Custom MCPs
The AI-native integration layer for your business.
Model Context Protocol (MCP) is the open standard for connecting LLMs to the real world - your databases, internal tools, SaaS platforms, and custom workflows. We design, build, and deploy production-grade MCP servers that give Claude, ChatGPT, Cursor, and every agent you ship a clean, typed, secure interface to your entire stack. No brittle prompts. No copy-paste workflows. Just agents that actually use your systems.
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Stop Bolting LLMs Onto Your Stack. Wire Them In.
Before MCP, every integration between an LLM and your business was a custom one-off: a brittle system prompt, a hand-rolled function-calling shim, or a paid middleware vendor. MCP changed that. It is the one protocol every serious LLM client now speaks - Claude, ChatGPT, Cursor, Zed, Continue, and every agent framework worth running. A Custom MCP turns your proprietary data and internal tools into first-class citizens that any AI can reach securely, consistently, and under your control.
Production-Grade MCP Servers, Not Demos.
We design, implement, and operate MCP servers the same way we would any other production service. Typed contracts, versioned APIs, observability, rate limits, auth, and rollback plans. Your agents get a durable surface; your engineers get something they can actually support.
Tools - typed JSON Schema actions your agents can invoke (query, write, trigger, search)
Resources - streamable data sources your agents can read (logs, tables, files, dashboards)
Prompts - reusable, parameterized workflow templates your team (and agents) share
Auth - OAuth 2.1, scoped keys, SSO, per-tenant RBAC
Observability - structured logs, traces, Prometheus metrics, LangFuse-compatible spans
Deploy - Docker, Kubernetes, or air-gapped, deployed to your infra or ours
Every Internal System Becomes an AI-Native Tool.
If your team uses it, your agents should be able to use it too. We have shipped MCP servers that wrap everything from internal CRMs to warehouse-management systems to bespoke analytics stacks.
CRM + sales stack - Salesforce, HubSpot, Close, Outreach read/write surfaces
Data warehouse - Snowflake, BigQuery, Databricks with row-level security enforced
Product analytics - Amplitude, Mixpanel, PostHog query and cohort endpoints
Internal tools - your Rails/Django admin, Retool apps, internal REST APIs
Ops + DevOps - GitHub, Linear, Jira, PagerDuty, Datadog, Grafana
Domain-specific - EHR systems, legal doc stores, property MLS feeds, church CRMs
Auditable By Default. Safe By Design.
Giving an LLM access to your systems is a trust decision. Our servers treat every call as a potential security event. Scoped tokens, argument validation, prompt-injection guards, full audit trails, and kill switches are standard - not optional upgrades.
OAuth 2.1 + short-lived tokens; SSO via Okta, Entra, Google Workspace
Per-tool, per-tenant, per-role scopes - least privilege enforced at the protocol layer
Argument schema validation with typed guards against prompt-injected payloads
Full audit log: caller, tool, arguments, result hash, latency, outcome
Rate limits, quota, and circuit breakers at the tenant and tool level
Self-hosted deployment so data never leaves your infrastructure
Questions from Professionals Like You
- Model Context Protocol is an open standard published by Anthropic and adopted across the industry. It defines how LLM clients (Claude Desktop, ChatGPT, Cursor, agent frameworks) discover and call tools, read resources, and invoke prompts on remote servers. Think of it as USB-C for AI agents.
- Function calling is per-vendor and per-model. MCP is cross-vendor and cross-client. A well-built MCP server is reachable by Claude, ChatGPT, Cursor, Zed, Continue, and any agent framework that speaks the protocol - with no code changes. You build the integration once and every AI tool in your stack gets it.
- Yes. Most of what we build is wrapping internal databases, internal REST APIs, and industry-specific tools that no off-the-shelf MCP server exists for. We also fork and harden community servers where it makes sense.
- Wherever you need it. We deploy to your cloud (AWS, GCP, Azure), to your on-premise environment, or to gr0.ai-managed infrastructure. Air-gapped deployment is available for regulated industries.
- Custom LLM Training gives your agents a model that knows your domain. AI on Your Data gives them grounded answers from your knowledge base. Custom MCPs give them the hands to actually do the work - reading and writing to your live systems. Most mature engagements use all three.
- A single-system MCP (e.g., a CRM wrapper with 10–20 tools) typically ships in 2–3 weeks. A multi-system platform with auth, RBAC, and observability is a 6–10 week engagement.
Stop copy-pasting into ChatGPT. Give your AI hands on your stack.
MCP is the cleanest integration surface for AI agents that will exist this decade. Book a call and we'll map your stack, identify the three highest-leverage MCP servers to build first, and scope the engagement.