Procurement planning based on Excel spreadsheets and intuition — classic logistics. The result: excess stock in warehouses alongside shortages of fast-moving items, emergency orders with premium tariffs. We develop comprehensive AI systems for logistics and supply chains that shift logistics from reactive to predictive mode. The system knows about a shortage 3-4 weeks before it happens and automatically adjusts procurement plans. One of our clients — a retail chain with 2000 SKUs — after implementation reduced operational budget by 18% and improved OTIF from 84% to 94%. Savings amounted to 45 million rubles per year on a project budget of 8 million.
How AI forecasts demand in the supply chain?
Demand forecasting is a multi-level challenge: from SKU to supplier and transport leg. Errors at each level multiply, causing the bullwhip effect (bullwhip effect). We use hierarchical time series forecasting with bottom-up and top-down reconciliation. Base models — AutoARIMA, AutoETS, CrostonOptimized — are combined via MinTrace OLS:
from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA, AutoETS, CrostonOptimized
from hierarchicalforecast.methods import MinTrace
from hierarchicalforecast.core import HierarchicalReconciliation
models = [AutoARIMA(), AutoETS(), CrostonOptimized()]
sf = StatsForecast(models=models, freq='W', n_jobs=-1)
forecasts_df = sf.forecast(df=panel_data, h=12)
hrec = HierarchicalReconciliation(reconcilers=[MinTrace(method='ols')])
reconciled = hrec.reconcile(
Y_hat_df=forecasts_df,
Y_df=historical_data,
S=summing_matrix,
tags=hierarchy_tags
)
More about forecast reconciliation
The MinTrace method minimizes error variance across the hierarchy. Choosing the OLS solver gives a closed-form solution without iterations.
The top-level forecast (category) is aggregated from SKU forecasts. MinTrace ensures consistency across all levels. Result: MAPE accuracy 10-18% vs. 25-35% without reconciliation — that is 1.5-2 times better.
Why is inventory optimization important?
The newsvendor problem for stochastic demand: determine the optimal order quantity considering overstock and shortage costs. ML extends the classic approach: Conditional Value-at-Risk in the objective protects against worst-case scenarios, multi-echelon inventory optimization synchronizes stock across the network, and Reinforcement Learning adapts policies to seasonality and promotions. Applying these methods reduces inventory days by 30% and lowers holding costs — budget savings reach 20% on test purchases.
Transport flow optimization — VRPTW (NP-hard). We use Google OR-Tools for problems up to 500 stops and Large Neighborhood Search with ML heuristics for large networks. Deep Learning (Attention Model) provides an end-to-end solution but requires fine-tuning to the customer's specifics. Result: delivery cost reduction of 8-15%.
| KPI |
Baseline |
After AI |
| OTIF |
82-88% |
92-96% |
| Inventory days |
45-60 |
28-38 |
| Delivery cost |
100% |
85-92% |
| Perfect Order Rate |
75-82% |
88-94% |
| Forecast accuracy (MAPE) |
25-35% |
10-18% |
Risk management with GNN and Knowledge Graph
Early disruption warning. NLP parsing of news, AIS vessel delay data, Baltic Dry Index — the system monitors external signals and builds a supplier Knowledge Graph. GNN predicts the likelihood of cascading failure when a key node fails. Diversification Score via Herfindahl-Hirschman Index assesses risk concentration and suggests diversification scenarios with ROI.
Digital twin for simulation modeling
The simulation model (AnyLogic, SimPy) runs scenarios: 14-day delay from Shanghai → impact on OTIF. Monte Carlo simulation with real lead time distributions answers "what if" questions. ROI of this approach is confirmed by cases: one project delivered 40 million rubles savings in the first year.
How to estimate ROI?
We run a pilot on historical data: digital twin simulation shows forecasted KPIs — OTIF, MAPE, inventory levels. Comparison with baseline demonstrates operational budget reduction of 15-20% with service level growth. The report includes payback period and scaling recommendations.
What is included in the work?
- Architectural documentation: data model description, integration schemes, model specification.
- Model development and training: forecasting, optimization, risk analytics.
- Integration with ERP/TMS/WMS: REST, EDI, BAPI.
- Client team training: workshops on operation.
- 3 months post-launch support: monitoring, fine-tuning, fixes.
10+ years on the market, 50+ projects in logistics and SCM. Our experience guarantees results. We take turnkey projects: from data audit to model deployment. Contact us to discuss your project and get a pilot in 3-4 months.
Efficiency metrics
Our hierarchical forecasting approach reduces MAPE by 40% relative to standard ARIMA. Compare with alternatives:
| Approach |
MAPE |
Implementation cost |
| Classic ARIMA |
25-35% |
Low |
| Hierarchical MinTrace |
10-18% |
Medium |
| Deep Learning (NeuralForecast) |
8-14% |
High |
Development timeline for a comprehensive platform is 6-10 months, including forecasting, inventory optimization, and routing with ERP integration.
AI does not replace the logistician — it gives them superpowers. Request a consultation to discuss your tasks.
Industry AI Solutions: Healthcare, Finance, Retail, Manufacturing
We encounter the same pain points: a general text model doesn’t distinguish medical nomenclature, and a standard object detector confuses “weld seam scratch” with “casing scratch.” Each time these are different defects with different consequences. To avoid this, we build industry-specific solutions on top of general methods, but with deep domain knowledge — from regulatory requirements to data specifics. Over 5 years, we have completed 80+ projects in fintech, healthcare, retail, and manufacturing, and none were without adaptation to a specific business case.
Healthcare: Regulatory Maze and Data Governance
Medical AI differs not in technical algorithms but in a compliance-first approach. Depending on the country of application, the model may be a Class II or III medical device requiring clinical trials (FDA, CE MDR, GOST R). We ensure compliance with these standards at the architecture stage — fixing them post-factum is 10× more expensive.
Medical imaging. Detection on X‑rays, CT, MRI is a mature area. Models on ResNet, EfficientNet, SegFormer achieve AUC 0.94–0.97 on standard tasks (pneumonia on CXR, polyps on colonoscopy). Key issue is generalization: a model trained on data from one scanner manufacturer degrades on another due to differences in preprocessing and artifacts. Solution: domain adaptation via MONAI (Medical Open Network for AI) from NVIDIA, which includes DICOM loading, 3D augmentation, and confidence calibration. TotalSegmentator — for automatic segmentation of 117 structures on CT, production‑ready, Apache 2.0 license.
Clinical NLP. Extracting structured information from clinical records: diagnoses (ICD‑10/11), prescriptions, dates, indicators. medspaCy, scispaCy, MedCAT — specialized NLP libraries with ontologies (SNOMED‑CT, UMLS). Fine‑tuning BioBERT or ClinicalBERT on our data yields F1 0.85–0.92 on NER tasks versus F1 0.65–0.72 for general BERT. We verified this on a project with a regional oncology center — cancer stage extraction accuracy increased by 23%.
Clinical decision support. LLM assistants for clinical decision support are a regulatory gray area. We use an RAG system on top of clinical guidelines (UpToDate, local protocols) with explicit citation for each statement. The model does not diagnose but helps find relevant protocols. Stack: LlamaIndex + pgvector + pubmedbert-base-embeddings + Llama Guard for safety. Data in DICOM/HL7 FHIR, on‑premise deployment mandatory.
Deliverables in a Healthcare Project
- Data audit and regulatory mapping (FDA/CE/GOST)
- Architecture selection based on medical device type
- Model development and validation (AUC, sensitivity, specificity)
- Integration with PACS/EHR (HL7 FHIR)
- Preparation of documentation for CE marking (if required)
- Staff training on model usage
Finance: How to Ensure Interpretability of a Scoring Model under Basel IV?
The financial sector is one of the most mature in applying ML, but regulation is maximal. Every model affecting credit decisions falls under Basel IV, EU AI Act, GDPR Article 22. We deliver AI solutions for fintech that satisfy these requirements — in a project for a top‑10 bank we deployed a scoring model where each record required SHAP explanations.
Credit scoring. Gradient boosting (LightGBM, XGBoost) dominates. Neural networks yield +0.5–2% AUC but lose interpretability. Standard: LightGBM + SHAP to explain each decision. Fairness checking is mandatory: Fairlearn or aif360 for auditing disparate impact on protected attributes (age, gender). The default class is 1–5% — with an imbalance of 1:30, a model with 97% accuracy may have recall 0.2. Solution: focal loss, class_weight='balanced', SMOTE + careful validation. In one fintech scoring project, the model reduced credit losses by $2.1 million annually.
Algorithmic trading and risk management. LSTM and Transformer for price forecasting are popular but unstable in production due to non‑stationarity of financial series. A more robust approach: ML for signal generation (classification: up/down over horizon N) with traditional portfolio optimization on top. Backtesting via Zipline‑Reloaded, vectorbt, QuantLib. Proper backtesting is critical — look‑ahead bias kills results. We guarantee a clean experiment: all data at signal time is available in real time.
AML (Anti‑Money Laundering). Graph Neural Networks for analyzing transaction networks is an actively developing area. PyG, DGL for GNN. Task: detect suspicious patterns in transaction graphs (layering, structuring). Recall is more critical than precision — better 10 false alarms than miss one money laundering. In a project for a large payment service, we increased recall by 18% without increasing false positive rate.
Deliverables in a Financial Project
- Data audit and regulatory requirements (Basel, EU AI Act)
- Model selection and explainability (SHAP, LIME)
- Fairness check and bias mitigation
- Integration with core banking / trading systems
- Documentation and compliance reporting
- Model drift monitoring and retraining
Retail and e‑commerce: Recommendation Systems and Demand Forecasting
Recommendation systems. Current architectural standard: two‑tower model for retrieval + ranking with cross‑features. TensorFlow Recommenders or Merlin from NVIDIA for GPU‑accelerated feature processing. For small catalogs (<100k items), LightFM is sufficient. A common mistake is training on implicit feedback without accounting for position bias. Solution: IPW (Inverse Propensity Weighting) or randomized logging on a portion of traffic. Development time for a basic recommendation system is 4–8 weeks, including A/B test.
Demand forecasting and inventory optimization. Hierarchical forecasting: SKU → category → store → region. HierarchicalForecast from Nixtla automatically reconciles forecasts across levels. TFT or N‑HiTS for base forecast, gradient boosting for adjustment on exogenous factors (promotions, weather, events). One retail project led to a 15% reduction in stock‑outs due to precise promotion calibration.
Visual search and size compatibility. CLIP embeddings for image search — deploy in 2–3 weeks: clip‑ViT‑B‑32 or clip‑ViT‑L‑14, Faiss or Qdrant index, REST API. For size recommendation — specific models on return data and reviews with fit indication.
Deliverables in a Retail Project
- Analysis of transactions, products, customers data
- Architecture selection (collaborative / content‑based / hybrid)
- Development and evaluation (NDCG, recall@k, MRR)
- A/B test and business impact monitoring
- Versioning and model retraining support
Manufacturing: Quality Inspection and Predictive Maintenance
Quality control and defect detection. CV models for product inspection are one of the most mature industry tasks. YOLOv10 for defect detection, SegFormer for segmentation. Specifics: class imbalance (defects are rare), high recall requirement (missing a defect is worse than false alarm). Typical dataset: 500–2000 defect images + 500–1000 normal. Few‑shot learning via DINO or SAM 2 works with 50–100 annotated examples. We gained experience on an electronics production line — recall 0.95 at FPR 0.03. A predictive maintenance deployment saved a manufacturing client $500,000 per year in unplanned downtime.
Predictive maintenance. Vibration sensors, current sensors, thermocouples → feature extraction → anomaly or mode classification. Models: LSTM‑AE for unsupervised, LightGBM for supervised (if failure history is available). Integration with SCADA/OPC‑UA via opcua-asyncio or MQTT. Key metric: False Negative Rate — a missed pre‑failure is more costly than a false alarm. Threshold tuned to business cost of each error type. Timeline: 3 to 6 months to production.
Digital twin and simulation. Surrogate models — ML models replacing expensive physical simulation. If a CFD simulation takes 6 hours and a surrogate (trained on 10,000 simulations) takes 0.01 seconds, that's 2,000,000× speedup for optimization. SALib for sensitivity analysis, botorch for Bayesian optimization on top of surrogate.
Deliverables in a Manufacturing Project
- Sensor / image data audit
- Model selection for task (CV / time series / vibro)
- Pipeline development (ETL, feature engineering, training)
- Deployment on Edge / on‑premise
- Model monitoring and retraining
General Principles of Industry AI
Regardless of industry, there are patterns that work everywhere. Data matters more than architecture. In healthcare, 1000 quality labeled images are better than 100,000 poor ones. In manufacturing, 200 real defect examples are more valuable than 10,000 synthetic ones. Compliance‑first design — regulatory requirements are easier to embed into architecture from the start than to add later. Logging, explainability, versioning from day one. Domain expert on the team — an ML engineer without domain knowledge does slowly and error‑prone what an ML engineer plus a doctor/financier/technologist does quickly and correctly.
We guarantee certification to customer requirements (ISO 13485, SOC 2, GDPR) and provide full model documentation (model card, datasheet, compliance report). Our experience: 10,000+ engineering hours and 80+ projects.
Work Process for an Industry AI Solution
-
Domain immersion (2–3 days) — interviews with experts, studying regulatory requirements, auditing available data.
-
MVP design (1–2 weeks) — stack and architecture selection, feasibility assessment.
-
Development and validation (from 4 weeks to 6 months depending on industry) — model training, testing, compliance.
-
Integration and deployment (1–4 weeks) — on‑premise / cloud / edge, documentation, staff training.
-
Support and monitoring — model drift, retraining, SLA.
Estimated timelines:
| Type of Solution |
Minimum Time |
Full Cycle with Compliance |
| Retail recommendation |
4–8 weeks |
3–6 months |
| Credit scoring |
6–12 weeks |
6–12 months |
| Medical imaging |
12–24 weeks |
12–24 months (with CE) |
| Predictive maintenance |
8–16 weeks |
3–6 months |
Cost is calculated individually for each project. Get a consultation — we will evaluate your dataset, regulatory map, and business goals.
Why Choose Our Industry AI Solutions?
-
80+ completed projects in fintech, healthcare, retail, and manufacturing.
- 5 years on the market — proven experience with compliance and deployment.
- Quality guarantee: we ensure target metrics (AUC, recall, latency p99) and provide full documentation.
- Licensed technologies: PyTorch, MONAI, LightGBM, Qdrant — we use open‑source with commercially safe licenses.
- Flexibility: we work as a contractor or as an extension of your team.
Contact us for a free data audit and consultation. Request a proposal with a detailed work plan. We will discuss your task and prepare a commercial proposal.
