Service
Applied ML & Data Science
Bespoke machine-learning systems beyond LLMs — forecasting, risk scoring, anomaly detection and quantitative signals — engineered for production, with the validation rigour that separates a real edge from a curve-fit.
Applied ML means building predictive systems that learn from your own structured data — forecasting, ranking, scoring, anomaly detection, signal generation — as opposed to generative AI. On the tabular data most enterprises run on, classical ML (gradient-boosted trees) usually beats both LLMs and deep learning, at a fraction of the cost. We build these to production standard, with rigorous validation.
The problem
The right tool for the data
Generative AI is remarkable, but most enterprise data is tabular — transactions, customers, SKUs, ledgers, sensor logs — and on tabular data classical ML still wins. In head-to-head studies, gradient-boosted trees (XGBoost, LightGBM, CatBoost) consistently match or beat deep learning and roughly double the predictive quality of the best LLMs, while training in minutes and serving in milliseconds. The credible position isn’t "AI for everything"; it’s using the tool the problem demands.
The solution
Where automation removes the friction
Techniques & stack — and when each fits
For tabular work we choose deliberately: XGBoost for maturity and robustness at scale, LightGBM for speed on large numeric data, CatBoost for categorical-heavy or smaller datasets. For genuine sequence problems we use LSTM and Temporal Fusion Transformers (which are interpretable and handle known-future covariates) — though even in forecasting, boosted trees frequently beat neural nets, and the best results are often hybrids. The real edge usually comes from feature engineering, not model choice.
Production is the other half: orchestrated, reproducible training pipelines; feature stores to kill training-serving skew; drift monitoring; and automated retraining with safe rollback. The modern stack — Python, scikit-learn, XGBoost/LightGBM/CatBoost, PyTorch, SHAP, MLflow — engineered so the model runs reliably, not just in a notebook.
Quantitative rigour (where most ML "edges" are illusions)
In quantitative finance, the model is the easy 10% — rigorous validation is the 90% that separates a real edge from a curve-fit artifact. We work in the López de Prado tradition: triple-barrier labelling, meta-labelling for bet sizing, and purged/embargoed (combinatorial) cross-validation to stop leakage from overlapping labels. We correct backtest Sharpe for multiple testing (deflated Sharpe, probability of backtest overfitting) and report all trials. The sober reality we build around: live performance typically runs 30–50% worse than backtest, and most academic strategies fail with real capital — so we engineer for out-of-sample, and we never promise returns.
Production, governance & when not to use ML
Data leakage is the #1 silent killer — lookahead bias, target leakage, temporal misalignment — so we audit for it explicitly. For regulated finance, SHAP turns boosted-tree models into auditable ones (supporting FCRA/ECOA adverse-action reasons and Basel III/GDPR transparency). And we’ll tell you when ML is the wrong tool: deterministic, rule-expressible logic, tiny datasets, or strict audit needs are often better served by rules or a hybrid than by a model.
Example workflows we build
- Forecasting (demand, load, volatility) with boosted trees / TFT
- Risk & credit scoring (XGBoost + SHAP, audit-ready)
- Fraud & anomaly detection (real-time, sub-5ms scoring)
- Quant signal generation (triple-barrier, meta-labelling, purged CV)
- MLOps: pipelines, feature store, drift monitoring & retraining
The results
The commercial impact
Our approach
From manual to automated
- 01Frame the problem
We confirm ML is the right tool, define the target and the metric that matters (economic, not just statistical).
- 02Engineer features & model
Leakage-safe feature engineering and the right technique (boosted trees, LSTM/TFT, hybrids) for your data.
- 03Validate rigorously
Walk-forward / purged cross-validation, multiple-testing correction, and honest out-of-sample expectations.
- 04Deploy & monitor
Production pipelines, feature store, drift monitoring and automated retraining — with SHAP explainability.
Why a custom build beats off-the-shelf
- The right tool by evidence — classical ML for tabular, not an LLM forced onto a prediction problem.
- Validation rigour (purged CV, deflated Sharpe, leakage audits), engineered for out-of-sample.
- Production-grade MLOps, not a notebook — feature stores, drift monitoring, retraining.
- SHAP-based explainability for regulated use; honest about when ML is the wrong tool.
Frequently asked questions
When should we use classical ML instead of an LLM?
For tabular/structured data — forecasting, ranking, scoring, anomaly detection — boosted trees like XGBoost are usually more accurate, far cheaper, and faster to train and serve than an LLM. An LLM is the wrong tool for predicting churn, default, demand or a trading signal.
How do you prevent overfitting and data leakage?
Leakage is the #1 silent killer, so we audit for it: point-in-time features, purged and embargoed (combinatorial) cross-validation, walk-forward testing, and multiple-testing correction (deflated Sharpe, probability of backtest overfitting). We report all trials.
Why does a backtest look better than live performance?
Because backtests are easy to overfit. Live results typically run 30–50% worse, so we validate out-of-sample with realistic costs and slippage and engineer for that — we never quote trading returns.
XGBoost vs LightGBM vs CatBoost — which do you use?
It depends on the data: LightGBM for speed on large numeric datasets, CatBoost for categorical-heavy or smaller data, XGBoost for maturity and robustness at scale. Often the edge is in feature engineering, not the model.
Can an ML model be explainable enough for regulators?
Yes — SHAP-based explanations make boosted-tree models auditable, supporting FCRA/ECOA adverse-action reasons and Basel III/GDPR transparency, while keeping the accuracy advantage over logistic regression.
What does it cost?
Engagements are fixed-price and scoped to the outcome. Every engagement is fixed-price with ROI targets agreed up front, backed by our 90-day ROI guarantee. Book a free audit for a clear price and ROI estimate.