Custom MCP Servers as Part of Enterprise AI Infrastructure
We build and operate MCP servers as part of enterprise AI infrastructure, enabling LLMs to interact with authoritative systems and data sources without sacrificing control, compliance, or operational stability.
MCP Server Development for Regulated, Production AI Systems
Design the AI Integration Layer
We help organizations define how LLMs should interact with internal systems, regulated data sources, and external services. MCP is treated as an architectural interface, not a shortcut, embedded within existing security, governance, and compliance models.
uild Production-Grade MCP Servers
We engineer custom MCP servers that prioritize latency, availability, auditability, and predictable behavior. Each server is designed for real workloads, with robust observability, error handling, and scaling strategies suitable for regulated production environments.
Operate and Scale with Confidence
We deploy, monitor, and maintain MCP infrastructure under real demand. From hosting and SLAs to versioning and long-term ownership, we ensure MCP servers remain reliable, compliant, and ready to evolve as AI use cases scale across the organization.
Proven in Production with Leading AI Platforms
deepsense.ai, as an Anthropic partner, have designed and run MCP connectors used in live Claude deployments across healthcare and life sciences, powering access to authoritative sources such as CMS, ICD-10, NPI, ChEMBL, ClinicalTrials.gov, and bioRxiv/medRxiv, listed in the official Claude Connectors Directory.
In parallel, we have delivered production connectors and AI integrations in collaboration with OpenAI teams, including enterprise deployments for enterprise organizations.
How We Build and Operate MCP for Regulated AI
A comprehensive approach to designing, building, and operating MCP servers as part of secure, production-grade AI infrastructure, enabling LLMs to interact with authoritative data and systems while meeting the demands of scale, compliance, and regulatory scrutiny.
Identifying the Right MCP Strategy
We help organizations determine when MCP is the right abstraction, how it should be scoped, and how it fits into 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.
The result is AI systems that have access to the right context and tools, without sacrificing control, traceability, or operational safety.
Custom MCP Server Development
We build MCP servers that connect LLMs to authoritative, business-critical data sources, from internal databases and proprietary APIs to 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.
Our experience in regulated industries means we routinely integrate with complex enterprise systems while preserving data provenance, enforcing least-privilege access, and ensuring that every request can be traced and reviewed when required.
Engineering MCP Servers for Production Reality
A functional MCP server is not enough in production environments.
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.
This is the difference between MCPs that work in demos and MCPs that can be trusted inside live, regulated systems.
MCP-as-a-Service
For teams that want to move faster without building and operating this layer internally, we offer 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.
Typical timelines range from weeks for pilots to a few months for production-grade systems, depending on scope and integration complexity.
Our MCP and MLOps State-of-the-Practice
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Healthcare & Life Sciences MCP Server Suite
We built six specialized MCP servers that unify access to key healthcare and life sciences data sources.
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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…
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Accelerating LLM Experimentation for ML Use Cases
The client received a production-ready testbed to explore LLM performance across a range of ML tasks.
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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.
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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…
What Our Clients Say About Us
“At Unstructured, we have been delighted to partner with deepsense.ai, a collaboration that has significantly accelerated the development across our Product Roadmap. Specializing in the complex domain of unstructured ETL for RAG, deepsense.ai has matched our technical intensity and contributed across various functional areas.”
Brian S. Raymond
Founder & CEO at Unstructured
“Sky partners with deepsense.ai on ML, predictive analytics, price elasticity, and AI consulting. In price elasticity modeling, deepsense.ai strengthened pricing strategy and RevOps through robust model design and optimization, delivering accurate, adaptable solutions with clear business impact.”
Kostis Manolitzas
Group Head of Data Science Innovation and AI at Sky
” Our collaboration shows how to apply cutting-edge AI in niche markets and industries where we seek a competitive advantage. We share efforts in our innovative approach, which differentiates us from peers and startups, embodying our belief that it’s better to disrupt ourselves than to be disrupted by the competition.”
Burkhard Boeckem
CTO at Hexagon AB
“One particular example where deepsense.ai’s expertise really stood out was their involvement in the development of our GenAI-powered frontline worker digital assistant. The solution integrated a diverse set of data sources, providing assistance to frontline employees with relevant responses in their moment of need.”
Tom Bianculli
CTO at Zebra Technologies
“deepsense.ai has been a dependable and high-quality partner to Brainly’s AI research, development, and operations efforts over the past 3 years. Their team has integrated seamlessly with our in-house teams, bringing top-tier talent and a collaborative spirit that drives innovation.”
Bill Salak
CTO & SVP Operations at Brainly
“We engaged deepsense.ai for an AI Advisory engagement with the aim of reviewing and enhancing our AI capabilities and practices. deepsense.ai was adept at identifying practical quick-win improvements in our AI operations, providing guidance for our long-term investment priorities in the AI domain and ensuring a thorough transfer of knowledge to our internal AI team throughout the engagement.”
Mariusz Gralewski
CEO at DocPlanner
“We have successfully partnered with deepsense.ai on multiple R&D projects. The deepsense.ai team was able to effectively partner and work hand-in-hand with our development team, complementing our domain knowledge with deep expertise in AI/ML and predictive analytics.”
Ned Taleb
Co-Founder & CEO at B-Yond
“deepsense.ai quickly delivered a Proof of Concept for a code completion tool, using a state-of-the-art technological stack, including the newest available LLMs and libraries. They also led an excellent LLM discovery workshop that jump-started AdaCore’s integration of LLM solutions into our business processes and products.”
M. Anthony Aiello
Head of Product & Innovation at AdaCore
“The deepsense.ai solution for anomaly detection helped expedite the problem detection times and decreased the number of monitoring failures. We were also impressed by their team’s technical know-how and solution-based mindset.”
Nitin Navare
CTO at LogicMonitor
“Over a three-year period, deepsense.ai has augmented our internal capabilities with a dedicated team of consultants, helping us enhance our MLOps Platform. Their involvement from early ideation and design phases through to the entire software development cycle has ensured a high standard of engineering maturity and adherence to industry best practices.”
Carsten Ingerslev
Head of Advanced Analytics
at Danish Business Authority
“We engaged deepsense.ai to improve some of the functionalities of our AI-driven transportation management platform in terms of Estimated Time of Arrival (ETA) and On-time Probability (OTP). The ease of collaboration with deepsense.ai’s team and their adaptability to our feedback and ideas made the entire development process seamless and productive.”
Paul Beavers
CTO (2020-2023) at a transportation management platform provider
Our Insights and Resources
Explore in-depth insights on LLMs, RAG, MLOps, computer vision, edge solutions, predictive analytics, and beyond—delivering value-packed perspectives for both business leaders and developers.
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Custom MCP Servers as Part of Enterprise AI Infrastructure
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