Products
Products
Products
Explainable AI for Smarter Compliance
Compliance teams are drowning in alerts and blind spots. Netra brings context, speed, and explainability — finally, clarity that scales.
From Data Overload to Explainable Intelligence
Netra’s reasoning agents analyze, connect, and explain — delivering clarity where black-box automation falls short.
Discover hidden relationships, real-time context, and the why behind every risk signal.
From Manual Checks to Reasoning Engines
Replace static workflows with dynamic AI that reasons across entities, ownership, and disclosures — turning fragmented data into explainable context.
Each Netra agent operates with full traceability — every decision is logged, sourced, and auditable.
From Documents to Defensible Decisions
AI-powered OCR and graph reasoning transform unstructured reports into explainable insights — connecting the dots others miss.
SStart investigating.
White Paper
White Paper
White Paper
Transform Compliance from Reactive to Predictive
Netra integrates seamlessly into existing systems, enhancing onboarding, monitoring, and investigations with real-time, explainable intelligence.
Why Netra Leads in Explainable AI for Compliance
Our reasoning-first architecture connects graph data, LLM context, and audit trails
— delivering decisions that are both fast and defensible.
Solution
Netra
Quantexa
Ascent RegTech
WorkFusion
SAS AML
SymphonyAI Sensa
Lucinity
Fenergo CLM
ComplyAdvantage
Trunarrative (LexisNexis)
Hawk AI
Entity Expansion
✓ Contextual NLP
✓ Contextual entity resolution
✓ Regulatory ontology links
◑ Pre-trained models
◑ Rules-based
◑ Risk indicators only
◑ Behavioral entities
◑ Entity profiles
◑ Keyword/entity match
✗ Absent
✗ Absent
Graph Reasoning
✓ Multi-hop analysis
✓ Relationship graphs
◑ Rule graph only
◑ Rules engine
◑ Network graph for risk
✗ Absent
✗ Absent
◑ Risk graph, limited reasoning
✗ No graph reasoning
✗ Absent
✗ Absent
Workflow Assistance
✓ Proactive suggestions
◑ Human-in-loop workflow
◑ Workflow triggers
✓ Workflow automation
✓ Rule-driven alerts
✓ Onboarding workflows
✓ Anomaly-driven triggers
✓ Case collaboration UI
◑ Case management workflows
✓ Case + review workflows
✓ Alert triage workflows
Solution
Netra
Quantexa
Ascent RegTech
WorkFusion
SAS AML
SymphonyAI Sensa
Lucinity
Fenergo CLM
ComplyAdvantage
Trunarrative
Hawk AI
Entity Expansion
✓ Contextual NLP
✓ Contextual resolution
✓ Regulatory ontology links
◑ Pre-trained models
◑ Rules-based
◑ Risk indicators only
◑ Behavioral entities
◑ Entity profiles
◑ Keyword/entity match
✗ Absent
✗ Absent
Graph Reasoning
✓ Multi-hop analysis
✓ Relationship graphs
◑ Rule graph only
◑ Rules engine
⚠ Graph for risk
✗ Absent
✗ Absent
⚠ Limited reasoning
✗ No graph reasoning
✗ Absent
✗ Absent
Workflow Assistance
✓ Proactive suggestions
◑ Human-in-loop workflow
◑ Workflow triggers
✓ Workflow automation
✓ Rule-driven alerts
✓ Onboarding workflows
✓ Anomaly-driven triggers
✓ Case collaboration UI
⚠ Case management
✓ Case + review workflows
✓ Alert triage workflows
Explainable Reasoning, Real-Time Context, Audit-Ready Design
What Makes Netra Different?
What features and capabilities are important in Netra's RIA?
1. Smart Entity Resolution
Connect people, companies, and ownership with relational precision.
2. Network Risk Detection
Reveal hidden ties across jurisdictions and time.
3. Adaptive Fraud Analytics
identify behavioral anomalies with contextual reasoning.
4. Real-Time Explainability
Every output includes the “why” and the evidence behind it.
How Netra Helps
Your Team
Analysts get speed. Compliance officers get trust. Executives get foresight. Netra makes every investigation traceable — and every decision defensible.
Find quick answers on integrations, security, and compliance readiness.
1. How is the risk score determined, and can it be customised?
Netra’s risk scores are generated through an explainable reasoning engine that combines graph analytics, agentic AI, and rule-based logic. Each score is fully traceable — showing why a decision was made, which data points influenced it, and how the weighting was applied. The framework can be customised to align with your organisation’s internal policies, risk appetite, and regulatory requirements — ensuring transparency without sacrificing flexibility.
2. What data sources does the platform use for screening and information gathering?
Netra connects to over 200 global data providers, combining purchased, public, and proprietary sources. Notable providers include Dun & Bradstreet (corporate data) and Creditreform (financial data). The platform supports multilingual processing (including Arabic, Russian, and Chinese) and can extract information from client websites. It also incorporates client-supplied data via questionnaires or uploaded documents. To ensure accuracy, Netra cross-verifies findings across multiple sources to reduce false positives.
3. What is the value of the visual knowledge graph (ownership tree)?
The visual knowledge graph turns complex ownership data into explainable context. It shows not just who is connected, but how and why — revealing the relationships, timelines, and entities that influence a risk decision. Each node in the graph links back to verified sources, allowing analysts and auditors to trace every conclusion with one click. In short, it transforms compliance from static reports into living, transparent intelligence.
4. Can the platform screen against internal or client-specific lists?
The visual knowledge graph offers an intuitive map of ownership and relational links for the entity under review. It displays layered connections (e.g., shareholders, directors, affiliates) across levels of ownership or influence. Yes. Netra is fully customisable, allowing clients to upload and integrate internal lists—such as blacklists, watchlists, or competitor databases—via bulk uploads (e.g., Excel). These custom datasets are automatically included in risk reviews and screening processes, enabling compliance with internal policies and operational needs.
5. Is the platform API-enabled for integration with other systems?
Absolutely. Netra provides API access to retrieve risk scores, reports, and data points. This enables seamless integration with internal tools such as Customer Lifecycle Management (CLM) platforms, allowing users to access Netra's functionality without leaving their own workflows.
6. How does the platform use Artificial Intelligence (AI) and Large Language Models (LLMs)?
Netra is exploring AI and LLMs to enhance user experience and efficiency. Applications include: Summarising key findings from gathered data Auto-filling client profiles or questionnaires based on available inputs AI assistants (Agent AI) to guide compliance officers, search documents, or extract insights Smart document review, with AI highlighting relevant sections or extracting structured data The platform is evolving in this area, with users showing varied interest in the depth of AI integration within compliance workflows.
1. How is the risk score determined, and can it be customised?
Netra’s risk scores are generated through an explainable reasoning engine that combines graph analytics, agentic AI, and rule-based logic. Each score is fully traceable — showing why a decision was made, which data points influenced it, and how the weighting was applied. The framework can be customised to align with your organisation’s internal policies, risk appetite, and regulatory requirements — ensuring transparency without sacrificing flexibility.
2. What data sources does the platform use for screening and information gathering?
Netra connects to over 200 global data providers, combining purchased, public, and proprietary sources. Notable providers include Dun & Bradstreet (corporate data) and Creditreform (financial data). The platform supports multilingual processing (including Arabic, Russian, and Chinese) and can extract information from client websites. It also incorporates client-supplied data via questionnaires or uploaded documents. To ensure accuracy, Netra cross-verifies findings across multiple sources to reduce false positives.
3. What is the value of the visual knowledge graph (ownership tree)?
The visual knowledge graph turns complex ownership data into explainable context. It shows not just who is connected, but how and why — revealing the relationships, timelines, and entities that influence a risk decision. Each node in the graph links back to verified sources, allowing analysts and auditors to trace every conclusion with one click. In short, it transforms compliance from static reports into living, transparent intelligence.
4. Can the platform screen against internal or client-specific lists?
The visual knowledge graph offers an intuitive map of ownership and relational links for the entity under review. It displays layered connections (e.g., shareholders, directors, affiliates) across levels of ownership or influence. Yes. Netra is fully customisable, allowing clients to upload and integrate internal lists—such as blacklists, watchlists, or competitor databases—via bulk uploads (e.g., Excel). These custom datasets are automatically included in risk reviews and screening processes, enabling compliance with internal policies and operational needs.
5. Is the platform API-enabled for integration with other systems?
Absolutely. Netra provides API access to retrieve risk scores, reports, and data points. This enables seamless integration with internal tools such as Customer Lifecycle Management (CLM) platforms, allowing users to access Netra's functionality without leaving their own workflows.
6. How does the platform use Artificial Intelligence (AI) and Large Language Models (LLMs)?
Netra is exploring AI and LLMs to enhance user experience and efficiency. Applications include: Summarising key findings from gathered data Auto-filling client profiles or questionnaires based on available inputs AI assistants (Agent AI) to guide compliance officers, search documents, or extract insights Smart document review, with AI highlighting relevant sections or extracting structured data The platform is evolving in this area, with users showing varied interest in the depth of AI integration within compliance workflows.
1. How is the risk score determined, and can it be customised?
Netra’s risk scores are generated through an explainable reasoning engine that combines graph analytics, agentic AI, and rule-based logic. Each score is fully traceable — showing why a decision was made, which data points influenced it, and how the weighting was applied. The framework can be customised to align with your organisation’s internal policies, risk appetite, and regulatory requirements — ensuring transparency without sacrificing flexibility.
2. What data sources does the platform use for screening and information gathering?
Netra connects to over 200 global data providers, combining purchased, public, and proprietary sources. Notable providers include Dun & Bradstreet (corporate data) and Creditreform (financial data). The platform supports multilingual processing (including Arabic, Russian, and Chinese) and can extract information from client websites. It also incorporates client-supplied data via questionnaires or uploaded documents. To ensure accuracy, Netra cross-verifies findings across multiple sources to reduce false positives.
3. What is the value of the visual knowledge graph (ownership tree)?
The visual knowledge graph turns complex ownership data into explainable context. It shows not just who is connected, but how and why — revealing the relationships, timelines, and entities that influence a risk decision. Each node in the graph links back to verified sources, allowing analysts and auditors to trace every conclusion with one click. In short, it transforms compliance from static reports into living, transparent intelligence.
4. Can the platform screen against internal or client-specific lists?
The visual knowledge graph offers an intuitive map of ownership and relational links for the entity under review. It displays layered connections (e.g., shareholders, directors, affiliates) across levels of ownership or influence. Yes. Netra is fully customisable, allowing clients to upload and integrate internal lists—such as blacklists, watchlists, or competitor databases—via bulk uploads (e.g., Excel). These custom datasets are automatically included in risk reviews and screening processes, enabling compliance with internal policies and operational needs.
5. Is the platform API-enabled for integration with other systems?
Absolutely. Netra provides API access to retrieve risk scores, reports, and data points. This enables seamless integration with internal tools such as Customer Lifecycle Management (CLM) platforms, allowing users to access Netra's functionality without leaving their own workflows.
6. How does the platform use Artificial Intelligence (AI) and Large Language Models (LLMs)?
Netra is exploring AI and LLMs to enhance user experience and efficiency. Applications include: Summarising key findings from gathered data Auto-filling client profiles or questionnaires based on available inputs AI assistants (Agent AI) to guide compliance officers, search documents, or extract insights Smart document review, with AI highlighting relevant sections or extracting structured data The platform is evolving in this area, with users showing varied interest in the depth of AI integration within compliance workflows.
1. How is the risk score determined, and can it be customised?
Netra’s risk scores are generated through an explainable reasoning engine that combines graph analytics, agentic AI, and rule-based logic. Each score is fully traceable — showing why a decision was made, which data points influenced it, and how the weighting was applied. The framework can be customised to align with your organisation’s internal policies, risk appetite, and regulatory requirements — ensuring transparency without sacrificing flexibility.
2. What data sources does the platform use for screening and information gathering?
Netra connects to over 200 global data providers, combining purchased, public, and proprietary sources. Notable providers include Dun & Bradstreet (corporate data) and Creditreform (financial data). The platform supports multilingual processing (including Arabic, Russian, and Chinese) and can extract information from client websites. It also incorporates client-supplied data via questionnaires or uploaded documents. To ensure accuracy, Netra cross-verifies findings across multiple sources to reduce false positives.
3. What is the value of the visual knowledge graph (ownership tree)?
The visual knowledge graph turns complex ownership data into explainable context. It shows not just who is connected, but how and why — revealing the relationships, timelines, and entities that influence a risk decision. Each node in the graph links back to verified sources, allowing analysts and auditors to trace every conclusion with one click. In short, it transforms compliance from static reports into living, transparent intelligence.
4. Can the platform screen against internal or client-specific lists?
The visual knowledge graph offers an intuitive map of ownership and relational links for the entity under review. It displays layered connections (e.g., shareholders, directors, affiliates) across levels of ownership or influence. Yes. Netra is fully customisable, allowing clients to upload and integrate internal lists—such as blacklists, watchlists, or competitor databases—via bulk uploads (e.g., Excel). These custom datasets are automatically included in risk reviews and screening processes, enabling compliance with internal policies and operational needs.
5. Is the platform API-enabled for integration with other systems?
Absolutely. Netra provides API access to retrieve risk scores, reports, and data points. This enables seamless integration with internal tools such as Customer Lifecycle Management (CLM) platforms, allowing users to access Netra's functionality without leaving their own workflows.
6. How does the platform use Artificial Intelligence (AI) and Large Language Models (LLMs)?
Netra is exploring AI and LLMs to enhance user experience and efficiency. Applications include: Summarising key findings from gathered data Auto-filling client profiles or questionnaires based on available inputs AI assistants (Agent AI) to guide compliance officers, search documents, or extract insights Smart document review, with AI highlighting relevant sections or extracting structured data The platform is evolving in this area, with users showing varied interest in the depth of AI integration within compliance workflows.