The practices behind the results
Ten delivery practices across our two engines, AiRE and Labs, plus the data and production foundations both stand on. Each page says what you get, how long it takes and where it fits, with the documented work to back it up.
Proof, not promises
22%
EBITDA improvement
PE-backed retailer. Needed AI-driven pricing to compete with online giants.
3.2%
Margin increase
Asset manager. Manual due diligence taking weeks per deal.
80%
Faster processing
Government department. Contract review bottlenecking procurement.
AiRE
Roll out across the organisation
Opportunity assessment & roadmapping
A structured read on where AI pays back, prioritised into a costed, sequenced roadmap.
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Forward-deployed AI engineering
Engineers embedded in your team, shipping AI into live workflows week by week.
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Generative AI applications
LLM products, agents, and internal copilots built for measurable uplift.
See the serviceLabs
Replace the spreadsheet-era workflow
Workflow automation & internal tools
The processes running on spreadsheets and manual handoffs, rebuilt as AI-powered internal tools.
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Decision analytics
Analytics-driven products and internal tools that put a decision in front of the person making it.
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Forecasting and demand modelling
Demand forecasts, with a measured baseline and modelled seasonality, wired into your buying cadence.
See the serviceFoundations
Data & production engineering
Data platform builds
Modern data stack implementations: ingestion, warehouse, transformation, BI.
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MLOps and production ML
Taking prototypes to production: CI/CD, monitoring, retraining, drift detection.
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Customer segmentation
Behavioural and value-based segmentation that marketing and product can act on.
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Sales and pipeline enrichment
Lead and deal scoring, plus deal sourcing across the wider market, wired into the CRM your revenue team already runs.
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Diagnose, prototype, build, productionise
Every practice runs on the same arc, and it loops. What we learn in production feeds the next diagnosis.
Diagnose
We map the decision, the data and the workflow before writing a line of code, so effort lands where value actually leaks.
Prototype
A working prototype on your data in weeks, not a slideware demo. Enough to prove the shape before the build cost.
Build
We build the real thing on your stack, tested and version-controlled, designed to be owned by your team rather than rented from ours.
Productionise
Monitoring, handover and the runbooks that keep it honest after go-live. Then what we learn feeds the next diagnosis.
In their words
What clients say
QuantSpark helped us to develop our demand forecasting tool at Acqua & Sapone, enabling the change management needed to move away from a manual ordering process. Their solution gave the buying team clear visibility of model performance and the practical tools to fine-tune predictions — particularly around promotional periods. It was an excellent example of a pragmatic approach to analytics, that will drive real operational value. We are in the process of rolling the tool out more widely.
Paul Archer
Data Science Director, TDR Capital
Demand forecasting at Acqua & Sapone