AI talent and the data work behind your models
Two lines of work
Recruiting AI specialists
We recruit AI specialists across every level and profile — from engineering and product roles to leadership and domain experts. We run the whole process, from search through assessment to hire.
Model training and evaluation projects
We prepare training data, evaluate models and fine-tune them. We run the full cycle — from staffing the team to managing the project and controlling for quality.
AI specialists and leaders for your industry
FinTech and banking
ML teams and AI leaders for banks and fintech products.
Computer Vision and AgriTech
CV engineers and labeling for agriculture and industry.
MedTech and Life Sciences
AI specialists and domain experts (physicians) for training and reviewing models.
GenAI and LLM products
Developers and researchers for generative products and agents.
Data and ML infrastructure
Data Scientists, data engineers and MLOps for platforms and pipelines.
Enterprise AI
Corporate AI assistants, deployment and AI leadership.
Roles we place
Leadership+
Engineering & Development+
Architecture & Data+
Research & Modeling+
Data Operations & Quality+
Product & Domain Experts+
What training and evaluation projects include
Data labeling
We label data of any type and prepare it for training: classification, categorization, entity extraction (NER), OCR verification, image annotation, object segmentation and training-dataset preparation.
Human feedback (RLHF)
We prepare data for fine-tuning: we write reference answers, rank alternatives, correct model errors, assemble training examples and preference datasets, and evaluate reasoning models.
AI model quality evaluation
We assess how accurate, coherent and useful a model’s answers are: we compare outputs across models and evaluate factual accuracy and completeness, reasoning, instruction-following, language naturalness and user experience.
Expert review of AI responses
We bring in domain experts who check factual accuracy, catch professional errors, recommend improvements and help build specialized datasets.
Multilingual AI testing
We test the model in the region’s languages: we check grammar and natural phrasing, assess localization and adapt content to cultural context.
AI safety / vulnerability testing
We probe the model’s weak points and test its robustness: we surface hallucinations, toxicity, discriminatory responses, data leaks, policy violations and unsafe recommendations.
Launching and running AI projects
We scope the project
We study the task, the data and the infrastructure, and set scope, timeline and quality in the contract.
We build the team
We recruit and train specialists for your processes, and build the team around the specific task.
We run the work
We assign tasks, coordinate the team and control quality at every stage of the work.
We close out
We deliver on time and within the agreed scope, and report against the agreed KPIs and SLAs.
Selected projects
Building an ML team for a bank in Georgia
Placed the key roles to launch the bank’s ML function:
Head of ML • ML engineers • Data Scientists • Data engineers • Project manager
Building an AI team for a bank in Tajikistan
Staffed the full team:
AI/ML Lead • ML engineers • Data engineers • Backend developers • AI Product Manager • Project manager
Leaders and AI engineers for enterprise AI projects
Filled leadership and engineering roles:
Head of AI • AI Product Manager • LLM developer • ML engineer • MLOps engineer
An annotation team for a Computer Vision project in agritech
Assembled an annotation team and set up:
image annotation (bounding boxes and segmentation) • object classification on farmland • data and annotation quality control • training-dataset preparation
Multilingual data labeling for AI model training
Organized the data-preparation workflow:
text annotation • data quality control • information classification • AI model output evaluation
Human feedback and AI model quality evaluation
Built a team of AI Evaluators and Human Feedback Specialists:
model output evaluation • answer comparison • improvement recommendations • training examples to enrich the model
Our track record in numbers
Azerbaijan, Armenia, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Russia, Tajikistan, Uzbekistan, UAE, Qatar, Kuwait, Oman, Saudi Arabia
We match people to the task, vet them on real work and scale the team quickly to your volume, timeline and quality requirements.
Turnkey project teams, data labeling, and AI model evaluation and training.
People and Products