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Decision Engine / QDIP

Turn uncertainty into decisions

Prediction tells you what may happen. QDIP helps determine what to do about it.

QDIP combines expected effects, uncertainty, costs and operational constraints to evaluate available actions, explain the trade-offs and support a decision that can be measured against what happens next.

See QDIP in actionDescribe your decision
01 / Constraints02 / supporting evidence03 / Human decision
QDIP/Decision workspace
Illustrative scenario · Local preview
Resource allocation
  1. 01Context
  2. 02Constraints
  3. 03Evaluation
  4. 04Evidence
01 / Inputs

Available budget

Requests
12
Priority
Urgent need
02 / Decision model
maximize priority_coveragesubject to total_cost <= budget eligible(request) == trueConceptual model · not executable code
03 / Evaluation Evaluation
Recommended allocationFund 5 highest-priority requests
requests funded5/12
04 / Explainability

Budget limits the number of eligible requests that can be funded.

HA human approves the final decision
Example data, not a live QDIP executionOpen the working Resource Allocation demo
Audit traceOpen an inspectable recommendation with alternatives, evidence and decision trace.Inspect a decision
Baseline comparisonCompare a QDIP recommendation with the current resource-allocation plan through the same evaluation path.Open Resource Allocation
Deterministic replayReview the research and reproducibility layer used to keep decision paths inspectable across runs.Review research

Does your team repeatedly make decisions like these?

Which requests should receive limited resources?
Which opportunity deserves attention first?
Should we act now, wait, or choose another option?
Which option should we choose when priorities conflict?

These decisions become difficult when objectives compete, resources are limited, rules constrain the available options, and some information remains uncertain. QDIP provides a consistent way to evaluate those choices.

The challenge isn’t having more information. It’s evaluating the same kind of decision consistently when priorities, constraints and evidence change.

CHANGE ONE THING

See a decision react to a changing constraint.

A small resource-allocation example. Change the available budget and inspect how the recommendation changes — without hiding the trade-off.

Recommendation, not an automatic decision
  1. 01
    ContextEvidence · priorities · constraints
  2. 02
    AlternativesFeasible actions
  3. 03
    EvaluationEffects · uncertainty · trade-offs
  4. 04
    RecommendationWhy · human decision
Available budget€40k
12 eligible requestsPriority: urgent need
RecommendationFund the highest-priority 5 requests
Why?

The tighter budget makes 7 otherwise eligible requests infeasible. QDIP preserves the configured priority order within the active constraint.

ALLOCATE · DECIDE · PRIORITIZE

One Decision Engine. Different decisions.

Resource Allocation

Where should limited resources go?

Evaluate competing needs against priorities, available resources and constraints.

Explore Resource Allocation
NeedsResourcesPrioritiesConstraints
Recommended allocationFits the active constraintsBalances the configured prioritiesKeeps the trade-offs visibleWhy this recommendation?
Have a similar recurring decision?Describe your decision

Applications built in the QDIP ecosystem

Dzvin.co

Mental Wellbeing Application

Dzvin is a mental wellbeing application in the QDIP ecosystem, exploring decision intelligence to support more informed, explainable decisions.

Explore Dzvin

What decision does your team make repeatedly?

Show us one recurring decision, the information used to make it, and the constraints that matter. We can determine whether it is a good fit for QDIP.

Describe your decision