By 2026, Gartner projects that 80 percent of enterprises will deploy generative AI in production environments. Many of these businesses will struggle with the same challenge, however: models that sound powerful but miss company-specific context.
To solve this issue, forward-thinking teams are turning to model-connection platforms (MCPs). These solutions act as a secure bridge between LLMs and enterprise data (databases, SaaS apps, APIs and file systems) with governance built in. They enable AI to respond with organization-specific knowledge rather than generic outputs.
This article explores how MCPs work, why adoption is accelerating and what engineering and data teams can take from Devart’s experience building one in production.
What Is a Model-Connection Platform (MCP)?
A model-connection platform (MCP) is middleware that securely links large language models to enterprise data, providing a standardized framework for connecting models to business knowledge. It offers secure data gateways, smooth integration, retrieval augmented generation (RAG) orchestration and governance/auditability.
The Growing Need to Connect LLMs to Internal Data
Large language models are powerful, but they don’t inherently “know” your business. Most are stateless: They generate responses from training data and prompts, but lack the internal context (policies, system logs, product documentation) that guides real decisions.
That blind spot creates predictable problems. It means that, despite being advanced and fluent, LLMs can produce incomplete answers, overlook compliance requirements or resort to guesswork when proprietary details are involved.
To address these issues, some teams attempt quick fixes like copy-pasting documents into prompts. Although this workaround may suffice for a demo, it collapses at scale. Others turn to external APIs as a quick bridge, but this creates even bigger risks: uncontrolled data flows that can expose regulated information outside company walls.
MCPs remove these limitations by providing the missing layer. They securely bridge models and enterprise data, enabling:
- Internal assistants that answer with company-specific knowledge.
- Smart documentation retrieval that cuts through sprawling wikis and ticket systems.
- AI-powered analytics where natural language queries find governed data directly.
What Is an MCP, and Why Are They Emerging Now?
A model-connection platform (MCP) is middleware that securely links large language models to enterprise data. Instead of teams building brittle, one-off pipelines for each use case, an MCP offers a standardized framework for connecting models like GPT-5 or Claude to the business knowledge that makes them useful.
Every MCP delivers four core capabilities:
Secure Data Gateway
MCP enforces permissions, encryption and role-based access so models never overstep their boundaries.
Smooth Integration
It also fuses connections across fragmented sources, from relational databases and APIs to SaaS platforms and unstructured file stores.
RAG Orchestration
MCP manages how models retrieve only the relevant slices of data at query time, ensuring efficiency without compromising control.
Governance and Auditability
It also maintains logs, versioning and compliance records so enterprises can trace and trust every interaction.
Why now? Because enterprise AI has crossed a threshold. Retrieval augmented generation (RAG) is the default method for grounding LLMs, and adoption has shifted from demos to production. Companies no longer want chatbots that sound impressive; they want copilots, support tools and BI layers that work against live data without exposing risks. MCPs are the layer making that possible.
Inside MCP Architecture: Key Components and Features
An MCP is built from several layers that work together to make LLMs useful inside the enterprise. Let’s explore them.
Data Connectors
These are the software “plugs” that let the MCP talk to different systems: databases like Oracle, PostgreSQL, or SQL Server; SaaS apps like Salesforce; and cloud storage. Without connectors, every new integration would require custom coding, making it slow and brittle to maintain.
Vector Search
This is a way of searching based on meaning instead of exact words. MCPs convert documents into numeric “maps” (embeddings) using tools like OpenAI’s embedding models, then store them in vector databases or extensions such as pgvector. This approach allows results to surface even when the wording is different. For example, a query for “vacation policy” can still find a document labeled “annual leave.”
Prompt Management
A prompt is the input sent to an LLM. It’s the question or instruction. Prompt management is about controlling how enterprise data is injected into that input. The MCP filters out irrelevant details, enriches the query with only what’s useful, rewrites or shortens it if needed and routes the final prompt to the right model or workflow.
Security and Compliance
This is the guardrail layer. MCPs enforce who can access what (role-based access control), encrypt traffic, record audit logs and can even mask sensitive fields so private data never reaches the model. It’s what allows enterprises to move from prototypes to production without violating internal or regulatory rules.
Performance Optimization
These are the techniques that keep the system fast and affordable. MCPs cache frequent queries so they don’t hit the database again, process multiple requests in parallel or asynchronously to cut down waiting time and monitor token use to prevent costs from spiraling. Without this, adoption fails once users see delays or bills pile up.
Together, these components make MCPs more than a connector. They’re the framework that ensures models can access enterprise data securely, efficiently and at scale.
Benefits for Engineering, AI and Data Teams
MCPs shift the focus from integration headaches to building AI applications that deliver business value. This results in numerous benefits.
Reduced Engineering Burden
MCPs handle data access and prompt assembly as part of the platform. Engineers spend their time on features and workflows rather than coding one-off connectors or maintaining brittle pipelines.
Faster Prototyping and Deployment
With reusable APIs and prebuilt logic, AI features can move from design to production in a fraction of the time. What once required months of back-end work now becomes a matter of weeks.
Improved Accuracy and Relevance
By grounding responses in enterprise data, MCPs give LLMs the context they need to deliver precise, trustworthy answers. This raises adoption and confidence among end users.
Compliance-First Design
Access controls, encryption, audit logs and data masking are built into the platform. Governance comes by default, so teams can scale AI projects knowing security and compliance are in place from the start.
For engineering, AI and data teams alike, the result is less effort spent on infrastructure, faster delivery cycles and AI outputs that are accurate, secure and ready for enterprise use.
Real-World Applications of an MCP
The real value of an MCP shows up in practice. With governed access to enterprise data, LLMs shift from abstract engines to everyday tools. Here’s where they make the difference:
Business Intelligence and Analytics
Many BI platforms now integrate with MCPs to let users query data in natural language. Instead of writing SQL or navigating dashboards, an analyst can simply ask, “What were last quarter’s sales by region?” and get an answer grounded in governed data.
Internal AI Support Chatbots
With an MCP, support bots can draw directly from company policies, documentation and past tickets. Employees receive consistent, policy-aligned answers instead of generic responses, reducing the load on HR and IT help desks.
Developer Copilot for Database Tools
In environments like PostgreSQL or SQL Server, MCPs allow copilots to suggest queries and code snippets based on internal schemas and data. This helps developers work faster while staying aligned with the company’s own databases and naming conventions.
Knowledge Discovery Tool
MCPs unify search across silos (wikis, databases, document repositories) into a single natural-language interface. A researcher can ask a question once and see relevant answers from across multiple systems.
AI-Enhanced BI Layer
Dashboards become conversational when powered by MCPs. Users can drill into reports with follow-up questions such as “Show me only the EMEA region” or “Compare this to last year,” without writing filters or adjusting queries manually.
These applications all share a theme: MCPs provide the secure connection that turns LLMs from generic text generators into trusted assistants embedded in daily workflows.
Lessons From the Field: Building an MCP
At Devart, building an MCP taught us that success depends less on models and more on infrastructure, data precision and responsiveness.
The first challenge was hybrid environments. Most organizations straddle on-prem and cloud systems, introducing compliance gaps, integration complexity and performance tradeoffs. Unless those environments are bridged consistently, latency and reliability suffer.
Data retrieval was another critical pain point. Over-fetching slows responses with noise, while under-fetching strips away crucial context. Retrieval pipelines need the same rigor as tuning SQL queries. Precision is everything.
From these lessons, a few practices stand out. Begin with one well-defined use case and deliver it end-to-end before scaling. Choose vector databases deliberately: pgvector aligns with PostgreSQL, while Pinecone offers scalability, but the wrong choice can lock you into long-term costs. Apply access control consistently across the pipeline, not just at the prompt.
Finally, success depends on cross-team alignment. Everyone, including DevOps, DBAs and AI leads, owns a piece of the puzzle, and collaboration often matters more than tools. In our case, PostgreSQL with pgvector, LangChain and Azure OpenAI provided a strong foundation, but the real win came from coordination across teams.
Make AI Work With an MCP
MCPs are emerging as the critical infrastructure layer for enterprise AI. By securely linking LLMs to internal systems, they move beyond demos and enable reliable use cases across analytics, support and knowledge management.
At Devart, our experience confirms that the future of intelligent applications depends on more than the model itself. Real impact comes from bridging models and data with governance, precision and speed built in from the start.
For teams experimenting with LLMs today, the next step is clear: audit your data access workflows, identify gaps in security and retrieval and begin shaping an MCP strategy. Those design choices will determine whether AI remains experimental or becomes a lasting business capability.
