Data Layer for Enterprise AI

Nexla uses a zero-trust identity model designed for MCP. When an AI agent makes a request, identity flows from the MCP client through the gateway to the connector: the user’s auth key resolves to a source-level credential, and data source policies are enforced at the origin – not just at the API boundary. This means every data access is authenticated and authorized at the source, with full audit trails, regardless of which agent or application made the request.

Nexla’s MCP Gateway uses enterprise context, including user role, current task, and access permissions, to dynamically assemble the right set of tools for each agent request. Rather than exposing every available tool at once, the Gateway’s Context Engine, Tool Router, and Policy Check components work together to ensure agents see only the tools they’re authorized to use and that are relevant to the task at hand. This reduces agent confusion, improves accuracy, and enforces data governance automatically.

The Agentic Probe is an AI-driven data discovery engine that autonomously explores your connected data sources – scanning databases, file systems, and APIs, to identify data entities that could become valuable MCP tools for your agents. Rather than requiring you to build a pipeline before you know if data is worth connecting, the Probe evaluates business relevance and tool candidacy first. It surfaces a shortlist of high-value candidates, ready to be converted into governed Nexsets, without writing any integration code.

Nexla is purpose-built for AI agents, not just analytics dashboards. Traditional platforms (Informatica, Fivetran) were designed for batch analytics. Nexla delivers semantic intelligence, real-time (<5 min), agent-native protocols (MCP), and natural language interface (Express.dev). Result: Deploy in days, not months.

Hallucinations happen with incomplete context. Nexla’s Nexsets include semantic metadata (agents understand “customer” across systems), quality validation, business context, and lineage tracking. Customer example: 95% reduction in claims processing errors.

Conversational data engineering platform for data pipelines. Describe what you need in plain English, Express builds it. Example: “Connect Salesforce to Snowflake, sync accounts daily” → pipeline generated in 3 minutes vs 3 weeks traditional. Try it free at express.dev

• POC: Minutes (Express.dev self-service) to 2-5 days (guided)
• Production: 1-2 weeks (simple), 4-8 weeks (complex enterprise)
• Partner onboarding: 3-5 days vs 6 months traditional Why faster: 600+ pre-built connectors, no-code interface, built-in compliance

Yes. SOC 2 Type II, HIPAA, GDPR, CCPA compliant. Features: End-to-end encryption, RBAC, data masking, audit trails, local processing option, secrets management. Trusted by healthcare, financial services, insurance, government. Learn more: nexla.com/security

A focused team can build a few connectors and one MCP server. The real cost is everything after the prototype: schema evolution, rate limit handling, retry logic, RBAC, audit logs, and error handling. Industry estimates put time to feature parity at 18 to 24 months minimum. Every engineer building connectivity infrastructure is not building what differentiates your business. Nexla ships 600+ connectors, governed MCP via MCP Studio, and Nexsets in production from day one.