Gradient-boosted everything.
XGBoost, LightGBM, CatBoost on structured data. Often beats neural nets on tabular problems, and the inference cost is two orders of magnitude lower.
Own the model
own the moat
When a vendor API won't carry the competitive differentiation, we build the model in-house. Classical ML, deep learning, fine-tuning, distillation, embeddings, with feature stores, training pipelines and model registries that survive team turnover.
Discipline areas
Where ML earns its keep
Not every problem needs a deep network. We match model class to the data, latency and interpretability budget, and stay honest when a simpler technique outperforms the trendy one.
Neural nets when they fit.
CNNs for vision, transformers for sequence, graph nets for relational structure. Custom architectures when off-the-shelf doesn't match the problem geometry.
Adapt, don't pretrain.
Full fine-tune, LoRA / QLoRA, DPO / RLHF on top of open-weight base models. When your data is the asset, adapting a strong base model is the leanest moat.
Big teacher, small student.
Train a small model to mimic a large one's outputs. Collapse a 70B-param teacher into a 7B student that serves at one-tenth the inference cost, for narrow tasks.
Vector representations that matter.
Custom text, image, multimodal and domain-specific embeddings. Evaluated on your actual retrieval task, not benchmark leaderboards your users will never run.
Time series, done right.
Prophet, N-BEATS, TFT, plus classical ARIMA/ETS ensembles. Demand, capacity, anomaly detection, with confidence intervals the planning team can actually use.
Lifecycle
From dataset to deployed system
Every model we ship goes through four stages with versioned artifacts at each boundary. Reproducibility isn't optional, it's the reason the model survives the first incident.
01, Data
Labels, features, versions.
Labeling workflow, feature store, dataset versioning. Every training run traces back to a snapshot.
- DVC
- Feast
- Label Studio
- Snorkel
02, Train
Experiments & runs.
Tracked hyperparameter sweeps, distributed training, checkpoint registry. Nothing untracked reaches staging.
- Weights & Biases
- MLflow
- Ray Train
- DeepSpeed
03, Evaluate
Offline, online, adversarial.
Holdout + cross-val + slice analysis. Fairness audit, stress tests, drift baseline. Promotion gated on metric contract.
- Evidently
- WhyLabs
- Ragas
- Alibi
04, Serve
Latency, cost, rollback.
Low-latency inference, autoscaling, shadow deploy, canary, automated rollback on SLO breach.
- Triton
- TorchServe
- Ray Serve
- vLLM
Stack
Tooling we run in production
Our default stack is battle-tested across ML engagements. Swaps happen per engagement when the problem warrants, never for fashion.
01 Training
- PyTorch
- JAX
- Ray Train
- DeepSpeed
- Hugging Face
- Unsloth
- Axolotl
02 Experiments
- Weights & Biases
- MLflow
- DVC
- Optuna
- Feast
- Label Studio
- Snorkel
03 Inference
- vLLM
- TGI
- Triton
- Ray Serve
- ONNX Runtime
- TensorRT
- ExecuTorch
Adjacent disciplines
ML doesn't live alone
Every ML system leans on its neighbours. These are the disciplines we co-run on most engagements.
Foundation
Data Engineering
Pipelines, lakes, warehouses, streams. No ML model works on dirty data, the foundation layer we build before model work starts.
AppliedComputer Vision
Vision models in production: detection, segmentation, OCR, video analytics. ML in manufacturing, retail, medical, security.
UmbrellaArtificial Intelligence
The broader discipline: foundation models, retrieval, agents, evaluation, inference ops. ML sits inside this layer.
ShortcutAI Integration
Sometimes you don't need a custom model, you need existing AI wired into your product. Faster route when that fits.
Classical · deep · tabular
Have a dataset, need a model
Share the task, the data shape and the latency envelope. We come back with a model shortlist, training cost estimate and feasibility note inside ten working days. We'll tell you when gradient boosting beats a neural net, and we'll save you the GPU bill.