AI & Decision Intelligence
Turn data, intelligence and AI into decisions that move the business. We combine Data Science & AI, Reporting & Analytics, and GenAI/Agentic AI to help enterprises predict what is likely to happen, understand why it matters, decide what to do next, and increasingly automate action, with governance built in.
The model was never the hard part
Most enterprises already have models, dashboards, data and AI experiments.
The harder question is: what happens after the insight?
BigTapp connects intelligence to the decision, the workflow and the action, so predictions become recommendations, recommendations become decisions, and decisions can increasingly trigger governed action. We do this through our proprietary solution platform, Beeonix. Beeonix agents carry the result into your core systems with a human at the approval gate. Every decision is explainable, auditable and measured on cost per decision through our solution accelerator, Cloud Unity.
From a model that predicts to a decision that moves
We pick the decisions worth changing, ground them in your own data and definitions, put them where the work actually happens, then widen what they are allowed to do as the evidence supports it.
AI maturity is not a switch. It is a progression.
BigTapp helps enterprises move from AI that assists people to AI that works alongside them, and ultimately to governed autonomy across repeatable business processes.
AI Assistants
Find. Summarize. Explain.
AI helps employees and customers access information, generate content and answer questions.
Examples:
- Knowledge assistants
- Customer service copilots
- Enterprise search
- Document Q&A
- Analyst assistants
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AI Co-Workers
Understand. Recommend. Collaborate.
AI takes on a persistent role alongside a person, combining context, knowledge and next-best actions.
Examples:
- RM Companion
- Wealth Advisor Co-Worker
- Risk Analyst Co-Pilot
- HR AI Co-Pilot
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Agentic Workflows
Plan. Orchestrate. Execute.
Specialized agents work across systems and tools to complete multi-step processes, with policy controls, approvals and human escalation.
Examples:
- KYC workflows
- Credit review
- Document processing
- Regulatory reporting
- Operations workflows
Autonomous Enterprise
Sense. Decide. Act. Learn.
Bounded, governed AI systems execute repeatable business processes with increasing autonomy while humans retain strategic accountability, exception management and governance.
Everything it takes to turn AI into decisions
The signal layer agents act on
- Predictive & Decision Intelligence: Predict what is likely to happen, understand the drivers, and translate predictions into actionable business decisions across growth, risk and operations.
- Customer & Behaviour Intelligence: Understand customer behaviour, propensity and response to identify the right opportunities, interventions and next-best actions across acquisition, activation, retention and cross-sell.
- Risk & Early-Warning Intelligence: Detect emerging credit, fraud, exposure and customer risks early, enabling proactive intervention and risk-adjusted decision-making.
- AI Lifecycle & Continuous Learning: Operationalise AI from development to production with monitoring, evaluation, drift detection and governed feedback loops, enabling intelligence to improve as conditions and outcomes change.
- Graph & Relationship Intelligence: Reveal hidden relationships, dependencies and network patterns across customers, counterparties and value chains to strengthen risk intelligence and uncover growth opportunities.
- Explainable AI & Responsible AI: Make AI decisions transparent and auditable by surfacing the drivers behind risk, pricing and next-best-action recommendations, enabling informed decisions and stronger governance.
- Agentic Intelligence & Orchestration: Turn intelligence into action through AI co-workers, autonomous agents and multi-agent workflows that reason, use enterprise tools and orchestrate processes within defined controls.
- Decision Model: Pre-built FSI model templates for credit scoring, fraud and underwriting, plus cross-industry domains.
Turn intelligence into action through AI co-workers, autonomous agents and multi-agent workflows that reason, coordinate and execute tasks across enterprise processes, with governance and human oversight built in.
- AI Co-Workers: Give people intelligent digital colleagues that understand their role, bring together enterprise context, and assist across multiple tasks, from client preparation and decision support to follow-up and execution.
- Autonomous Agents: Enable AI to pursue defined goals, reason through tasks, use enterprise tools and complete multi-step activities with minimal human intervention.
- Multi-Agent Orchestration: Coordinate specialised agents across complex business processes, with structured hand-offs, tool access and workflow execution, enabling end-to-end automation with full traceability.
- Knowledge-Grounded Intelligence: Ground every AI decision and action in trusted enterprise data, business context and domain knowledge through the Reference Data Model, Knowledge Graph and Semantic Layer.
- Governed Autonomous Execution: Move from assisted decisions to autonomous action within defined policies, permissions and risk boundaries, with human oversight, evaluation, auditability and full traceability built in.
Where the decision actually commits
- Decision Intelligence: Turn data, models and context into better business decisions.
- Next-Best Action: Recommend the right action, customer, timing and channel.
- Decision Workflows: Embed intelligence into end-to-end business processes.
- Adaptive & Autonomous Decisions: Automate repeatable decisions within defined policies and risk boundaries.
- Continuous Decision Optimisation: Learn from outcomes and continuously improve decision quality and impact.
Insight that arrives, not insight you go and fetch
- Automated Insights: Automatically surface the signals, trends, anomalies and opportunities that matter, without requiring users to search through dashboards and reports.
- Explainable Insights: Go beyond what changed to understand why it changed, with explainable drivers, context and evidence behind every important insight.
- Conversational & Self-Service Intelligence: Let business users explore trusted data, ask questions in natural language and generate insights without depending on specialist analytics teams.
- Proactive Intelligence & Alerts: Deliver the right insight at the right time, proactively alerting users to emerging risks, opportunities and exceptions before they become business problems.
- Insight-to-Action: Connect insights to recommendations, workflows and next-best actions, turning intelligence into measurable business outcomes.
What makes a regulator comfortable
- Responsible AI by Design: Embed responsible AI principles from the start, with controls for explainability, fairness, privacy, human oversight and ethical use throughout the AI lifecycle.
- Policy, Permissions & Guardrails: Control what AI can access, decide and execute through policy-driven permissions, tool gating, data controls and configurable guardrails.
- Human Oversight & Decision Rights: Define where AI recommends, where it acts and where humans approve, override or escalate, with clear accountability.
- Continuous AI Evaluation: Continuously test, evaluate and monitor AI behaviour, model performance, grounding, drift, reliability and business outcomes before and after deployment.
- Auditability & AI Traceability: Maintain an evidence trail of AI decisions, actions, data, policies and human interventions, enabling transparent, explainable and audit-ready AI at scale.
Numbers over narratives.
Real outcomes across banking, financial services, insurance and retail, where AI turns data into better decisions, customer experiences and business outcomes.
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