier

    • Change a tone of the message

  • Created a SaaS product that generates mobile applications for an e-commerce store. OmniShop is aimed at aiding small to medium-sized businesses in competing against e-commerce giants by furnishing them with a mobile app. They focus on fostering a close relationship between brands and their customers through the mobile app platform they provide.

    • Type: Small company

    • Funding: Pre-Seed/Angel

    • # of Employees: 1-10

    • Ownership: Private

    • Annual Revenue (not publicly shareable): Pre-revenue UPDATE: They sold 3 times their service at the time of writing (October) - they are post-revenue

  • Business Problem(s)

    • MVP product

    • Scaling

    • New offering

    What would happen if they didn’t solve these problems?

    • Difficult to raise capital due to scalability of the product

  • Enabled them to recognize how to organize, store, and utilize data for their products. Enabled them to design an automation process for mobile app generation, which provided scalability for their product.

    Results & Key Learnings

    • Closed a first set of clients

    • Built the functional product

  • They created an AI platform for big brands that are about to put some POS or Display inside of a store; their system can estimate the return over the design. So they can get 10 materials to register and judge them.

    • Type: Small company

    • Funding: Pre-Seed/Angel - Around 400k

    • # of Employees: 1-10

    • Ownership: Private

    • Annual Revenue (not publicly shareable): Pre-revenue - In the process of closing their first big client in Nederlands UPDATE: They are now post-revenue. Closed the first big client

  • Business Problem(s)

    • MVP product - They didn’t know how to build the initial algorithm for their business problem.

    What would happen if they didn’t solve these problems?

    • They wouldn’t have a product at all (our algorithm is their product) - This client has gained potentially the most out of our collaboration. Any valuation now is due to that algorithm.

  • An end-to-end Machine Learning solution that takes data from their clients, processes it and predicts what is the potential outcome of their in-store marketing efforts (POS, Displays).

    Details:

    • Their client’s uploads a set of images for their proposed POS/Display designs, Shopnosis uses people to initially label the image and provide as much info about it as possible.

    • Our algorithm takes the image and labeled information and starts the prediction process.

    • GCP (Google Cloud Platform) uses the image for similarity and finds similar products that we already have in the database -> this part return 5 most similar products

    • Statistical models takes the labeled info and run the set of rules to filter out the noise and the best candidate

    • The combination of both stages are used to determined the final prediction

    • Increased the accuracy of their system to 70%

    UPDATE OCTOBER 2023: In the meantime they hired a full time data analyst to help them with data and stats full time. They proposed to move to the improvement stage of the algorithm, we performed the analysis process and additional tests and proposed to upgrade the current process with ChatGPT based classifier - no implementation needed from Datablooz. The whole infrastructure will be moved to their GCP account.

    Results & Key Learnings

    • They closed the first client of 400k ARR

    • Got an verbal offer to sell the startup because of the algorithm for $15 million