SciForce: who we are

Published: January 9, 2018

SciForce is a Ukraine-based IT company specializing in development of software solutions based on science-driven information technologies.

We have wide-ranging expertise in many key AI technologies, including Data Mining, Digital Signal Processing, Natural Language Processing, Machine Learning, Image Processing, and Computer Vision. This focus allows us to offer state-of-the-art solutions in data science-related projects for commerce, banking and finance, healthcare, gaming, media and publishing industry, and education. We offer AI solutions to any organization or industry that deals with massive amounts of data. Our applications help reduce costs, improve customer satisfaction and productivity, and increase revenues.

Our team boasts over 40 versatile specialists in two offices in Kharkiv and Lviv — the regional capitals and the most rapidly developing IT centers of Eastern and Western Ukraine. Our specialists include managers, architects, developers, designers, and QA specialists, as well as data scientists, medical professionals, and linguists.

With such an organizational structure, we have the flexibility to help our customers both launch short-term small or medium projects and build long-term partnerships that would strengthen our partners’ in-house teams and change the vision of an offshore team from mere contractors to an important part of the organization with the corresponding motivation and loyalty.

Aside from the development of software per se, SciForce renders the full range of consulting services in deploying a new project or fine-tuning an ongoing project that needs re-evaluation or restructuring.

The philosophy of SciForce is not only to hire experienced professionals but to foster specialists through mentoring and knowledge sharing. The dedicated SciForce Academy project helps us find young talents, which not only facilitates internal hiring and transfers but also creates a productive atmosphere of mutual respect, trust, and patience.

In our corporate blog, we are going to share our knowledge and insights into frontier information technologies, provide expert opinions from our specialists, and offer you a glance at our daily life.

Stay with us!

RELATED BLOG ARTICLES

View all Articles

Computable Phenotyping in OMOP: Where Large Language Models Help – and Where They Do Not

A health database rarely captures the exact clinical state a researcher wants to study. It contains traces of that state: diagnoses, medication orders, laboratory results, procedures, changes in the level of care, and clinical notes. These records support care, communication, billing, and hospital operations. They were not created for future research questions. Computable phenotyping turns these traces into an algorithm for identifying patients, clinical events, or periods of interest. The proce

# Healthcare

# Data Science

# LLM

From OMOP Workflows to Living Evidence: SciForce at OHDSI Europe Symposium 2026

This April, Polina Talapova and Mariia Pahur represented SciForce at the 7th European OHDSI Symposium in Rotterdam – three vivid days of workshops, poster sessions, MindMeetsMachines mapping competition and an oral presentation aboard the SS Rotterdam, a retired ocean liner moored on the Maas river. The symposium's theme was Continuous Collaboration for Living Evidence Generation. The word "living" matters here. Traditional evidence-generation projects are often designed as discrete studies. A

Telehealth Platform Architecture: Building Secure, Scalable Virtual Care Systems

Building a telehealth platform at clinical scale means solving for hospital network restrictions, HIPAA compliance and auditability, and the data load of continuous remote monitoring – and the architecture decisions that determine whether it holds up are mostly made in the first few sprints. The engineering debt from early decisions starts showing up at scale: video sessions dropping when hospital firewalls, restrictive egress policies, or network address translation prevent a direct media path;

# Healthcare

# AI / ML

# Data Science

Improving Diagnostic Accuracy and Workflow

AI in Medical Imaging: From Diagnostic Accuracy to Clinically Usable Workflow

A radiologist on a standard hospital shift may read dozens to well over a hundred imaging studies, depending on subspecialty, setting, shift structure, and case complexity. Each one is a search for something that might be subtle, easy to miss, or buried in noise. At that volume, non-trivial discrepancy or error rate is a known risk in radiology practice, especially under high workload and time pressure. Radiologists are working through growing imaging volumes with a workforce that has never full