Your models already predict. The challenge is turning every score into a traceable, profitable and defensible action.
Churn, fraud, AML, risk, underwriting or purchase propensity: many companies already run models in production that output scores and help prioritise.
That is where the problem starts.
The model predicts.
The organisation still has to decide what to do.
Call the customer? Offer a promotion? Request documents? Investigate? Auto-settle? Escalate to a specialist team? Do nothing?
That decision often sits outside the model. It depends on the operator’s judgement, business manuals, available capacity and implicit rules that are often neither formalised nor audited.
The prediction is there.
The decision is not.
That gap between predicting and acting is one of the largest sources of lost ROI in enterprise AI.
The score predicts; it does not decide
A predictive model produces probabilities, rankings or scores. But a score, on its own, is not an operational decision.
A customer with a high churn probability does not say which offer they should receive. A fraud alert does not say whether to block, investigate, request documents or release. A high-risk file does not say whether it deserves manual review or whether the cost of investigation exceeds the expected risk.
Tools such as SHAP or LIME can help explain which variables influenced a prediction. They are useful for analysis, diagnosis and model understanding.
But they were not built to answer the key operational question:
What do we do now with this prediction?
Explaining a score is not the same as choosing an action. And understanding why the model predicted something does not guarantee the organisation will act in a profitable, consistent and auditable way.
The missing piece: a prescriptive layer
At AyGLOO we built that layer.
We call it Prescriptive Decision AI: a decision layer that sits above your existing models and turns predictions into executable, traceable and economically defensible actions.
- It does not replace your model.
- It does not require retraining it.
- It does not break your current processes.
- It does not change your core systems.
It adds what is usually missing: decision logic that translates the score into action, with control, traceability and economic criteria.
The model keeps predicting.
What changes is what happens next.
Explain, prescribe, execute or escalate
The platform separates three levels that are often blurred:
Explain means understanding how the model behaves.
Prescribe means deciding which action makes the most operational and economic sense.
Execute or escalate means acting automatically when there is enough evidence, or sending the case for human review when there is not.
This separation matters.
- Not every case should be automated.
- Not every prediction should become a direct action.
- Not every uncertainty should be resolved with manual review.
The right decision depends on the evidence available, the cost of acting, the cost of being wrong and the economic value of the case.
Three interlocking checks: how AyGLOO decides
Before executing an action, AyGLOO applies three modules that cross-validate each other.
1. Twin Model: verifiable equivalence between rule and model
The Twin Model identifies regions of model behaviour where a simple rule reproduces its predictions on observed cases.
When that equivalence is verified in a segment, there is mathematical evidence that the rule reproduces the model’s behaviour in that region—not a post-hoc approximation like SHAP or LIME, but a structural property that can be checked case by case.
The organisation stops relying only on an approximate explanation of the score and leans on a understandable, auditable and verifiable rule before acting.
2. ISA: the portfolio turned into measured typologies
ISA groups cases into typologies defined by verifiable rules: customers, files, alerts, operations or applications that share common patterns.
Each typology is measured with real historical data: conversion, fraud, churn, recovery, friction, model reliability or operational cost.
Decisions are therefore tied not only to an isolated score but to membership in a known, measured and monitored group.
When typology patterns shift, ISA helps detect drift and surface cases that might previously have gone unnoticed.
3. Graph XAI: the network an individual model does not see
In many sectors, risk is not only in the individual or the file. It is in the network. Organised fraud, AML, claims abuse, linked accounts, recurring suppliers, intermediaries, merchants, garages or beneficiaries may not show clearly in a case-level model.
Graph XAI analyses those connections and surfaces relationships a standalone model may miss—especially relevant in banking, insurance, telco, utilities and retail, where part of the value or risk lies in collective patterns, not isolated events.
For an action to run automatically, the controls must converge. The platform acts not because the score is high or low, but because there is a verifiable basis to act.
Decisions are economic, not only statistical
The score does not decide alone. The decision must incorporate economics, risk and intervention cost.
AyGLOO combines counterfactual logic, what-if analysis and economic criteria to compute which action makes the most sense for each case.
The question shifts from:
What probability did the model output?
to:
Which action maximises expected return, reduces risk or minimises total case cost?
The system identifies which actionable variable would move a case into a clearer lane—a document, a check, an offer, a call, an extra review or an external verification.
It then combines that lever with customer value, expected risk cost, friction created and operational cost of intervening.
The output is not only a recommendation.
It is a decision defensible with numbers.
A full package with every decision
When the system decides to act, it does not stop at a label.
It can generate customer communications, agent scripts, the recommended CRM action or a briefing for a specialist team.
When it should not act alone, it escalates the case with a full package:
- estimated risk;
- economic value of the case;
- typology;
- verifiable explanation;
- network signals;
- recommended lever;
- estimated ROI;
- next best action.
Human teams do not receive only an opaque score. They receive a prepared decision with context and rationale.
This cuts analysis time, avoids unnecessary reviews and concentrates human judgement where it truly adds value.
Traceability for business, audit and compliance
Every decision, automatic or escalated, is linked to the data, the model, the rules, human intervention and the final outcome.
That makes it possible to reconstruct why you acted, what evidence existed, which alternatives were considered and what impact the decision had.
- For the business: operational control.
- For internal audit: traceability.
- For compliance: reviewability.
- For model risk management: an extra governance layer on existing models.
The platform does not replace each institution’s validation processes. It complements them with a reproducible, documented and monitorable decision layer.
No reliance on generative LLMs
AyGLOO’s decision layer does not rely on generative LLMs to justify actions. Agents are deterministic, auditable and reproducible—avoiding stochastic variability, hallucinations or uncontrolled generated explanations.
Decisions rest on verifiable rules, structural evidence, economic analysis and end-to-end traceability.
What changes in operations
The impact is not only about explaining better. It is about operating better.
Speed. Every case receives a decision, recommended action or justified escalation in seconds.
Cost. Teams stop reviewing cases that can be resolved automatically under approved policies and focus on those that truly need judgement.
Fewer operational false positives. Converging controls reduce unnecessary actions on cases that look suspicious in isolation but are consistent with a clean typology and normal network context.
More hidden risk surfaced. ISA and Graph XAI help detect collective patterns, typology drift or anomalous relationships a standalone model may miss.
More defensibility. Every decision embeds verifiable logic, economic argument and traceability for internal or external review.
Any sector with model-driven decisions
The architecture is agnostic to the underlying model. It can run on classifiers, scoring models, risk engines, propensity systems or predictive motors already in production.
Typical use cases:
- Retention and churn: who to contact, which offer, which channel and expected return.
- Fraud and claims: when to auto-settle, request documents, investigate or escalate.
- AML and transactional fraud: prioritise alerts, map networks, justify escalations and cut unnecessary manual review.
- Underwriting and risk: turn scores into consistent, auditable operational lanes.
- Marketing and sales: turn propensity into next best action, not only a customer ranking.
The principle is always the same: turn predictions into concrete, controlled and measurable actions.
What we do not do
- We do not use generative explanations to justify critical decisions.
- We do not replace your model.
- We do not require you to change your current architecture.
- We do not retrain by default.
- We do not modify your core systems.
- We do not automate sensitive decisions without approved policies.
AyGLOO operates as an additional layer on what you already have.
Low-friction adoption
A pilot can run on a real portfolio in four weeks, evaluating past decisions, potential economic impact, automation potential and cases that should be escalated.
Production rollout can be gradual—by segment, typology or decision lane—with a reasonable goal of going live within a quarter.
Adoption is reversible, measurable and compatible with current processes.
Your model will keep predicting. What changes is what happens next. Most companies do not need to start from scratch with models. They need to capture more value from what they already have.
The challenge is no longer only to predict. It is to decide.
A prescriptive, transparent and economically disciplined layer turns each prediction into a justified action—or shows when it should go to human review.
The model predicts; our agents act. Talk to us.