Services
Decision analytics
Decision products, not dashboards. This is QuantSpark Labs building a tool for a single high-value decision: an anomaly detector, a prediction, a queryable pricing database, an extraction, wired onto the enterprise platform you already run and surfaced where the choice is made. It is a build, not a study, and it is distinct from workflow automation: that rebuilds a manual process, this creates an analytical capability the business did not have. Fixed scope, small team, a working product in weeks.
The process we optimise
The process we optimise: a high-value decision that has no product behind it
This practice is QuantSpark Labs building a product, not running a study. There is a decision your organisation makes repeatedly and at real value, how to price, where revenue is leaking, which case to act on first, and today it is made on judgement plus whatever someone can assemble by hand, because no tool exists that puts the analysis in front of the person making the call. We build that tool: an anomaly detector, a prediction, a queryable pricing database, an extraction model, wired onto the enterprise platform and data you already run, and surfaced at the exact point the decision is taken. The deliverable is a working decision product your team owns, not a slide of findings or a dashboard sitting to one side. That is the line between this and workflow automation: workflow automation rebuilds a manual spreadsheet process step for step, while this builds a new analytical capability the business did not have and puts it where the decision happens.
Before and after
What changes when the system is rebuilt
Before: the decision has no product behind it
- A high-value, recurring decision is made on judgement and whatever can be pulled together by hand.
- The data that would inform it is buried in contracts, systems or reports no one has time to reconcile.
- Where dashboards exist, they sit apart from the decision, so they inform little and change less.
- No one can say whether better analysis would pay back, because the analysis has never been built.
After: a decision product on your platform
- A working tool, an anomaly detector, a prediction or a queryable database, built on your enterprise platform.
- The analysis is surfaced at the moment of choosing, inside the interface where the decision is made.
- The recurring decision is faster to take and easier to defend, with the evidence traceable to its source.
- Adoption is measured, so you can see the product being used rather than hope that it is.
How we think about it
We start from the objective, then work outwards
The same discipline runs through every engagement: understand what the process is for, then design the system to serve it and measure against it.
Start from the decision and the value in getting it right
We begin with the recurring decision itself: how it is made, how often, and what a better call is worth, whether that is revenue recovered, risk avoided or hours returned. Naming the value first keeps the work from drifting into analytics for their own sake, and it tells us whether a product is worth building at all. For a global airport services operator, the decision was whether billable services were going uncaptured; the answer was worth £2m to £2.2m of annual revenue.
Build the analytical capability the decision needs
We build the thing that did not exist: a supervised anomaly-detection model, a prediction, a document-extraction engine, whatever the decision actually requires, tested against your data. This is product building on analytics, not reporting. For a global travel logistics operator, we used document-extraction AI to turn pricing buried across scattered contracts into a single queryable database its commercial teams could negotiate from.
Put it on the enterprise platform, at the point of decision
We wire the capability onto the systems and data you already run and surface it where the choice is made, so the evidence is present without anyone leaving their workflow to find it. For a central government department reviewing thousands of contracts, that meant a review interface pre-filling each assessment from the extraction model, plus a live compliance dashboard for the Permanent Secretary, so specialists checked the AI's work rather than starting from scratch.
Measure adoption and the decision, not just the model
We track whether the product is used at the decision point and whether the decision improved, because a tool no one opens has failed however good the model is. Where the fit is wrong we adjust it. Independent measurement matters: a study of rent-arrears prediction software used by social landlords found evictions due to arrears fell 37.8 per cent among users over three years, against 13.3 per cent among non-users, an honest read on whether a decision product actually changes outcomes.
An engagement, step by step
A representative decision-product engagement, drawn from how we structure this work:
- Weeks 1 to 2
Diagnose the decision and size the prize
We pin down the recurring decision in scope, watch it being made, and agree what a better call is worth. We map who decides, on what information and under what pressure, and confirm the analytical capability that is actually missing.
- Weeks 2 to 4
Prototype the analysis
We build a first version of the model or extraction that answers the real question behind the decision, tested against your data. Prototyping early lets the decision-makers react to something concrete and tells us fast whether we have framed the capability correctly.
- Weeks 4 to 6
Build it into a product on your platform
We turn the analysis into a tool and place it on the systems you already run, surfaced where the decision is taken rather than in a standalone dashboard. The evidence is simply present when the choice comes up, and traceable back to its source.
- Weeks 6 to 8
Adopt and measure
We track whether the product is used at the decision point and whether the decision improved against the value we agreed. Where the fit is not right we adjust it. You leave owning a working decision product, with adoption visible and scope and price as agreed at the outset.
The output is a working decision product on your enterprise platform, owned by your team, with its use measured rather than assumed. It is a build, not a report, and the scope and price are fixed upfront.
What you get
- A working decision product built on your data
- The analytical capability the decision needs: detection, prediction or extraction
- Surfaced on the enterprise platform you already run, at the point of decision
- Adoption measurement so usage is visible, with scope and price fixed upfront
How it typically runs
Typical engagement: 4 to 8 weeks, 1 engineer, fixed scope.
Diagnose the decision
Weeks 1 to 2
Prototype
Weeks 2 to 4
Build and embed
Weeks 4 to 6
Adopt and measure
Weeks 6 to 8
Indicative timeline. Every project is scoped individually: book a discovery call and we will provide a detailed proposal within 48 hours.
Where it fits
This practice is delivered through both QuantSpark engines, depending on whether the answer is rollout or build.
Proof
Decision analytics in the field
DataControl Platform: intelligent private equity data management
A mid-market private equity firm
Predictive maintenance and ops dashboards for a UK industrial manufacturer
UK industrial manufacturer (£800m turnover)
Customer segmentation analytics suite lifts conversion probability by 30%
An online marketplace platform