Multi-Agentic Native AI Platform
DQLabs Prizm is a comprehensive, AI-native, multi-agentic platform designed to transform how organizations manage data quality, cataloging, and observability. By leveraging a multi-agentic approach, Prizm provides enterprises with powerful tools to continuously understand data, evaluates its trustworthiness, and operates across the enterprise — unifying data quality, observability, cataloging, and governance into a single control plane.
Platform Pillars
Data Cataloging
Automatically discover, classify, and enrich data assets with metadata, business context, and lineage across all your sources.
Data Quality
Continuously monitor and enforce quality standards using adaptive AI-driven rules, profiling, and anomaly detection.
Data Observability
End-to-end visibility into pipeline health, data freshness, volume, and schema changes with intelligent alerting.
Core AI-native functions
1. Multi-Agent Architecture
1. Multi-Agent Architecture
Prizm is built around autonomous, role-driven agents that continuously profile, prioritize, analyze, and remediate data issues — reducing manual intervention and enabling scalable data trust. This is the heart of what makes Prizm truly AI-native rather than just AI-assisted. See Multi-Agent Architecture for the full technical breakdown.
2. Unified context across quality, observability & lineage
2. Unified context across quality, observability & lineage
Prizm connects observability signals, data quality metrics, lineage, usage, and business context into a single control plane — ensuring issues are understood in terms of their broader impact, not just as isolated anomalies.
3. Criticality-driven prioritization
3. Criticality-driven prioritization
Prizm automatically identifies and prioritizes business-critical data assets, focusing monitoring depth and remediation effort where impact is highest — rather than treating all data equally. This is a significant shift from traditional rule-based quality tools.
4. AI-ready data at scale
4. AI-ready data at scale
Prizm continuously evaluates data fitness for analytics, ML, and GenAI use cases — helping organizations scale AI initiatives with confidence, accountability, and reduced operational risk.
5. Continuous learning from context
5. Continuous learning from context
Built with an agentic core, Prizm learns from metadata, lineage, usage patterns, and outcomes to prioritize what matters, detect issues early, and orchestrate resolution with minimal human intervention — while keeping humans in control through AI Stewardship.
AI-native vs. agentic: two complementary layers
Prizm’s capabilities split cleanly into two categories that work together:
AI-native is the foundation — how the platform is built and what it understands. Agentic is the execution layer — what it does on its own. Prizm is one of the few platforms that genuinely combines both, rather than layering AI features on top of a legacy rules engine.
Core capabilities
Intelligent data cataloging
- Automatically discovers and classifies data assets across your organization
- Creates rich metadata with domain-specific business context and lineage information
- Enables natural language search and exploration of data assets
- Facilitates data democratization while maintaining security
Real-time data observability
- End-to-end monitoring of data pipelines and workflows
- Real-time alerting on data quality and system performance issues
- Comprehensive dashboards with actionable insights
- Predictive analytics to forecast potential data problems
Data quality management
- Automatically detects anomalies, duplicates, and inconsistencies
- Implements smart data validation rules that evolve with your data
- Provides root cause analysis for quality issues
- Recommends and executes data cleansing operations
Architecture at a glance
Prizm is a cloud-native, containerized platform designed to integrate with existing enterprise data ecosystems, supporting deployment in public cloud, private cloud, on-premises, or hybrid environments.Deployment models
- SaaS (Prizm-managed) — runs in Prizm-controlled cloud accounts, with secure networking options for customer connectivity
- Customer-managed (VPC/VNet/on-prem) — runs entirely within the customer’s environment for maximum control
- Hybrid — control plane and UI centralized, while the execution plane runs closer to customer data
Security foundations
Prizm follows defense-in-depth principles: least privilege for human and machine identities, separation of duties for administrative actions, and continuous verification (zero trust) for every request.- Identity & access: SAML 2.0 / OpenID Connect SSO, optional LDAP, RBAC with optional ABAC tag-based constraints
- Data protection: TLS 1.2+ in transit, AES-256 at rest, vault/KMS-backed secrets management
- Auditing: authentication events, authorization decisions, administrative and configuration changes, and data access actions are all logged with actor, action, target, and outcome
- Compliance mapping: supports SOC 2 / ISO 27001 and GDPR/CCPA evidence requirements through access control, change management, and audit logging
Key benefits
For Data Teams
Reduce manual data observability and quality tasks by up to 80%, gain end-to-end visibility into assets and lineage, and scale governance practices efficiently
For Business Users
Access trusted, high-quality data for decision-making and find relevant assets quickly through natural language search
For Organizations
Increase ROI from data investments, reduce risk from poor data quality, and accelerate time-to-insight across data initiatives
Explore the platform
Multi-Agent Architecture
How Prizm’s specialized agents collaborate to manage data quality and observability
AI Stewardship
How Prizm keeps humans in control of autonomous AI actions
Autonomous Intelligence
The five-layer architecture behind Prizm’s self-driving data management
Converse — AI Chat Interface
Prizm’s natural-language interface for querying, configuring, and acting on your data