Methodology
A repeatable pipeline from raw data to analytical output
Turbo Frog develops and uses its own software, analytical methodologies, algorithms, machine learning models and data-processing techniques.
- 01
Intake & assessment
We review sources, formats, volume and quality, then define the target output and the measures used to judge it.
- 02
Cleaning & validation
Records are validated, deduplicated and normalised. Anomalies are flagged and resolved against documented rules.
- 03
Structuring
Data is mapped into a consistent schema, with derived attributes added where the analysis requires them.
- 04
Classification & modelling
Proprietary taxonomies, statistical methods and machine learning models assign categories and produce scoring outputs.
- 05
Analytical review
Results are reviewed against expected distributions and sampled manually before anything is released.
- 06
Delivery & iteration
Processed datasets, classifications, scores and reports are delivered in the agreed format, with recurring runs where needed.
Principles
How we work
Reproducibility
Every transformation is defined in code and can be re-run on new data.
Transparency
Deliverables are accompanied by documentation of methods and limitations.
Proportionality
Model complexity is matched to the problem, never applied for its own sake.
Confidentiality
Client data is handled strictly within the scope of the engagement.