Data, AI & Automation
Turn data into decisions; add AI where it creates real value.
What’s included
Services
Data pipelines (batch/streaming), warehousing & lakehouse
BI dashboards, experimentation (A/B), attribution modeling
AI assistants (conversational, RAG), retrieval pipelines, embeddings
Recommenders, NLP/CV use cases, agents & automation
MLOps (model registry, CI/CD for models, monitoring, drift detection)
We don’t just develop software — we own and operate our own infrastructure, giving clients unmatched control, security, and performance.
Zenoviy Burychko
CEO, Kernex
About the process
Delivery
Use-case & ROI selection
Data assessment & architecture
POC → Pilot → MLOps
Governance & Monitoring
Deliverables & Success metrics
Deliverables
- Data/AI Architecture: Design of the data pipelines and AI models' structure.
- Feature Store Design: Centralized repository for storing and managing features for machine learning.
- Notebooks: Interactive documents for data exploration, analysis, and model training.
- Inference Services: Deployed models for making real-time predictions.
- Dashboards: Visual tools for monitoring model performance and business metrics.
Success metrics
- Business KPI Uplift per Use-Case: Improvement in key business metrics due to the AI solution.
- Model Latency/Accuracy: Time taken for predictions and the accuracy of the model.
- Cost per Inference: The cost associated with each model inference or prediction.
