The practices behind the results
Six delivery practices, drawn on by both QuantSpark engines. Each page says what you get, how long it takes and where it fits, with the documented work to back it up.
Generative AI applications
LLM products, agents, and internal copilots built for measurable uplift.
Typical engagement: 6 to 12 weeks, 2 engineers, outcome-linked pricing.
See the serviceData platform builds
Modern data stack implementations: ingestion, warehouse, transformation, BI.
Typical engagement: 12 to 20 weeks, 2 to 3 engineers, milestone-based pricing.
See the serviceDecision analytics
Analytics embedded in decision workflows, not dashboards for dashboards' sake.
Typical engagement: 4 to 8 weeks, 1 engineer, fixed scope.
See the serviceForecasting and demand modelling
Time-series forecasts and demand models wired into the operational cadence.
Typical engagement: 6 to 10 weeks, 1 to 2 engineers, fixed-scope pilot.
See the serviceCustomer segmentation
Behavioural and value-based segmentation that marketing and product can act on.
Typical engagement: 4 to 8 weeks, 1 engineer, fixed-price discovery.
See the serviceMLOps and production ML
Taking prototypes to production: CI/CD, monitoring, retraining, drift detection.
Typical engagement: 8 to 16 weeks, 2 engineers, rolling retainer after go-live.
See the service