Custom AI solutions development
Ready-made AI services address common tasks. Custom development is needed when the task is specific to your industry, the data is unique, or integration into corporate infrastructure is required. We design and build AI solutions from scratch—from architecture selection to production deployment.
How we approach the task
The first thing we do is ensure that custom development is truly necessary. Most problems can be solved by further training existing models or orchestrating existing APIs. In-house development from scratch is justified in three cases: unique data not found in public datasets; privacy requirements that preclude the use of cloud APIs; and specific architectural requirements for latency or throughput.
What is included in the full development cycle?
Discovery (1-2 weeks): Data audit (volume, quality, markup), formalization of business metrics into ML metrics, baseline analysis (what a simple rule-based solution or existing SaaS provides), assessment of the feasibility of target indicators.
Proof of Concept (2–4 weeks): A quick experiment on real data. If the basic PoC doesn't demonstrate sufficient quality, we work out why and what needs to be changed before full development.
Model development: Architecture selection: transformers (BERT/GPT family, T5), CNN/ResNet for CV, recurrent for time series, GNN for graph data. Fine-tuning vs. training from scratch. Feature engineering for tabular data (XGBoost, CatBoost—not everything can be solved with neural networks).
MLOps pipeline: Data versioning (DVC), experiment tracking (MLflow/W&B), CI/CD for ML (GitHub Actions + model registry), monitoring in production (drift detection, performance degradation alerts).
Production deployment: FastAPI / Triton Inference Server, Docker + Kubernetes, auto-scaling, A/B testing infrastructure.
Tech stack
| Component | Tools |
|---|---|
| Frameworks | PyTorch, TensorFlow, JAX |
| Experiments | MLflow, Weights & Biases, Optuna |
| Data | Apache Spark, Pandas, Polars, DVC |
| Deploy | FastAPI, Triton, TorchServe, ONNX |
| Orchestration | Airflow, Prefect, Dagster |
| Monitoring | Evidently AI, Grafana, Prometheus |
Standard terms
| Difficulty of the task | Discovery+PoC | Full development | Production |
|---|---|---|---|
| Classification/Regression | 1–2 weeks | 4–8 weeks | 2–3 weeks |
| NLP (special domain) | 2–3 weeks | 8–16 weeks | 3–4 weeks |
| Computer Vision | 2–4 weeks | 10–20 weeks | 3–5 weeks |
| Multimodal | 3–4 weeks | 16–24 weeks | 4–6 weeks |
What we don't do
We don't promise accuracy without data analysis. We don't start development without proof-of-concept. We don't hand over black-box models without documentation, testing, and the ability to retrain. Each project concludes with the handover of source code, documentation, and training for the client's team.