How we help
For clients, we build data science solutions that run without constant supervision. For fun, we ship apps we'd actually want to use ourselves.
Model Development & Deployment
From problem definition to a model running on your infrastructure. We handle the entire pipeline — data preparation, model training, quality assurance and deployment. You get something that works.
AI Agents & Automation
We build AI agents that handle reports, data pipelines and internal workflows — so your team can focus on work that actually requires a human.
Dashboards & Reporting
We design and build dashboards that give your team a clear view of what's happening — updated automatically, built to last.
Data Science Consulting
We review your data, assess your existing ML models and honestly say what's worth building and what isn't.
What a project looks like end to end
Based on a typical data science engagement. Timelines are on the realistic side — complex data environments and slow access approvals add time.
Problem Discovery
Weeks 1–2- —Understanding the business problem and defining what success looks like
- —Getting data and system access (often slower than expected)
- —Stakeholder interviews to capture domain knowledge and business logic
- —Mapping available data sources and identifying gaps
Data Preparation
Weeks 3–4- —Transforming raw data into the required format
- —Data quality assessment — finding and resolving issues
- —Building reliable data pipelines
Model Development
Weeks 5–7- —Feature engineering and selection
- —Model training, tuning, and iteration
- —Results review with stakeholders — confirming the model solves the right problem
Productionalisation & Deployment
Weeks 8–11- —Hardening the model for production use
- —Deploying on your infrastructure
- —Integration and end-to-end testing
- —Longer if your infrastructure setup is complex or approvals take time
Monitoring & Retraining
Weeks 12–13- —Model performance tracking setup
- —Automated retraining logic implementation
- —Drift detection and alerting
Dashboard & Reporting
Weeks 14–15- —Results dashboard creation
- —Handover, documentation, and knowledge transfer
Roughly 15 weeks in total
Multi-model projects, legacy data environments, or long approval chains can push this closer to 5–6 months.