Generative AI
Production-grade GenAI without the vendor lock-in.
Fine-tune LLMs, deploy multi-modal RAG, and personalize at scale – on-premise or private cloud with complete observability and zero data leakage.
Built for regulated industries. Deploy generative AI without sending data to third parties. Complete audit trails and explainability included - not bolted on.
Where off-the-shelf GenAI fails for regulated industries
Data Leakage Risk
Generic LLM platforms send your data to vendor clouds. Fine-tuning means exposing customer PII, financial records, and proprietary strategies to third parties.
Bolt-On Governance
Guardrails, hallucination detection, and audit trails are afterthoughts. Compliance teams reconstruct evidence manually after models deploy.
Integration Tax
GenAI operates in silos. No connection to predictive models or autonomous agents. Teams manually bridge systems, losing context and creating compliance gaps.
Three capabilities.
One governed platform.
Fine-Tune
No-code LLM fine-tuning for domain accuracy.
FINE-TUNE
- Supervised fine-tuning (SFT) with labeled datasets
- LoRA (Low-Rank Adaptation) for parameter-efficient tuning
- QLoRA for reduced memory footprint
- On-premise training - your data never leaves your network
- Complete lineage tracking from dataset to deployed model
Multi-Modal RAG
Retrieval augmented generation with complete observability.
Multi-Modal RAG
- Vector database integration with hybrid search (dense + sparse)
- Semantic chunking with overlap strategies
- Multi-vector indexing for cross-modal retrieval
- Contextual compression and re-ranking
- Source attribution for every generated response - critical for audit
Deploy & Monitor
LLMOps with continuous governance, not bolt-on compliance.
DEPLOY & MONITOR
- On-premise or private cloud
- Complete observability: latency, cost per query, hallucination scores
- Retrieval accuracy
- Generation quality
- One-click rollback with version control
Not standalone LLMs. Integrated intelligence.
iTuring’s generative layer sits between predictive models and autonomous agents – feeding context from predictions and triggering agentic workflows.
Collections
Personalization
Behavioral fingerprinting generates market-of-one messaging. No templates and Complete FDCPA compliance with audit trails.
Claims &
Document Analysis
Extract insights from policies, contracts, medical records. Multi-modal RAG with source attribution for every claim.
Fraud Investigation
Summaries
Generate case summaries with complete lineage from raw transaction data to LLM output. Audit-ready evidence in seconds.
Enterprise-Grade LLMOps - Built for scale, security, and continuous compliance.
Data Sovereignty
Data Sovereignty - On-premise deployment (air-gapped if required)
- Private cloud with VPC isolation
- Zero data sent to third-party LLM vendors
- Complete control over model weights and training data
Fine-Tuning at Scale
Fine-Tuning at Scale - No-code interface - no ML engineering required
- LoRA/QLoRA for memory-efficient training
- Supervised fine-tuning with custom datasets
- Domain-specific model variants for financial services
Multi-Modal RAG
Multi-Modal RAG - Vector database: billions of embeddings
- Hybrid search (dense + sparse retrieval)
- Cross-modal indexing (text, images, PDFs)
- Source attribution with explainability
Complete Observability
Complete Observability - Cost per query monitoring
- Hallucination detection scores
- Retrieval accuracy metrics (precision@k, MRR)
- Immutable audit trails for regulatory review
What "governance-as-code" actually means.
Generic platforms offers “bolt-on governance” – guardrails added after models deploy. iTuring engineers compliance into the architecture.
Lineage Tracking
Every GenAI output traces back to:
Lineage Tracking
Every GenAI output traces back to:
- Source documents (RAG retrieval)
- Model version and fine-tuning dataset
- Prompt template and user inputs
- Timestamp and user identity
Maker-Checker Approvals
Electronic workflows gate GenAI deployments:
Maker-Checker Approvals
Electronic workflows gate GenAI deployments:
- Model changes require dual approval
- Fine-tuning datasets reviewed for bias
- Prompt templates validated for compliance
- Rollback authority with audit justification
Explainability
Not just “the model said so”:
Explainability
Not just “the model said so”:
- Source attribution for every RAG response
- Confidence scores with calibration metrics
- Feature importance for fine-tuned outputs
- Hallucination detection with red-flag alerts
Continuous Monitoring
Real-time observability across 60+ parameters:
Continuous Monitoring
Real-time observability across 60+ parameters:
- Data drift detection (schema changes, quality degradation)
- Model drift (confidence distribution shifts)
- Retrieval accuracy degradation
- Cost per query anomalies
Built for regulated industries, not generic enterprise.
Capability
Data Privacy
Governance
Integration
Fine-Tuning
RAG Observability
Deployment
Compliance
Generative AI
On-premise or private cloud. Zero data to vendors.
Native to architecture. Immutable audit trails.
Unified with predictive + agentic layers.
No-code. LoRA/QLoRA. Domain-specific variants.
Source attribution + retrieval accuracy tracking.
2-8 hours. One-click rollback.
Examination ready.
Generic Platforms
Cloud-based. Data sent to third parties for fine-tuning.
Bolt-on. Manual documentation.
Standalone GenAI. Manual integration required.
Code-first. Requires ML engineering.
Basic API metrics.
Weeks. Complex infrastructure management.
Generic enterprise controls.
Frequently Asked Questions
How does on-premise deployment work?
Complete LLM inference and fine-tuning infrastructure deploys in your data center or private cloud (AWS VPC, Azure Private Link, GCP VPC). Your data, model weights, and training datasets never leave your network. Air-gapped deployments supported for maximum security.
What LLMs are supported?
Open-source models (Llama, Mistral, Falcon) and domain-specific variants. Fine-tuned financial services LLMs included. Platform-agnostic architecture supports custom models.
How is multi-modal RAG different from standard GenAI?
Standard GenAI operates on prompts alone. Multi-modal RAG retrieves context from your documents, databases, and knowledge bases – then generates responses grounded in your data with complete source attribution. Critical for regulated industries requiring explainability.
Can GenAI integrate with our existing predictive models?
Yes. iTuring’s generative layer natively integrates with predictive models and autonomous agents on the same platform. Collections agents use risk scores to personalize GenAI messaging. Fraud models trigger GenAI case summaries. No middleware required.
What audit documentation is provided?
Immutable lineage from source data to RAG retrieval to LLM generation and to output. Includes model version, prompt template, retrieval sources, confidence scores, and timestamps. One-click audit reports for regulatory examination.
How fast can we deploy GenAI use cases?
2-8 hours from use case definition to production deployment. No infrastructure provisioning. No ML engineering. Pre-built templates for collections, fraud, claims, and compliance.
