AurvikAI

Machine Learning Development

ML models that predict what matters — and stay accurate in production.

We start with the simplest model that solves the problem. Complexity is added only when it improves the outcome.

Custom machine learning for classification, prediction, recommendation, and anomaly detection. Built by engineers who have shipped production ML systems across healthcare, finance, logistics, and retail.

18yrproduction ML experience

50+models in production

4industries served

Talk to the AI team →See related work

ML philosophy

Every model earns its place in production.

We establish baselines before building models. The ML model needs to beat the baseline meaningfully to justify the complexity. Many don't — and we'll tell you when that happens. That's how you get systems that are maintainable, not just impressive in a demo.

34%

of proposals we decline after data assessment

AurvikAI engagement data

Problem framing first

Translating the business problem into the correct ML problem — classification, regression, ranking, or anomaly detection — before touching data.

Baseline before model

Rule-based or naive statistical baselines that define the bar the ML model must meaningfully exceed.

Cost-aware evaluation

Measuring the business cost of each error type — a false negative in fraud detection costs differently than in recommendation.

Drift-aware deployment

Monitoring for data drift and concept drift from day one, with automated retraining triggers.

ML capabilities we deliver

Production systems solving real business problems.

Classification & categorisation

Document classification, support ticket routing, content moderation, and lead scoring. From binary classification to multi-label taxonomies with hundreds of categories.

NLPVisionTabular

97%

accuracy on ticket routing

How we build production ML

A repeatable process that reduces risk and accelerates time to value.

80% of ML project time is data work. We front-load it deliberately.

Data profilingAudit

Completeness, quality, leakage risk, and distributional analysis before feature engineering begins.

Feature engineeringBuild

Domain-informed features that capture real signal — combined with your domain experts' knowledge.

Data validationQuality

Automated validation pipelines that catch data problems before they reach the training pipeline.

Production monitoring

Models degrade. We design for that reality.

Every ML system includes drift monitoring, retraining triggers, and performance dashboards. Data drift means the inputs are changing. Concept drift means the relationship between inputs and outputs is changing. Both degrade predictions. Both are detectable. Both trigger automated retraining in our systems.

models degraded undetected

<5min

avg. drift detection time

See our ML monitoring approach →

Machine learning model monitoring dashboard showing drift detection and performance metrics

Data scientist reviewing machine learning model evaluation results and feature importance analysis

AurvikAI model evaluation — business-relevant metrics, not just accuracy.

Typical ML engagement timeline

From problem framing to production deployment.

2–3 weeks

Problem framing & data audit

Translate the business problem into an ML problem. Audit data for quality, completeness, and leakage risk. Establish baseline performance.

ML problem specificationData quality reportBaseline model

3–5 weeks

Feature engineering & model development

Build domain-informed features. Train candidate models. Evaluate against business-relevant metrics. Select the model that balances performance with maintainability.

Feature pipelineTrained model candidatesEvaluation report

2–3 weeks

Production deployment & monitoring

Deploy with drift monitoring, retraining triggers, and performance dashboards. Validate in production against the evaluation framework.

Production modelMonitoring dashboardModel card

INSIGHTS

Thinking worth reading

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