Custom AI Solution Development
A retailer spent three months tuning a ready-made demand forecasting AI service — accuracy never exceeded 60%, and p99 latency spiked above 500 ms. An assortment of 10,000 SKUs with seasonal peaks didn't fit standard models. We had to build a custom solution from scratch, accounting for product group specifics and promotions. We designed a pipeline based on gradient boosting (CatBoost) with hand-crafted features, achieving an F1 boost to 0.85. Our experience: 10+ years in industrial ML, over 50 projects in retail, finance, logistics, and healthcare. We guarantee transparency: every development stage is coordinated with your team, and the final model comes with full documentation and code. The payback period for custom AI investments averages 6–12 months. We apply RAG, fine-tuning, and LoRA depending on the task.
Why Custom Development?
Ready-made APIs (OpenAI, Google Vision) cover 80% of typical scenarios. The remaining tasks require a custom AI architecture:
- Unique data — your database contains proprietary information or rare formats absent in public datasets.
- Privacy — you cannot send data to cloud models (GDPR, corporate policy).
- Latency requirements — online inference with p99 < 100 ms requires fine optimization with Triton Inference Server.
- Specific architectures — e.g., graph neural networks (GNN) for supply chain analysis or multimodal fusion for video analytics.
How Fine-Tuning with LoRA Reduces Costs
Fine-tuning with LoRA reduces GPU costs by 10x compared to full model training. We fine-tune LLaMA 3 or Mistral on your data: achieving 90% quality without building from scratch. During the discovery phase, we build a baseline — if rule-based or fine-tuning delivers metrics close to target, we drop custom architecture. Budget savings with this approach reach 60%.
Why PoC Is Mandatory
Without a rapid prototype, risks are enormous. One churn prediction project seemed simple — a ready-made solution baseline gave AUC=0.72. The customer wanted >0.9. After a two-week PoC, we found systematic data gaps, and without filling them, AUC couldn't exceed 0.8. The client saved budget by switching to improving data collection. We never start full development without a PoC confirming target metrics are achievable. Thus, machine learning outsourcing with a PoC minimizes financial risks.
What's Included in Development
Each project includes:
- Documentation: model card, experiment report, operations manual.
- Access to repository, datasets, registered models in MLflow.
- Training for your team (2–3 sessions of 2 hours each).
- Post-production support for 3 months: drift monitoring, bug fixes, dependency updates.
- ML consulting and data audit at the start.
Development Process: Step-by-Step
- Discovery (1–2 weeks). Data audit — volume, quality, labeling availability. Realistic assessment of target metrics: for example, if baseline F1 is 0.6 and target is 0.95, we determine if it's theoretically achievable. We use uncertainty estimation. At this stage, we also determine the optimal AI architecture.
- Proof of Concept (2–4 weeks). Model sketch on real data. Quick experiment with minimal feature set. If PoC shows quality below threshold, we don't proceed to full development — we search for alternatives or change the formulation.
- Model Development. Architecture selection: transformers (BERT, GPT, T5) for NLP, CNN/ResNet for CV, recurrent for time series, GNN for graphs. For tabular data, XGBoost and CatBoost often yield better results at lower cost. Fine-tuning pretrained models with LoRA to save GPU hours. We apply production ML practices for reproducibility.
- MLOps Pipeline. Data versioning (DVC), experiment tracking (MLflow/W&B), CI/CD for ML (GitHub Actions + model registry). In production — data and concept drift monitoring (Evidently AI, WhyLabs). Automatic retraining algorithms on metric degradation.
- Production Deployment. FastAPI for serving, Triton Inference Server for high-load scenarios, ONNX Runtime for optimization. Docker + Kubernetes — auto-scaling by load. A/B testing of new model versions.
Custom vs Ready-Made API Comparison
Custom solutions deliver 3x higher accuracy on specific data, and p99 latency drops 5x compared to API solutions. For example, in defect detection on a production line, F1 rose from 0.45 (cloud API) to 0.92 after fine-tuning ResNet. Payback period averages 6–12 months. For a hypermarket chain, we built a custom CatBoost model for demand forecasting. Result: 30% higher prediction accuracy, latency reduced from 500 ms to 80 ms, 25% reduction in write-offs. Full cycle: 14 weeks.
Technology Stack
| Component | Tools |
|---|---|
| Frameworks | PyTorch, TensorFlow, JAX |
| Experiments | MLflow, Weights & Biases, Optuna |
| Data | Apache Spark, Pandas, Polars, DVC |
| Deployment | FastAPI, Triton, TorchServe, ONNX |
| Orchestration | Airflow, Prefect, Dagster |
| Monitoring | Evidently AI, Grafana, Prometheus |
Typical Timelines
| Task Complexity | Discovery+PoC | Full Development | Production |
|---|---|---|---|
| Classification/Regression | 1–2 weeks | 4–8 weeks | 2–3 weeks |
| NLP (specialized 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 upfront without data analysis.
- We don't start development without a PoC.
- We don't deliver a black-box model without documentation and tests.
- Every project ends with source code, a report, and training for your team.
Get a consultation — we will assess your project's feasibility and propose the optimal solution. Order a preliminary data analysis — it's free and non-binding.
Learn more about MLOps methodology on Wikipedia.