Models built on what you already know
When general-purpose LLMs are not enough, custom machine learning takes over. We train forecasting, classification and vision models on your data, ship them to production and keep them healthy over time — with full governance.
From the first dataset to a model in production with active monitoring
Typical accuracy reached on document and image classification use cases
Prediction observability with automatic alerts on drift and degradation
When you truly need a model built for you
Off-the-shelf models are great at talking, writing and summarising. But when the problem is very specific — forecasting demand for a single SKU, spotting a defect on a production line, flagging fraud or recommending the right product to the right customer — you need a model trained on your data. That is the daily job of our ML engineers.
Before training anything we assess data quality, volume and representativeness. If needed we enrich it, label it with AI-assisted workflows and build a robust data pipeline.
Every model ships with versioning, automated evals, canary releases, rollback and operational dashboards. No mystery models that no one knows how to update.
If your data cannot leave your infrastructure we train and run the models on-prem. Otherwise we use managed GPUs on AWS, Azure or GCP — your call.
Nightly retraining with drift detection
A small scheduler that every night checks data drift, decides whether the model needs retraining and registers a new tracked version.
The model families we build
We pick the right technique for the problem without dogma. Combinations of models often beat the single best one.
Demand, sales, consumption
Time-series models (Prophet, N-BEATS, temporal Transformers) to plan production, inventory, energy, shifts and cash flow with declared error margins.
- Multi-horizon and probabilistic forecasting
- Seasonality, holidays and campaign handling
- Rigorous backtesting and production monitoring
Documents, transactions, intents
We route invoices, emails, tickets and transactions into business categories with custom models or fine-tuned pretrained encoders.
- Document AI for invoices, contracts, delivery notes
- Fraud and AML detection
- Intent detection for bots and automatic routing
Quality, OCR, recognition
We train vision models for in-line quality control, handwriting reading, object tracking and visual compliance.
- Object detection (YOLO, DETR) and segmentation
- Layout-aware OCR and extraction
- Edge deployment to cameras and industrial devices
Products, content, offers
Hybrid recommender engines (collaborative + content + contextual) optimising for conversion, retention or margin — not just clicks.
- Two-tower and sequence models
- Embedded A/B tests and contextual bandits
- Cold-start protection and catalogue management
Maintenance, fraud, security
Unsupervised and semi-supervised models that flag out-of-pattern behaviour in real time on sensor, log or transaction data.
- Predictive maintenance for industrial assets
- Fraud detection for payments and access
- Dynamic threshold alerting
Extraction, entity linking, sentiment
When you need deterministic answers on text — not free-form generation — we build traditional NLP pipelines with predictable performance and low cost.
- Custom named entity recognition
- Relation extraction and knowledge graphs
- Sentiment and aspect mining for voice of customer
Our ML method
We remove the risk of ending up with an interesting model that nobody can use.
1. Data discovery & feasibility
We inspect the available data, quantify signal vs noise, define the business metric and decide whether ML is the right tool — or whether a simpler approach wins.
Output: versioned dataset, feasibility score, statistical baseline, agreed success metric.
2. Offline prototype
We build multiple baselines and compare with modern techniques. Together we pick which model is worth pursuing, measuring accuracy plus cost and latency.
Output: reproducible notebooks, model card, bias and limits report, recommendation.
3. Production and MLOps
We package the model as a robust service, deploy with canary release, monitor for drift and retraining. Then we hand the keys to you — or run it for you with an SLA.
Output: live service, model CI/CD, monitoring dashboard, retraining plan.
Technology stack
We choose stacks that do not lock you to any single vendor and respect your IT constraints.
PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, JAX
MLflow, Weights & Biases, DVC, Feast, Kubeflow, Vertex AI Pipelines
TorchServe, Triton, BentoML, SageMaker, Azure ML, KServe
Evidently AI, Arize, Fiddler, custom Grafana dashboards
Snowflake, BigQuery, Databricks, Airflow, dbt, Kafka
AWS SageMaker, Azure ML, GCP Vertex AI, on-premise GPUs, Kubernetes
Related paths
Bring models into your systems
Once the model is ready it needs to live inside real workflows: ERP, CRM, apps. We handle that.
Pair ML with the power of LLMs
Custom models and language copilots often collaborate. We show you how.
Serve models at global scale
Low latency, failover and GPU management: ML in production is a distributed-systems problem.
Got data? Let us see what it can predict
A free session with one of our ML engineers to understand if your dataset is ready and which models could actually move the needle.
Questions about custom machine learning
When you need a predictive model trained on your data and how we ship it to production.