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Welcome to Trust3 AI

Trust3 AI is the only platform purpose-built to increase reliability, security and adoption of Generative AI in the enterprises.

Trust3 IQ

Trust3 IQ, the flagship offering from Trust3 AI, is the only AI-native, universal context engine that enables users to easily create, manage and connect business context with their AI Agents and assistants, boosting AI accuracy and reliability.

IQ connects with a variety of data sources, such as relational data stores and analytical data warehouses to unify metadata, business semantics, metrics, relationships, and governance rules into a continuously updated model of business context. AI agents can then use IQ via APIs and Model Context Protocol (MCP) to retrieve relevant data and context, enabling them to deliver accurate, business-aware responses based on this unified enterprise context.

  • AI-Native Context

    Unify context from multiple data sources to plug into AI assistants and agents.

  • Universal & Scalable Context

    Efficiently manage and organize your data context for optimal performance.

  • Trusted & Governed

    Efficiently manage and organize your data context for optimal performance.

  • Adaptive Intelligence

    Continuously adapts and learns with feedback from experts and usage patterns.

Challenges with AI in the Enterprise

Enterprises are rapidly accelerating the adoption of AI agents and AI tools in their workflows, however, accuracy and reliability of these agents still remains elusive.

Today, teams spend endless cycles of prompting & re-prompting these AI agents to manually stitch together the context required for these AI agents to be relevant and reliable.

Manual & Inefficient

Information Silos: Technical, analytics, and business teams own and maintain discrete pieces of information such as data models, catalogs, and knowledge resulting in fragmented context that's unusable by AI.

Trapped Knowledge: Often times the knowledge and rules about using the data is trapped with subject matter experts and in legacy systems, making it unusable by AI systems.

Semantic Inconsistencies: Much of the business semantics are locked inside fragmented tools within the enterprise with no singular definition of business terms. This leads to inconsistencies and conflicting results when connected to AI.

Governance Challenges: Unifying business semantics and data to create a universal context poses governance and compliance risks. Teams need to ensure that only right people have access to the right data and also make them auditable.

AI-Native Universal Context Engine

IQ solves these challenges by automatically connecting, unifying, and governing your enterprise context. Instead of manually stitching together fragmented information, IQ builds a continuously updated semantic graph that serves as the universal source of truth for your AI applications. Furthermore, unlike traditional approaches that send this whole context to AI, IQ retrieves only the relevant context to send to AI systems, ensuring that context space is used optimally.

  1. AI-powered Enterprise Context

    IQ ingests metadata from various data sources to create a unified enterprise context consisting of business concepts (eg: patient, claim, order), semantics, metrics (eg: revenue, amount, loss ratio), entities (instances of business concepts), entity relationships and governance rules(eg: records cannot be viewed by non citizens). This eliminates fragmentation and makes context readily available to any AI applications across the enterprise.

  2. Connect IQ with AI

    IQ supports Model Context Protocol (MCP) that allows you to connect this enterprise context with any AI agents and AI assistants as a tool. You can connect IQ with popular AI assistants like Claude, ChatGPT, or custom agents built using agentic platforms such as LangGraph, CrewAI, and n8n.

  3. Natural language to SQL

    Ask IQ using natural language and IQ effectively converts the natural language to extract the intent and meaning. IQ then retrieves the appropriate technical and business metadata context to generate the most accurate SQL optimized for the platform.

  4. Manage Context

    1. Automatic Updates: When metadata changes in your connected data sources, IQ automatically detects and updates the context to reflect new schemas, tables, columns, and relationships, ensuring your context stays current.

    2. Continuous Improvement: IQ maintains context accuracy through human-assisted feedback loops where Subject Matter Experts review and validate generated context, and by analyzing audit logs and query history to refine semantics and entity relationships based on real-world usage patterns.

Benefits

  • Build Accurate & Reliable AI: By providing AI applications with unified, business-aware context that includes proper semantics, relationships, and governance rules, IQ ensures that AI agents deliver grounded, accurate responses based on your actual data. This eliminates hallucinations and inconsistent results, enabling you to deploy AI applications with confidence knowing they understand your business the way your teams do.

  • Platform-Agnostic, Reusable Context: With MCP protocol support, connect with any AI agent or assistants such as Claude, ChatGPT or with your custom agents built using agentic platforms such as Langgraph, CrewAI, n8n and others. Build once and reuse across multiple domains and data sources.

  • Semantic Consistency: IQ creates a unified semantic model to ensure singular definitions of your business terms. By establishing a centralized ontology, IQ eliminates inconsistencies and conflicting results, ensuring that "revenue" means the same thing whether accessed from your CRM, data warehouse, or analytics platform.

  • Governed Access: IQ provides built-in governance and security controls that ensure the right people have access to the right data. With fine-grained access policies, audit trails, and compliance capabilities, teams can confidently expose unified context to AI applications while maintaining proper data governance and regulatory compliance.

Who is it for?

Trust3 IQ is designed for enterprises and teams that leverage AI but are struggling to improve accuracy, governance, and scalability of their AI use cases. IQ addresses the needs of diverse stakeholders across organizations:

AI Engineers and Developers

AI engineers and ML practitioners building AI agents, chatbots, and intelligent applications. IQ provides the enterprise context needed to make these AI applications accurate, business-aware, and compliant. With MCP protocol support, teams can easily integrate IQ's unified context into any AI framework, agent platform, or LLM-based application.

Organizations Adopting AI at Scale

Enterprises that are deploying multiple AI assistants and agents across departments but struggling with accuracy and consistency. IQ eliminates the fragmentation that plagues AI adoption by providing a single source of truth that can be reused across all AI applications, reducing time-to-market and increasing trust in AI outputs.

Data & Analytics Teams

Data engineers, data analysts, and data scientists who spend significant time understanding data relationships, profiling data quality, and managing semantic inconsistencies. IQ automates the discovery of table relationships, uncovers undocumented dependencies, and creates unified semantic models, freeing up time for higher-value analytical work while ensuring data integrity across the organization.

Industry-Specific Teams

Pharmaceutical and life sciences companies managing clinical trials, or sales teams looking to use AI to accelerate revenue operations, or other regulated industries that need to balance AI innovation with strict compliance requirements. IQ's hybrid deployment options support organizations with data residency constraints or air-gapped environments.

Use Cases

IQ allows you to interact with your enterprise data sources using natural language, extending the fit to a wide variety of use cases that require accessing your data & enterprise context without the overhead of translating the semantics to make sense of the data.

As demand grows across every department to "talk to the data," organizations are launching numerous AI chatbots and assistants that struggle with accuracy due to knowledge fragmentation and lack of enterprise context. Trust3 IQ eliminates this fragmentation by creating a unified semantic layer of enterprise context that can be instantly connected to any AI assistant or productivity tool. Define context once in IQ and reuse this across assistants for Strategy, FP&A, Operations, and more. Each assistant then operates with accurate, governed, and business-aware intelligence, enabling grounded conversations and faster, more reliable decisions.

Sales reps spend disproportionate time chasing low-priority or stalled opportunities due to lack of timely access to historical patterns or buying signals and knowledge about sales strategies in similar accounts. With AI Agents and the enterprise context, you can make this information readily available and bring it right inside the rep workflows with recommended actions & nudges.

Delays in patient enrollment, caused by time-consuming traditional methods and retrospective reporting, hinder drug development and regulatory milestones for pharma and life sciences companies. AI Agents powered by Trust3 IQ can provide context-aware recommendations by orchestrating data and semantics across varied data and knowledge sources and help clinical operations teams to take actions to notify site managers, coordinators, and study leads with proactive, data-driven insights.

Understanding database table relationships is crucial for data quality, impact analysis, and migrations, but these connections are often hidden, making manual discovery slow and error-prone. Trust3 IQ can automate this process by analyzing a database schema and query logs to identify both direct (e.g., foreign key) and indirect (e.g., inferred or "soft") dependencies across and within tables. Using statistical and pattern-based methods, IQ can uncover undocumented relationships, visualize dependency networks, and highlight critical or orphaned tables. This enables data engineering, governance, and analytics teams to quickly assess downstream impacts and strengthen overall data integrity.

Next Steps