Data Engineering & ML
Data Engineering & Machine Learning Services
We build the data infrastructure and models that reliable AI requires: ingestion and transformation pipelines, warehouses and lakehouses, feature stores, and machine learning models for forecasting, anomaly detection, recommendation, classification, and scoring - plus the MLOps practice that keeps them accurate in production.
Data Engineering
Two things get lost in the excitement about language models. First, most AI systems fail for data reasons, not model reasons. Second, for a large share of business problems - predicting demand, catching fraud, scoring leads, detecting anomalies - classic machine learning is more accurate, far cheaper, and more explainable than a large language model. We build both, and we choose based on the problem rather than the headline.
Ingestion and Integration
Batch and streaming pipelines from your applications, SaaS platforms, databases, and third-party feeds.
Transformation and Modeling
dbt-style transformation layers with tested, documented, version-controlled logic.
Warehouse and Lakehouse Builds
On Snowflake, Databricks, BigQuery, Fabric, or Azure/AWS-native services, matched to your estate.
Feature Stores and ML Infrastructure
Consistent features between training and serving, which is where a surprising share of model degradation originates.
Data Quality Engineering
Validation, tests, and monitoring so problems are caught in the pipeline rather than in a board report.
Real-Time Architecture
Event streaming where latency genuinely matters, and not where it doesn't.
Machine Learning
Forecasting - demand, revenue, capacity, and churn, with confidence intervals that make the uncertainty explicit instead of hiding it in a point estimate.
Anomaly detection - fraud, quality defects, operational outliers, and security signals.
Recommendation and personalization - for commerce, content, and next-best-action.
Classification and extraction - document routing, triage, sentiment, and structured extraction from unstructured sources.
Optimization and scoring - pricing, routing, scheduling, and lead or risk scoring.
MLOps: The Part That Decides Whether It Lasts
A model without monitoring is a liability with an API. Our MLOps practice covers versioning of data, code, and models; automated retraining pipelines; drift detection on inputs and outputs; performance dashboards owners actually read; and rollback capability. Models decay because the world changes - the question is only whether you find out from your monitoring or from your customers.
Not Sure Whether You Need Better Data or Better Models?
We'll review your pipelines and use cases and tell you which investment moves the needle first.
Where Data Pipelines Actually Break
Pipelines rarely fail loudly. They degrade, and the damage surfaces in a board report weeks later.
Schema drift. An upstream system adds or renames a field. The pipeline continues, silently dropping or misassigning data. Detectable with schema validation at ingestion; invisible without it.
Silent partial loads. A source is briefly unavailable, the pipeline succeeds with incomplete data, and downstream metrics are quietly wrong. Row-count and completeness checks catch this; success/failure alerting alone does not.
Duplicate and late-arriving records. Common with event data and API pagination. Without idempotent loading and late-arrival handling, numbers shift retroactively and trust in the platform erodes.
Timezone and date handling. Persistently underestimated, and a reliable source of reporting discrepancies in any organization operating across regions.
Definition drift. Two teams calculate the same metric differently, both correctly by their own definition. This is a governance failure that looks like a data failure - and it's the one that generates the most meeting time.
We build validation, reconciliation, and freshness monitoring into pipelines as standard, because the cost of discovering these in a board pack is far higher than the cost of testing for them.
Choosing Between ML and an LLM
A decision teams get wrong in both directions, at real cost:
| Use classic ML when… | Use an LLM when… |
|---|---|
Predicting a number or a category from structured data | Working with unstructured text, documents, or language |
Accuracy and explainability are paramount | Flexibility matters more than deterministic output |
High volume makes per-call cost significant | Volume is moderate and the task is genuinely linguistic |
You have labeled historical data | Little or no labeled data exists |
A regulator may ask why a decision was made | The output is assistive rather than decisive |
Using a language model to forecast demand is expensive and less accurate than a well-tuned time-series model. Using classic ML to summarize a contract is not feasible. Many real systems combine both: an LLM extracts structured fields from a document, and a classic model scores the result.
Making Models Trustworthy to the Business
Technical accuracy isn't sufficient - a model people don't trust doesn't get used. What earns trust:
Explainability appropriate to the stakes. Feature importance and per-prediction explanation where decisions affect customers, so a human can defend the outcome.
Confidence signalling. Predictions accompanied by uncertainty, with low-confidence cases routed to people rather than presented as equally certain.
Documented performance. Accuracy stated honestly, including where the model performs worst. Models that overclaim get abandoned after their first visible failure, regardless of aggregate performance.
Monitoring visible to owners. The business owner should be able to see whether their model is still performing without asking an engineer.
Frequently Asked Questions
Through schema drift, silent partial loads, duplicate or late-arriving records, and timezone handling errors - none of which trigger a failure alert. Schema validation, completeness checks, and freshness monitoring are what turn silent degradation into a visible alert.
Use classic ML for structured prediction where accuracy, cost at volume, and explainability matter - forecasting, scoring, anomaly detection. Use language models for unstructured text and language tasks. Many production systems combine both rather than choosing one.
Designing and building ingestion and transformation pipelines, warehouse or lakehouse platforms, data quality testing and monitoring, and the infrastructure that serves data to analytics and machine learning systems reliably.
Use classic ML for structured, numerical prediction problems - forecasting, scoring, anomaly detection, recommendation - where accuracy, cost, and explainability matter. Use language models for unstructured text, language generation, and reasoning over documents. Many real systems combine both.
MLOps is the practice of deploying, monitoring, retraining, and governing models in production. Without it, models silently degrade as data drifts, and nobody notices until a business metric moves - by which point trust is already damaged.
With reasonable data in place, a first production model typically takes eight to sixteen weeks including data preparation, training, evaluation, and deployment. Where data readiness is the constraint, that work comes first - see Data Strategy.
Yes. We build on Snowflake, Databricks, BigQuery, Microsoft Fabric, and cloud-native services, and we prefer improving a platform you already run over recommending a migration you don't need.