We build production‑ready MCP servers that expose reliable tools, connect to enterprise data sources, and enable retrieval‑augmented, policy‑guarded interactions for LLM applications.
The Model Context Protocol (MCP) defines how tools, data stores, and resources are exposed to LLMs in a consistent, secure way. An MCP Server implements this spec to provide capabilities like tool invocation, file and resource access, prompts, and retrieval for AI agents and chat clients.
Define MCP tools for actions like search, CRUD, ETL, notifications, and workflows.
Databases, CRMs, ERPs, ticketing, storage, third‑party APIs, and internal systems.
Resources, prompts, embeddings, and hybrid retrieval against files, DBs, and knowledge bases.
Policy checks, permissions, redaction, rate limits, and human‑in‑the‑loop approvals.
Traces, run logs, error taxonomies, KPIs, A/B evaluations, and regression suites.
Containerized services with CI/CD, env configs, secrets, and cost controls.
MCP Servers provide a standardized, secure bridge between AI clients and real‑world systems so teams can automate tasks safely and consistently across environments.
Follow the MCP spec for tools, prompts, and resources to ensure compatibility.
Apply guardrails, permissioning, and audit trails to meet enterprise requirements.
Instrumentation, error handling, and deployments for reliable, scalable usage.
Connect to databases, business apps, collaboration tools, and data platforms.