Build Intelligent Products
Product Teams using Hybrid Intelligence gain direct control and expressive freedom over the AI used for in-product decisions.
Decisions components become meticulously designed material for shaping behaviour, driving outcomes, and aligning stakeholders with clarity and creative authority.
Decision Toolbox
In-Product Decision Design
Bring adaptive intelligence into every decision your product makes – designed for performance, intent, and control.
Explainability and Trust
Make every decision explainable by design – clear to users, defensible to stakeholders, and trusted across teams.
Decision Exploration
See how decisions behave before impact – preview outcomes, test assumptions, and reduce risk.
Adaptability and Control
Keep decision logic aligned as real-world conditions change – without rebuilds, delays, or governance gaps.
Escalation and Safeguards
Embed human judgment where it matters – preserving oversight, selectivity, and control while scaling automation.
Use Hybrid Intelligence instead of
Deterministic Systems (Software)
Defines logic explicitly and freezes it in place. Product teams can inspect what the system does but not easily adapt it. Every change requires structural rework, limiting iteration and creativity.
Probabilistic Models (ML/AI)
Captures patterns from data but conceals reasoning. Product teams inherit outcomes without insight and iterate without control. Change is imprecise, explainability is post-hoc, and system behaviour resists design.
Large Language Models (LLMs)
Generate fluent outputs but lack structured reasoning. Product teams get surface-level articulation without grounded logic or decision traceability. LLMs excel at interpretation and synthesis, but not at precision, alignment or control.
Adapt in-product decisions to real-world differences.
Segment-Responsive Decisioning determines if and how effectively product teams can tailor decisions to maximise outcomes across user segments.
Hybrid Intelligence: Segments are structurally defined and adapt within the model with visible drivers and measurable outcomes.
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Deterministic: Segment logic is hard-coded. Adjustments require custom rules and manual upkeep across edge cases.
Probabilistic: Segments are inferred statistically. Behavioural variation is difficult to trace, control, or act on directly.
LLMs: segments must be inferred from unstructured prompts or external data. Behavioural logic is not embedded or controllable.
“I want my team to design decision components that respond to segment-level behaviour – so we can increase approval rates by 8%, reduce application dropout by 15%, and optimise segment-specific conversion without negatively impacting risk.”
Make explainability a first-class enabler.
Quality of Explanation determines how clearly product teams can communicate decision reasoning.
Hybrid Intelligence: Each decision includes structured attribution and symbolic logic. Explanations are built-in, stable, and ready to share.
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Deterministic: Rules are transparent, but explanations manually reconstructed from fragmented or compound logic.
Probabilistic: Requires post-hoc tools (e.g. SHAP, LIME). Outputs are approximations and may not reflect actual reasoning.
LLMs: Explanations are generated text, not structured reasoning. Outputs vary by prompt and cannot be traced to consistent decision logic.
“I want every AI-enabled decision to carry a stable, legible explanation – so we can demonstrate fairness and traceability on demand during regulatory reviews.”
Make accountability a design principle.
Traceability and attribution determine if and how reliably decision justification can be surfaced and embedded in product and oversight.
Hybrid Intelligence: Decisions are fully justified, with precise, structured attribution embedded at every step.
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Deterministic: Decision provenance must be manually reconstructed from logs or custom instrumentation.
Probabilistic: Decision paths are not inherently traceable. Post-hoc attribution methods are incomplete and unstable.
LLMs: Decision logic is implicit and cannot be traced. Outputs vary by prompt and offer no reliable provenance or attribution.
“I want every decision in the claims process – especially declines and escalations – to be traceable, explainable, and independently justifiable, so my team can operate with confidence and respond to internal and external scrutiny without delay or doubt.”
Continuous, intelligent, light-touch decision alignment.
Self-learning logic empowers product teams to maintain decision performance and realign decision components quickly and confidently.
Hybrid Intelligence: Decision reasoning: tuned, updated, and aligned on the fly, with full visibility, oversight, and safeguards.
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Deterministic: Changes require rule rewrites and code-level updates. Fragile to modify and slow to ship.
Probabilistic: Requires retraining, often with no direct control over decision performance.
LLMs: Model behaviour cannot be aligned or adjusted without retraining. Changes are unpredictable, and oversight is external to the system.
“I want our fraud decision logic to stay aligned with fast-changing threat patterns – so we can adapt quickly, minimise false positives, and maintain effective oversight and control.”
Understand the impact before committing.
Simulation and Scenario Testing extends the decision canvas, enabling product teams to build decisions that anticipate impact and explore alternatives before committing.
Hybrid Intelligence: Simulation is fast, safe, and non-destructive. Impact is measurable and testable before deployment.
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Deterministic: Scenario testing requires duplicated logic and sandbox environments with impact is hard to isolate and validate safely.
Probabilistic: Simulations are difficult without custom tooling. Behavioural outcomes are hard to predict without retraining.
LLMs: Simulations cannot be structured or repeated reliably. Outputs vary with prompts, making impact hard to isolate or validate.
“I want my team to test policy changes thoroughly before deployment – so we can clearly understand the impact and avoid introducing performance or compliance risk.”
Fairness: From a retrofit aspiration to product design principle.
Empirical Fairness Testing defines how clearly product teams can demonstrate and deliver evidenced fairness in every decision.
Hybrid Intelligence: Fairness can be evaluated per segment and per decision with built-in attribution and reasoning. Supports explainable, targeted bias mitigation.
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Deterministic: Fairness checks must be manually engineered. No standard way to surface or assess group-level impacts.
Probabilistic: Fairness is assessed post-hoc. Results are statistical, unstable, and difficult to trace to decision logic.
LLMs: Fairness cannot be systematically tested or attributed. Outputs depend on prompts, not structured reasoning or traceable treatment paths.
“I want real-time evidence that our decision systems treat customers fairly – so we can satisfy Consumer Duty requirements, respond confidently to FCA scrutiny, and build compliance into the product, not around it.”
Stay in sync with the real world.
Drift and Behaviour Monitoring determines if and how effectively product teams can prevent decision performance degradation as real-world conditions or policies change.
Hybrid Intelligence: Drift is visible by segment and rule path. Behavioural changes are observable, attributable, and testable in real time.
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Deterministic: Shifts must be detected manually or through external monitoring. Change is slow to surface and difficult to trace.
Probabilistic: Drift is measured statistically across the model. Segment-level shifts are difficult to isolate or explain.
LLMs: Behavioural drift cannot be tracked structurally. Output patterns vary with prompts, and changes lack attribution or testable logic.
“I want to detect shifts in onboarding and transactional behaviour across customer segments – so we can spot potential compliance risks early, respond precisely, and avoid overwhelming the team with false alerts.”
Compartmentalise and accelerate legacy transformation.
A Modular Decision Architecture determines how effectively product teams can modernise decision components inside legacy environments with selective transformation, controlled rollout, and rapid iteration without systemic disruption.
Hybrid Intelligence: Decision components are structured for independence and collaboration enabling impactful transformation with less entanglement or disruption.
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Deterministic: Logic is interwoven and rigid. Even small changes often require broad rewrites and redeployment.
Probabilistic: Models are monolithic. Improvements often require retraining or re-engineering the whole system.
LLMs: Logic is not modular or addressable. Outputs are emergent, not structured – making safe, component-level evolution infeasible.
“I want to isolate and update affordability calculations to meet new regulatory guidance – so we can respond quickly without triggering full regression testing, delaying delivery, or risking downstream logic failures.”
Make segments first-class decision design objects.
Embedded Segment Definitions determine how effectively product teams can align targeting, fairness, and personalisation through cohorts that are structurally defined, reusable, and governed within the decision system.
Hybrid Intelligence: Segments are structurally embedded and governed. Cohorts are visible, stable, and durable.
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Deterministic: Segments must be hard-coded and manually maintained. Scaling or adjusting across products is slow and error-prone.
Probabilistic: Segments are inferred post-hoc and shift with retraining. Visibility and governance are limited.
LLMs: Segments must be described in prompts or metadata. Cohort logic is not structured, traceable, or persistently usable.
“I want to tailor onboarding flows by segment – like age or employment type – without hardcoding or creating complexity, so we can increase completion rates across underserved cohorts.”
Nudge borderline decisions with precision, confidence, and control.
Local Sensitivity Control determines if and how well product teams can manage decisions near the boundary, enabling targeted adjustments that convert near-misses into measurable gains.
Hybrid Intelligence: Decision boundaries are visible and precise. Teams can adjust outcomes near the edge with full confidence and control.
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Deterministic: Boundaries are hard-coded. Sensitivity must be manually inspected or engineered.
Probabilistic: Margins are unclear. Small input changes cause unpredictable results, making edge cases hard to interpret. Output changes are non-linear and hard to attribute at the edge.
LLMs: Boundaries are not defined or inspectable. Output changes are prompt-sensitive and unpredictable near the decision edge.
“I want my underwriters to see exactly why a borderline application was rejected, so they can make safe, fast overrides without escalation or guesswork.”
Adapt continuously without losing control or oversight.
Risk-Aware Adaptability determines how effectively product teams can improve decision systems without engineering lag or loss of oversight, enabling continuous optimisation with safe, transparent guard rails.
Hybrid Intelligence: Systems adapt within defined constraints. Learning is transparent, controlled, and aligned to intent.
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Deterministic: Systems do not adapt. All changes are manual, slow, and carry deployment risk.
Probabilistic: Models adapt with retraining, often without clear oversight or explainability.
LLMs: Systems evolve unpredictably. Behavioural changes emerge without structure, intent alignment, or operational safeguards.
“I want to continuously refine our collections strategies based on how different customer segments respond – so we can improve repayment outcomes without triggering complaint volumes or breaching regulatory expectations.”
Build automation with human-in-the-loop escalation.
Selective De-Automation enables efficient, confident human-in-the-loop review for high-impact or borderline decisions – without sacrificing speed, scale, or control.
Hybrid Intelligence: Escalation logic is structured, explicit, and configurable. Decisions can route dynamically based on context, risk, and policy alignment.
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Deterministic: Manual review is hardwired and inflexible. Escalation logic is scattered and costly to update.
Probabilistic: Intervention thresholds are opaque. Oversight is ad hoc and triggered after the fact.
LLMs: Decisions cannot be routed for review. Outputs are non-deterministic, and escalation logic must be handled outside the model.
“I want borderline loan rejections to route directly to an underwriter with full reasoning—so we can recover qualified applicants without compromising policy or wasting review capacity.”
Make every decision defensible, reviewable, and audit-ready by design.
Audit-Ready Decision Logging determines how easily product teams can incorporate structured, accessible records of decision logic, attribution, and outcome—enabling audit, compliance, and internal review without additional overhead.
Hybrid Intelligence: Every decision is logged with structured inputs, logic, attribution, and outcome – making it fully traceable and reviewable by design.
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Deterministic: Logging is manual and fragmented. Decisions must be reconstructed from scattered records or code audits.
Probabilistic: Logs may include inputs and outputs, but not logic or attribution. Traceability is incomplete and unstable.
LLMs: Outputs are not decisions. Reasoning is not recorded, and logs lack structure, intent, or auditability.
“I want every decision that affects a customer to carry its own audit trail—so we can demonstrate compliance with Consumer Duty, respond confidently to FCA requests, and prove our decisions are fair, consistent, and aligned to policy.”
Track and version how decision reasoning changes over time.
Explainable Decision Evolution determines how clearly product teams can track how and why in-product decision reasoning has changed over time, enabling version control, rollback, and policy validation.
Hybrid Intelligence: Decision reasoning is versioned, testable, and traceable. Changes are explainable and aligned to intent, policy, and behavioural outcomes.
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Deterministic: Changes are tracked manually or through code comparisons. Impact on behaviour is difficult to assess or explain.
Probabilistic: Model changes are opaque. Behavioural shifts are hard to version, validate, or attribute clearly.
LLMs: Reasoning is not versioned. Output shifts with prompts or tuning, without structured traceability or change control.
“I want to show exactly when and why a decision rule changed—so we can respond quickly to regulators, resolve complaints with confidence, and prove that our changes align with policy intent.”
Enable learning without losing structure, control, or traceability.
Operationally Safe Learning determines how reliably product teams can allow systems to adapt over time, while preserving explainability, governance, and alignment with business intent.
Hybrid Intelligence: Learning occurs within defined boundaries. Adaptation is explainable, reversible, and aligned to operational and policy constraints.
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Deterministic: Learning is not supported. Updates are manual and require full regression testing.
Probabilistic: Models adapt flexibly but opaquely. Behavioural shifts are hard to detect, govern, or reverse.
LLMs: Learning is emergent. Changes stem from prompt tuning or retraining and cannot be governed or traced structurally.
“I want to scale AI-powered decisioning across the organisation—without triggering governance bottlenecks or risking stakeholder trust—so every change remains explainable, testable, and aligned with policy intent.”
Make compliance an in-built feature, not a delivery blocker.
Composable Compliance determines how product teams embed rules, policy, and regulatory constraints directly into decision logic—enabling structured enforcement without manual intervention, delivery delays, or brittle integration.
Hybrid Intelligence: Compliance rules can be embedded directly in the decision component. Enforcement is structured, traceable, and adaptable without slowing delivery.
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Deterministic: Policy logic must be manually coded into systems. Changes require redeployment and are brittle to maintain or scale.
Probabilistic: Compliance is external to the model. Enforcement relies on post-processing, overrides, or manual review.
LLMs: Policy rules must be enforced outside the model. Prompts do not guarantee alignment with regulatory obligations or internal policy.
“I want clients to apply their own compliance logic safely – so we can support variation at scale without adding fragility to our platform.”
Turn decision logic into a measurable driver of business performance.
Traceable KPI Alignment determines how effectively product teams can link decision behaviour to measurable business outcomes, with real-time attribution, faster performance tuning, and clear accountability across teams.
Hybrid Intelligence: Decision behaviour can be robustly linked to KPIs. Attribution and impact are observable, testable, and continuously adjustable.
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Deterministic: KPI impact is hard to isolate. Attribution requires duplicated environments and manual validation.
Probabilistic: Models optimise for statistical accuracy, not business KPIs. Performance linkage is opaque and fragile.
LLMs: KPI alignment is not native. Outputs are non-deterministic, and performance attribution is prompt-dependent and non-repeatable.
“I want to trace how pricing logic affects acquisition, retention, and margin – so we can tune our strategy by cohort and optimise for long-term value, not just short-term wins.”
Test the impact of small changes, without disrupting logic or flow.
What-If Testing determines how easily teams can experiment with realistic input changes to observe downstream behaviour, assess impact, and validate logic under operational conditions.
Hybrid Intelligence: Supports controlled input variation with traceable logic response. Behavioural impact is observable and safe to test.
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Deterministic: Requires manual test cases and duplicated rules. Difficult to isolate cause and effect.
Probabilistic: Requires retraining or surrogate models to simulate effects. Impact analysis is brittle.
LLMs: Outputs change with prompt rewording. No structured way to test controlled input changes.
“I want to understand how adjusting income by £1,000 affects approval probability – so we can explain outcomes and refine thresholds confidently.”
Find the smallest changes that would change the decision.
Counterfactual Evaluation determines how precisely teams can identify the minimum change needed to shift an outcome—supporting appeals, overrides, and fairness analysis.
Hybrid Intelligence: Counterfactuals are natively supported. Smallest actionable input shifts are visible, explainable, and testable.
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Deterministic: Requires full logic review or brute-force testing. Counterfactuals are not native.
Probabilistic: Counterfactuals are inferred using approximators. Validity is uncertain and hard to explain.
LLMs: No fixed boundaries. Decisions are generated, not structured, making counterfactuals undefined.
“I want to show declined applicants exactly what would’ve changed the outcome – so we can recover borderline cases and demonstrate transparency.”
Test how decisions behave when key features are removed.
What-If-Not Analysis determines how teams can isolate the influence of individual variables on outcomes, helping to evaluate logic dependencies, feature importance, and bias.
Hybrid Intelligence: Features can be excluded directly. Logic dependencies and behavioural shifts are traceable and explainable.
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Deterministic: Removing inputs requires rule rewrites and manual refactoring. Difficult to isolate cleanly.
Probabilistic: Feature attribution is post-hoc and indirect. Model dependencies are difficult to remove and observe.
LLMs: Feature influence is embedded in prompt structure. Removal is not measurable or testable.
“I want to know how much our decisions rely on employment status – so we can test for fairness and remove unnecessary dependency.”
