Design the AI Integration Layer

Build Enterprise MCP Servers

Operate and Scale

ANTHROPIC | deepsense.ai, as an Anthropic partner, has designed and run MCP connectors used in live Claude deployments across healthcare and life sciences, powering access to authoritative sources, listed in the official Claude Connectors Directory.

OpenAI | In parallel, we have delivered production connectors and AI integrations in collaboration with OpenAI teams, including enterprise deployments for enterprise organizations.

Identifying the Right MCP Strategy

We help organizations determine when MCP is the right abstraction, how to scope it, and how it fits within an existing enterprise architecture. In many cases, MCP servers coexist with traditional APIs, internal services, data platforms, and governance layers.

Our teams combine hands-on experience with: agentic AI workflows, secure system integration, regulated data access, and compliance frameworks such as HIPAA and GxP.

Custom MCP Server Development

We build MCP servers that connect LLMs to authoritative, business-critical data sources, including internal databases and proprietary APIs, third-party platforms, and public scientific or regulatory repositories.


Each MCP server is designed individually, based on: data sensitivity and access controls, latency and throughput requirements, audit and logging expectations, and the role the connector plays in downstream workflows.

Engineering MCP Servers for Production Reality

We engineer MCP infrastructure to handle: real user traffic and burst loads, failure scenarios and partial outages, source system rate limits and data changes, long-term maintenance and versioning.

Our implementation standards emphasize: predictable performance under load, comprehensive observability and logging, explicit error handling and degradation strategies, and architectural separation between AI reasoning and system access

MCP-as-a-Service

We take responsibility for: MCP server development and evolution, secure, enterprise-grade hosting, scaling and availability management, monitoring, alerting, and incident response.

Our managed offering includes clearly defined SLAs, usage-aligned pricing models, and direct access to the engineers who designed the system.

Every engagement is scoped individually, reflecting the reality that regulated AI systems require tailored operational guarantees, not one-size-fits-all platforms.

Our MCP and MLOps State-of-the-Practice

Enterprise-Grade Cloud Infrastructure Powering Scalable AI at Scale

Enterprise-Grade Cloud Infrastructure Powering Scalable AI at Scale

We delivered a cloud-native data science and ML platform on GCP and Kubernetes, fully aligned with enterprise security and IT standards.

Healthcare & Life Sciences MCP Server Suite

Healthcare & Life Sciences MCP Server Suite

We built six specialized MCP servers that unify access to key healthcare and life sciences data sources.

Guiding AI Success  in Infrastructure Monitoring

Guiding AI Success in Infrastructure Monitoring

The project delivered a clearer AI strategy, improved prototype performance with measurable quality gains, and equipped the client with practical methods…

Accelerating LLM Experimentation  for ML Use Cases

Accelerating LLM Experimentation for ML Use Cases

The client received a production-ready testbed to explore LLM performance across a range of ML tasks.

Scalable AI Infrastructure for SES AI – Enabling 128-GPU Deep Learning Workflows in the Cloud

Scalable AI Infrastructure for SES AI – Enabling 128-GPU Deep Learning Workflows in the Cloud

The solution significantly improved the efficiency and reliability of the Data Science workflows.

Streamlined Observability for GenAI Workflows

Streamlined Observability for GenAI Workflows

In our project, the observability system allows us to monitor full agentic workflows, link experiments to execution providers, and inspect both high-level sequences and…

Brian S. Raymond

Kostis Manolitzas

Burkhard Boeckem

Tom Bianculli

Bill Salak

Mariusz Gralewski

Ned Taleb

M. Anthony Aiello, Head of Product & Innovation at AdaCore

M. Anthony Aiello

Nitin Navare

Carsten Ingerslev

Paul Beavers

  • Building MCPs for Regulated Industries: Lessons from Production AI in Life Sciences

    Building MCPs for Regulated Industries: Lessons from Production AI in Life Sciences

  • deepsense.ai becomes Anthropic’s Service Partner

    deepsense.ai becomes Anthropic’s Service Partner

  • MCP in the Enterprise: Real Security Risks and How Developers Can Mitigate Them

    MCP in the Enterprise: Real Security Risks and How Developers Can Mitigate Them

  • Building ChatGPT Connectors: Lessons Learned with FastMCP, MCPInspector & OAuth

    Building ChatGPT Connectors: Lessons Learned with FastMCP, MCPInspector & OAuth

  • Understanding the Model Context Protocol: How Developers Can Build Secure, Cross-Model AI Integrations for Claude, ChatGPT, Cursor and GithubCopilot

    Understanding the Model Context Protocol: How Developers Can Build Secure, Cross-Model AI Integrations for Claude, ChatGPT, Cursor and GithubCopilot

  • Lessons Learned: Building Multi-Agent Systems with Anthropic’s MCP & Pydantic AI

    Lessons Learned: Building Multi-Agent Systems with Anthropic’s MCP & Pydantic AI

Providing guidance and delivering tailored AI solutions that give you a competitive advantage.

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Join our established list of long-term satisfied clients, including global brands, tech enterprises, ambitious scaleups and startups. Whether you’re rapidly scaling with AI or making it the core of your business, partner with us to achieve exceptional results.