Introducing 
Express — Go from prompt to pipeline in minutes.
Enterprise AI doesn’t stall because models are insufficient, rather because getting the right data, with the right context, across fragmented and enterprise systems, remains a months-long engineering problem. Nexla’s MCP architecture is built to solve exactly that.
Most enterprises today have invested heavily in AI models: foundation models, fine-tuned LLMs, and AI-powered products. Yet most AI initiatives fail to reach production at scale. The reason is almost always the same – data isn’t accessible in the way the AI needs it.
Enterprise data lives across hundreds of systems, from CRM platforms, ERP systems, databases, SaaS tools to document and videos, and in enterprise applications that were never designed to communicate with each other. Each of these systems hold critical context that AI agents need to act. Today, connecting each one requires custom integration work, fragile pipelines, and months of engineering time.
MCP (Model Context Protocol) has emerged as the standard interface for connecting AI agents to data. But most MCP implementations are shallow: they expose a single system’s API, lack enterprise context, and fall apart the moment an agent needs to span multiple systems in a single workflow.
Nexla’s approach is different, and for enterprises dealing with data fragmentation and older, mission-critical infrastructure, it is the only architecture that addresses the problem at its root.
Nexla’s MCP architecture addresses enterprise data connectivity across three distinct dimensions. Each solves a different layer of the problem and helps form the complete context-delivery system available for enterprise AI.
Connect any SaaS app, database, or API to your AI agents in minutes, not weeks. Nexla provides 550+ pre-built bidirectional connectors and our AI-powered builder does the rest. No custom code required.
Generic vendor MCPs expose limited data via APIs. Nexla enriches that with enterprise-specific context: internal docs, custom processes, business logic, and semantic metadata that makes AI responses genuinely useful.
Most enterprise workflows span multiple systems. Nexla’s cross-system MCP handles complete end-to-end workflows in a single call, eliminating the context bloat, latency, and orchestration errors that come from chaining multiple MCPs.
For organizations running on SAP, mainframe databases, homegrown ERP systems, or on-premises applications built decades ago, the AI opportunity has felt distant. These systems were never designed with APIs in mind, let alone AI agents. The conventional wisdom has been that AI transformation requires a multi-year transformation effort, first.
Nexla’s MCP architecture now makes AI a reality for these enterprises, without needing to re-tool and go through complex transformation.
Nexla connects to legacy systems where they are. Through 550+ pre-built connectors, including deep integrations with SAP, Oracle, IBM mainframe environments, legacy databases, and on-premises applications. Nexla’s Instant MCP can surface data from systems that have never had a REST API, without requiring any modification to the underlying infrastructure.
The institutional knowledge locked in enterprise systems is often the most valuable data an enterprise has: decades of transactional history, customer records, operational patterns, and business logic embedded in code that nobody has touched in years. Context-Rich MCP can expose and enrich that data, thus pairing raw legacy records with modern semantic metadata, documentation, and process context, so AI agents can use it meaningfully.
Because Nexla’s Cross-System MCP can combine legacy data with modern SaaS and cloud sources in a single workflow, enterprises don’t need to choose between their old infrastructure and their new AI investments. A procurement agent, for example, can simultaneously query a 1990s-era ERP system, a modern Snowflake data warehouse, and a real-time supplier API, all through one MCP, with full context.
The workflows below represent high-value enterprise processes that have historically been too complex to automate because they span multiple systems. Nexla’s MCP architecture makes all of them tractable.
Systems involved: CRM (Salesforce), billing, usage data, contract management
Outcome: AI agent surfaces renewal risk, drafts outreach, and updates CRM, without leaving a single context window
Billing and contract systems are often legacy ERP. Nexla connects both.
Systems involved: Support ticketing, CRM, billing, product usage, order history
Outcome: Support agents see a complete customer picture in real time with no tab-switching, no manual lookups
Order history and billing data often live in on-prem databases from legacy commerce platforms.
Systems involved: Financial systems, logging/SIEM, document stores, HR systems
Action: AI agent accelerates evidence gathering for SOC 2, ISO, or regulatory audits from weeks to hours
Financial and logging systems are among the most likely to be legacy. Nexla surfaces them without rip and replace.
Systems involved: ERP, supplier portals, inventory systems, commodity price feeds
Action: AI monitors inventory, contract terms, and lead times simultaneously, flagging disruption risk before it impacts operations
ERP systems (SAP, Oracle) are the backbone of supply chain. Nexla connects them natively.
Systems involved: HRIS, IT provisioning, org charts, internal wikis, policy docs
Action: New hires get instant answers from an agent that knows their role, their team, and the company’s actual internal processes
HRIS platforms are frequently legacy on-prem systems or outdated SaaS with limited APIs.
Systems involved: Financial systems, HR records, legal document stores, operational databases
Action: Cross-system MCP maps and queries disparate data stacks from two organizations simultaneously, compressing timelines dramatically
Target companies almost always have legacy systems. Nexla connects both sides of the deal without requiring target-side modernization.
Enterprise connectors
Data records/year
Time to first MCP
Lines of custom code
Salesforce has an MCP. SAP has an MCP. ServiceNow has an MCP. Each exposes its own system’s data through a single-vendor lens. They are, by design, incapable of providing cross-system context, because they have no visibility into the systems they don’t own.
Nexla’s position is fundamentally different. As a data integration layer that sits above all enterprise systems, Nexla is the only platform that can build an MCP with complete cross-system context, combining Salesforce data, SAP records, legacy databases, document repositories, and real-time streams into a single unified context for an AI agent.
MCP architecture refers to the infrastructure that enables AI agents to access enterprise data through the Model Context Protocol. It connects data sources, tools, and workflows so AI agents can retrieve context and perform actions across systems.
Nexla provides cross system MCP architecture that connects 550+ enterprise systems through a single MCP layer. Unlike vendor MCP servers limited to one platform, Nexla enables workflows across multiple systems in one request.
AI agents require access to enterprise systems such as CRM, ERP, data warehouses, and internal documents to provide accurate insights and automate workflows. Without this context, AI responses remain generic and disconnected from business operations.
Yes. Nexla’s MCP architecture connects legacy systems including SAP, mainframe databases, and on premise applications through pre built connectors and AI generated integrations.
Common use cases include revenue operations automation, customer 360 support insights, compliance audits, supply chain intelligence, and HR onboarding workflows.
