Custom ML for problems where being wrong has a cost. Supervised, unsupervised, reinforcement, and deep learning. Built with a measurable target, a baseline to beat, and a monitoring plan for the day the model drifts.
We sell the outcome, not the algorithm. Every model has a measurable target, a baseline it has to beat, and a monitoring plan for the day it drifts. If a regression beats a transformer on your problem, we ship the regression.
Sometimes the right answer is a transformer fine-tuned on your domain. Sometimes it's a logistic regression that runs in 2ms and is explainable in court. The bar to ship something more complex than the simplest model that works is high, and we're honest about which is which.
Industries: healthcare, defense, finance, manufacturing, and the public sector. Each model ships with a deployment plan, a rollback plan, and a named owner on our side until the system has run clean for two release cycles.
We pick the technique that fits the problem, not the technique that's trending. Here's the full toolbox we draw from on every engagement.
Predicting categories or continuous values from labeled training data. Logistic regression, gradient-boosted trees (XGBoost, LightGBM), random forests, SVMs, and feed-forward networks, benchmarked head-to-head before we commit to one.
Surfacing structure inside data with no ground-truth labels. K-means, DBSCAN, hierarchical clustering, PCA, t-SNE, UMAP, with cluster validation, stability analysis, and human-readable summaries of what each cluster actually represents.
When linear models hit a wall, we go deep. Convolutional networks for vision, recurrent and transformer architectures for sequence and language, custom heads on pretrained foundation models, all trained with proper validation, regularisation, and reproducibility.
For problems where every decision affects the next state, recommendation, pricing, scheduling, control. Policy gradient, Q-learning, contextual bandits, and offline RL on logged data, with safety constraints baked in for production.
Predicting demand, detecting failures, flagging unusual behavior. ARIMA, Prophet, gradient-boosted regressors, LSTMs, and temporal fusion transformers, chosen based on horizon, seasonality, and how much human-readable the forecast needs to be.
Classification, detection, segmentation, OCR, and visual quality control. Built on modern backbones (ResNet, EfficientNet, ViT, YOLO, SAM) and fine-tuned on your domain data, with the data-pipeline tooling that real projects need.
Representative ML problems we've solved, the kinds of decisions and signals our models have moved from "good idea" to "running in production."
Deep-learning classifier on Q-Chat-10 questionnaire responses, validated against clinician diagnoses. Surfaced at-risk children months before a standard pathway would have caught them, published in a peer-reviewed journal, deployed as a clinician-facing tool.
Multi-modal sensor fusion (vibration, temperature, current draw) feeding a gradient-boosted classifier with a 6-hour prediction horizon. Replaced a calendar-based maintenance schedule with a signal-based one, six-figure annual savings on a single line.
Sub-100ms transaction-risk scoring with SHAP-based explanations for compliance review. Streaming feature pipeline, gradient-boosted scoring model, and a kill-switch dashboard for the operations team, the explanation is part of the deliverable.
Five operating commitments that govern every machine-learning engagement we take on, from the first whiteboard session to the model retraining you'll be doing in year three.
Supervised classification and regression, time-series forecasting, anomaly detection, computer vision, and applied NLP in healthcare, defense, finance, manufacturing, and the public sector. We avoid green-field "let's see if AI works" exploration; the engagements we take on have a measurable target and a baseline to beat.
We always benchmark the simplest model that could plausibly work first, logistic regression, gradient-boosted trees, or a small MLP. If a more complex architecture beats that baseline by enough margin to justify the deployment cost, we use it. If not, the simple model ships.
A containerised inference service behind an authenticated API, an evaluation harness running on a schedule against held-out data, dashboards for latency and accuracy, alerting on drift, and a runbook your team can operate without us. Jupyter notebooks are not the deliverable.
For most engagements, yes, at least de-identified samples large enough to train and validate. We sign an NDA before kickoff and work inside whatever data-residency boundary you need: your VPC, your on-prem, or your air-gapped lab.
A focused single-model engagement runs 8 to 16 weeks: 2 weeks of discovery and benchmarking, 4 to 8 weeks of model development, 2 to 4 weeks of deployment and monitoring setup, and 2 weeks of warranty handover. Larger multi-model platforms take longer.
Tell us the decision you're trying to automate or improve. We'll come back with a one-page recommendation: the right technique, the data you need, the baseline to beat, and a realistic timeline. No sales pitch, no science fiction.