Autonomous AI Agents that learn from every decision.
Deploy self-learning agents that orchestrate complex workflows, adapt strategies in real-time, and inherit platform governance – no manual retraining required.
Built for regulated industries. Pre-built templates for collections, fraud, claims, and underwriting - or build custom agents with no-code tools. Complete audit trails included.
Where traditional automation falls short for regulated industries
Static Playbooks
Traditional "agents" follow pre-programmed rules. When strategies fail, humans manually update logic. No learning. No adaptation. Every change requires development cycles.
Disconnected Intelligence
Automation operates separately from predictive models and GenAI. Teams manually bridge systems, losing context and creating compliance gaps. Value trapped at the insight stage.
Governance Retrofitted
Rule-based automation lacks explainability, audit trails, and approval workflows. Compliance teams reconstruct evidence manually after deployment - or worse, during examinations.
WHY iTURING?
Four capabilities.
One autonomous platform.
Predictive-Powered
Predictive-Powered
Agents execute decisions grounded in real-time intelligence, not guesses.
- Collections agents use default-risk scores to prioritize accounts
- Fraud agents trigger on anomaly detection thresholds
- Growth agents activate based on propensity predictions
- Every action backed by explainable & interpretable model outcomes
Self-Learning
Self-Learning
Agents observe outcomes and self-optimize strategies autonomously.
- Payment success rates train next-contact timing
- False positive rates refine investigation thresholds
- Conversion outcomes retrain offer selection
- Accuracy improvement or maintenance without manual retraining
Multi-Modal Orchestration
Multi-Modal Orchestration
Agents coordinate across channels, tools, and data sources seamlessly.
- Trigger GenAI for personalized messaging
- Call predictive models for real-time scoring
- Execute workflows across SMS, email, IVR, APIs
- Seamless handoff between autonomous and human decisions
Governance-Native
Governance-Native
Agents inherit platform compliance – not retrofitted after deployment.
- Immutable audit trails: every decision logged with rationale
- Maker-checker approvals gate agent deployment
- Explainability: why this action, this channel, this timing
- Continuous monitoring: drift detection, bias alerts, performance degradation
Not standalone automation. Integrated intelligence.
iTuring’s agentic layer connects predictive models and generative AI – feeding from forecasts, triggering personalization, closing the learning loop.
Collections
Orchestration
Agents predict payment propensity, generate personalized messages, and execute multi-channel outreach automatically across SMS, email, and calls.
20%+ recovery lift, 60% cost reduction.
Underwriting
Intelligence
Agents extract data from applications and bank statements, assess credit risk with ML models, then generate complete underwriting memorandums with causation.
40% faster approvals, 25% lower defaults.
Fraud
Investigation
Agents detect anomalies, synthesize evidence from multiple sources, generate investigation summaries, and route cases to investigators with full audit trails.
30-minute investigations vs. 3 weeks.
Claims
Triage
Agents intake FNOL reports, assess severity, classify claim types, predict indemnity costs, and assign to the right adjuster automatically.
60% faster triage, 25% lower LAE.
Enterprise-Grade Agent Infrastructure - Built for scale, security, and continuous compliance.
Agent Development
Agent Development - No-code workflow builder (visual decision trees)
- Pre-built templates (collections, fraud, claims, underwriting, growth)
- Code-first option for custom logic (Python, R)
- Multi-agent coordination with shared context
- Version control with A/B testing
Intelligence Integration
Intelligence Integration - Predictive model APIs (real-time scoring)
- GenAI generation (RAG, fine-tuned LLMs)
- External system APIs (CRM, core banking, payment gateways)
- Multi-modal data ingestion (text, images, PDFs, structured)
- Event-driven triggers (real-time + batch)
Reinforcement Learning
Reinforcement Learning - Multi-armed bandit algorithms for action selection
- Contextual bandits with feature-based policies
- Thompson sampling for exploration-exploitation balance
- Champion-challenger A/B/n testing
- Automatic winner promotion with governance
Deployment & Observability
Deployment & Observability - Single-click deployment (2-8 hours to production)
- Real-time monitoring (success rates, costs, latencies)
- Immutable audit trails with full decision context
- Drift detection (performance degradation, outcome shifts)
- One-click rollback with version history
Built for regulated industries, not generic enterprise.
Capability
Decision Logic
Learning
Personalization
Integration
Governance
Deployment
Compliance
Agentic AI
Predictive models + reinforcement learning
Self-optimizing from outcomes (5-15% quarterly lift)
GenAI market-of-one messaging
Unified with predictive + GenAI layers
Immutable audit trails, maker-checker, explainability
Weeks (pre-built templates)
Examination ready
Traditional Automation
Hard-coded if-then rules
Manual rule updates by humans
Static templates
Standalone scripts, manual integration
Limited logging, manual documentation
Months (custom development)
Manual compliance review required
Frequently Asked Questions
What makes iTuring agents "self-learning"?
Agents observe outcomes (payment success, fraud detection accuracy, conversion rates) and feed that data back to predictive models via reinforcement learning. Models retrain automatically within governed workflows, improving 5-15% per quarter without manual intervention.
Can we build custom agents or only use pre-built templates?
Both. Pre-built templates (collections, fraud, claims, underwriting) deploy in 4-6 weeks. Custom agents built with no-code workflow builder or code-first Python/R deploy in 8-12 weeks. All agents inherit platform governance regardless of how they’re built.
How do agents integrate with our existing systems?
Agents call your CRM, core banking, payment gateways, and communication platforms via APIs. No rip-and-replace. Agents operate as intelligent middleware between predictive models, GenAI, and operational systems.
Can we start small and expand?
Yes. Most clients start with one use case (collections or fraud) to prove ROI, then expand to other workflows. All agents share the same platform infrastructure – no redeployment required.
What's the difference between agentic AI and RPA?
RPA follows static rules and breaks when conditions change. Agentic AI uses predictive intelligence to make decisions, generative AI to personalize actions, and reinforcement learning to improve continuously. Agents adapt without manual reprogramming.
How is agent explainability handled for regulators?
Every agent action logs: (1) Which predictive models triggered it, (2) Feature-level explanations for model scores, (3) Why this action vs. alternatives, (4) Outcome tracking. Complete audit trails ready for examination.
What if an agent makes an error?
All decisions are logged with full context. You can trace errors to root causes, adjust thresholds, or add human-in-the-loop approvals. Maker-checker workflows gate all agent logic changes. One-click rollback restores previous versions.
How long does agent deployment take?
Pre-built templates: 4-6 weeks including data integration and validation. Custom agents: 8-12 weeks. Compare to 6-12 months for traditional automation or RPA implementations.
