Foundation models are blind without your data. We deploy standardized MCP servers that securely connect models like AI Engine directly to your Salesforce CRM, internal ERPs, or custom Neo4j user graphs—with zero data leakage and full auditability.
Giving an LLM direct API access to your production database is a massive security risk. We use MCP to create a secure, standardized bridge.
Whether your B2B Analytics Agent needs to read a client's billing history, or your LangCase Tutor Agent needs to read a student's spaced-repetition decay curve, the MCP server acts as a strict, role-scoped bouncer.
Protocol
JSON-RPC over STDIO
Avg Latency
14ms
from mcp.server import Server
mcp = Server("enterprise-context-layer")
# Endpoint 1: B2B Enterprise Use Case
@mcp.resource("crm://{account_id}/open_opportunities")
def get_sales_context(account_id: str) -> str:
"""Exposes active sales pipeline data securely to the Lead Gen Agent."""
return salesforce_client.get_opps(account_id, validate_role="Sales_Agent")
# Endpoint 2: Consumer EdTech Use Case
@mcp.resource("student://{user_id}/vocab_decay_curve")
def get_decay_metrics(user_id: str) -> str:
"""Exposes Spaced Repetition (SRS) metrics to the LangCase Tutor Agent."""
return postgres_client.get_srs_data(user_id, validate_role="Tutor_Agent")We engineer MCP servers that define exact "tools" and "resources" the AI can use. The model never sees your SQL connection strings or Neo4j credentials.
It simply requests a resource URI, and the MCP server validates the request before returning the specific payload.
Zero-Trust Architecture
Enterprise IT requires absolute transparency. Because every interaction flows through the MCP layer, we generate granular, real-time audit logs across all your AI applications.
You will always know exactly which agent requested what data, whose permissions were used, and how long the retrieval took.
We refuse to build technical debt for our clients. Standardizing on the Model Context Protocol ensures your infrastructure is future-proofed against the rapidly changing AI landscape.
Date: 2026-05-09 · Status: Selected
| Integration Method | Security | Reusability | Verdict |
|---|---|---|---|
| MCP | High (Strict Scoping) | High (Universal Client) | Selected |
| Custom REST Wrappers | Medium | Low (Integration Sprawl) | Rejected |
| Direct DB Plugins | Critical Risk | Medium | Rejected |
All proprietary data connections will be routed through standardized MCP servers. This provides standardized, universally supported data bridges for models like Gemini and Claude while maintaining strict resource-level security.
Don't let fragile APIs slow down your AI adoption. Let our engineering team build the secure MCP infrastructure your data deserves.