Service: IT Staff Augmentation
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Cybersecurity Experts for Healthcare
The Client A private network of medical facilities (hospitals and diagnostic centers) operating across several EU countries. The organization processed sensitive patient medical data and integrated systems such as HIS, RIS, e-registration platforms, and cloud solutions used for storing diagnostic results. The challenge With the expansion of digital services (telemedicine, online test results, integrations with…
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Scaling DATA team – Human Pharma
The organization launched a new data warehouse project but lacked a senior professional who could combine analytical and data modeling expertise with the ability to run workshops with business stakeholders.
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Building DATA team for a Manufacturing sector
The platform had been designed and implemented by an external project team. The key challenge was ensuring a complete, structured, and transparent transfer of the solution to the internal engineering team.
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Scaling AI & ML team in the Industry sector
Operational data was being collected, but analysis was mainly retrospective. Maintenance teams reacted to failures instead of preventing them, and production planning relied on static assumptions.
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Scaling DATA team in the Financial sector
Cloud costs were growing faster than the revenue generated by digital initiatives. There was no full visibility of costs at the product or team level.
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Supporting Cybersecurity teams in E-commerce
The rapid pace of product development meant that application security was handled reactively. Security testing was performed irregularly, and responsibility for secure coding was distributed across development teams.
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Cloud – scaling AWS teams for E-commerce
As sales grew, issues related to the scalability and stability of the cloud environment began to emerge. The company experienced periodic drops in application performance and increasing infrastructure costs.
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Expanding an AI & ML Team for E-commerce
The company aimed to increase conversion rates and average basket value through personalized offers and improved demand forecasting. Transactional and behavioral data were collected but not used in an advanced way.
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Scaling Cybersecurity Capabilities in Logistics
Following a security audit and increasing customer requirements regarding data protection, the company identified a competency gap in operational cybersecurity.
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AI & ML Staff Augmentation for Logistics
The organization planned to implement AI/ML solutions to optimize routes and forecast shipment volumes but lacked data science and MLOps capabilities. The IT department was focused mainly on maintaining operational systems, while recruiting AI specialists was difficult due to a highly competitive market and the company’s relatively low recognition as a technology employer.
