AI for the workflows that run the business. Applied ML. Anomaly detection. Process automation.

Rules engines run out of runway.

Every enterprise operation starts with a rules engine. And every rules engine, given enough time, turns into a five-thousand-line if-then maze that nobody wants to touch. Adding another rule makes three other rules misfire. Edge cases pile up. Exception queues fill up. An operations lead ends up hand-scoring half the volume the engine was supposed to automate. This is the moment where applied ML earns its keep — not generative AI, not an LLM, but a measurement-driven model that learns the patterns the rules engine could not capture.

• Classification models where the rule tree has become unmaintainable.
• Scoring and ranking where rule-based scores collapse under data drift.
• Anomaly detection where static thresholds generate too much noise.
• Routing and triage where the volume is too high for human review.

Production ML pipelines, not notebooks.

An ML model that runs in a notebook is not a product. We build the full pipeline — data ingestion from source systems, feature engineering, model training with honest evaluation, deployment as a versioned service, monitoring for drift and quality regression, and integration into the tools your team already uses. No decks about data strategy. No maturity models. We ship a predictor into production, measure it, and keep measuring.

• Data pipeline from source systems to trained model.
• Rigorous evaluation against real production conditions.
• Versioned model deployment with rollback and shadow-mode options.
• Drift monitoring, quality alerts, and scheduled retraining.
• Integration into Salesforce, Zendesk, SAP, Netsuite, internal tools, or custom UIs.

The middle layer that runs in production.

Applied ML is one of the most durable and high-impact categories of AI investment. It is the classifier that routes the ticket, the scoring model that prioritises the lead, the anomaly detector that catches the fraud, the forecaster that sets the inventory threshold. These systems quietly save or earn millions per year and run for a decade.

Choosing the right architecture for your problem.

Not every problem needs an LLM. Sometimes a classification model is faster, cheaper, easier to evaluate, and easier to audit. A 300-feature boosted tree with a clean train/test split can outperform a generative agent at a hundredth of the cost. Part of our value is helping you find the right architecture for your specific problem.

• We pick the simplest model that meets the metric.
• We compare baselines honestly — rules, XGBoost, small transformers, LLMs.
• We publish training metrics, drift metrics, and production outcomes.