- Strict Schema Architecture. We remove the ambiguity behind many real-world MCP failures. To map real data flows and normalize edge cases, we work with your team.
- Idempotent Async Execution Layer. We add resilience controls that help agents avoid duplicate execution and stalls under load. This architecture handles network timeouts and concurrency spikes without breaking workflows.
- Observability and Reliability Metrics. With MCP as a service, we embed observability tools directly into the MCP server so you can see exactly how tools behave under real workloads. To act on them, you can see latency, pass rates, schema errors, and retries clearly enough.
Model Context Protocol (MCP) and CLI Development Services
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BUILD EACH CONNECTOR ONCE
Wrap each tool in one MCP server that Claude, ChatGPT, and Gemini can all call. A new model or agent uses the servers you already have. -
FIND THE MCP SERVERS ON DEVELOPER LAPTOPS
Map every MCP server your developers run locally and the data each one can reach. Replace them with approved tools that are just as quick to use. -
MAKE MCP SERVERS HOLD UP UNDER LOAD
Test each server with parallel agent traffic before launch. Fix the schema errors and timeouts that make agents fail halfway through a task.
What We Offer
One MCP Hub. Fewer Integrations.
Are you still building a new connector for every AI model and tool? Our MCP development services help you build a standardized MCP architecture with one server per tool.
Make Legacy Systems AI-Ready
Is your business data locked inside legacy systems? We build MCP adapters that turn legacy data into clean JSON that agents can use in real time.
Production Grade MCP Engineering
Do your MCP servers fail too often for agents to rely on them? We build MCP servers designed to keep success rates above 95% under real production load.
Bring Shadow AI Under Control
Do you know which MCP servers your teams are running locally? Each agent receives a short-lived token scoped to one specific action. This limits the blast radius if a token leaks.
Services We Provide
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Production MCP Server Engineering
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Shadow AI Audit and Environment Stabilization
- Shadow Deployment Discovery. We scan your engineering environment for unofficial MCP servers running on developer machines or in personal projects. Our team maps each server, its permissions, and the data it can access.
- Credential and Token Risk Assessment. We analyze how credentials are stored, shared, and used across all MCP tools and servers. We review local configuration files and OAuth pass-through patterns that may expose user identity. Designed to safely eliminate risks.
- Safe Developer Tooling Replacement. Instead of shutting down shadow tools, we replace them with secure alternatives developers will actually use. We mirror the speed and flexibility of their unofficial setups while removing the risks. Built around secure alternatives.
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Secure Authorization Implementation
- Dynamic Downscoping Engine. Instead of giving agents broad access, we generate the narrowest permission set for each tool call currently when it is needed. This limits what a leaked token can do and keeps each action tightly scoped.
- OAuth Pass-through Elimination. We replace user token pass-through with a layered identity model built for MCP workflows. Users keep their OAuth tokens out of tools, servers, and agent workflows.
- Unified Identity Integration. We connect your MCP environment to your identity provider and tie every tool call to a verifiable user or service identity. This gives you clean audit trails and consistent access policies for every tool call.
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Legacy Modernization via MCP Adapters
- Legacy System Mapping. We start by mapping how your legacy systems behave today, not how old documentation says they should behave. To handle it safely, we trace the system behaviors agents need.
- Normalization and Schema Harmonization. We take inconsistent responses and normalize them into typed MCP schemas. We clarify and validate each field, so agents receive predictable data.
- Adapter Server Engineering. We build lightweight MCP servers that sit between your legacy systems and your AI agents, translating old protocols into reliable tool calls. These adapters handle protocol details, so agents can focus on the task.
- Error Shielding and Resilience Layer. Legacy systems fail in unpredictable ways, so we wrap them in a resilience layer that absorbs common legacy system failures. Agents receive clear errors they can act on instead of cryptic system messages.
- Progressive Modernization Path. We design your adapters so they can evolve, adding new capabilities and retiring old ones. This creates a smooth modernization path where you modernize the parts that matter without forcing a full rewrite.
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CI/CD Orchestration for AI Agents
- MCP Pipeline Integration. We plug your MCP servers directly into your existing CI/CD pipelines so all critical MCP components are automatically validated before deployment.
- Automated Tool and Schema Testing. We create automated tests that validate tool behavior and reliability on every commit.
- Safe Deployment and Rollback Automation. We build deployment workflows that roll out changes gradually, monitor behavior, and revert automatically when failures appear.
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MCP Testing
- Parallel Load Simulation. To show how your MCP servers behave under pressure, we simulate real agent traffic across 1 to 32 parallel requests. This includes burst loads, long‑running calls, and production patterns that mirror real production use.
- Tail Latency Profiling. To uncover the hidden delays that cause cascading agent failures, we measure P50, P95, and P99 latency.
- Schema Mismatch Detection. To catch schema mismatches, one of the most common causes of MCP failures in production, we run automated schema validation across every tool. This includes common malformed inputs agents often generate. Best for significantly boosting pass rates in multistep agent workflows.
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Agent Behavior Evaluation
- Sandboxed Trajectory Environments. We create isolated sandbox environments where teams can observe agent behavior step by step and capture every action for review. To test risky workflows without touching production, you get a controlled space.
- Step‑by‑Step Agent Auditing. To show where logic breaks or tools are used incorrectly, we audit each step of the agent trajectory.
- Human‑in‑the‑Loop Review Layer. We build a review layer, so your team can approve high-impact agent actions before execution.
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MCP‑CLI Tooling
- Inner‑Loop Productivity Utilities. We build lightweight local tools for teams that iterate quickly.
- Typed Developer Toolkits. We create strongly typed developer toolkits that wrap MCP tools in safe interfaces.
- Local Sandbox Execution Tools. To test ideas, debug issues, and refine workflows before pushing anything upstream, this gives your team a safe space to test ideas, debug issues, and refine workflows before pushing anything upstream.
- Unified DevTools Experience. We unify internal tools into a consistent CLI experience across your team.
What Changes After MCP Rollout
Integration work happens once per tool
When you add a model or an agent, it connects to servers that already exist. Your team maintains one server per tool instead of one connector per pair.
Your security team can see every tool call
Calls are tied to a person or service in your identity provider and written to an audit log. Unofficial servers are replaced, so the picture is complete.
Fewer agent runs break halfway
Schema checks and load tests catch most failure causes before production. The ones that slip through show up in the server metrics with the call that caused them.
Agents can work with legacy data
Adapters give agents read and write access to older systems through typed tools. The core system stays as it is, and you can modernize it later in parts.
Enabling Real-Time Teleoperation of a Multi-Purpose Robotic Platform
A remote control system for a multi-purpose robotic platform needs a solid backend. Real-time commands, video streaming, and video powered by neural networks are among the baseline features, forming the backbone for efficient teleoperation.
Additional Info
- .NET Framework
- Razor
- PostgreSQL
- Xamarin
- YOLO
United Kingdom
AI-Driven Content Personalization for a Leading Sports Media Platform
AI-driven content personalization engine for a global sports media platform delivering real-time coverage, automated article generation, and fan-tailored news feeds.
Additional Info
- Next.js 14
- .NET 8 APIs
- Python (FastAPI, GPT-4.1, spaCy/HF Transformers)
- PostgreSQL + pgvector
- Kafka/Redpanda
- Redis
- Qdrant
- AKS (Azure Kubernetes)
- Argo CD
Switzerland
Testimonials
Our Experts' Insights
Frequently Asked Questions
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How do we know your MCP servers will stay reliable under load?
We build MCP servers for production reliability, not just basic functionality. We design them to stay reliable under heavy load, in both stress tests and production traffic.
Before launch, we test your MCP servers under realistic load, latency, schema, and concurrency conditions. You review the results directly, including pass rates, tail latency, and concurrency performance.
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Can you keep our agents stable as we scale?
Scalability requires more than the ability to handle a lot of requests. It requires the ability to find and fix odd bugs that occur at higher request volumes. We have experience building architectures that safely support thousands to hundreds of thousands of concurrent requests, and we know how to mitigate failure. We pride ourselves on creating stable, fault-tolerant systems.
The foundation is continuous testing that verifies the system can perform under production conditions. We replay agent calls and simulate load to identify issues before they reach production. See our Vibe Coding Services for a focused offering in this area.
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How do you prevent shadow AI projects inside our company?
We reduce shadow AI by giving developers secure tools that are faster than their unofficial setups. Developers set up MCP servers because they need speed, control, and tools that let them move quickly. We give them secure tools with the speed and flexibility they already expect.
At the same time, we improve visibility and introduce lightweight controls that reduce credential and data flow risk. For the complete scope, visit our AI development services.
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Will we need to replace our existing systems?
No. MCP lets you modernize without replacing your existing infrastructure. Instead of rewriting legacy systems, we build adapters that give agents clean data access and a secure interface. You modernize gradually while keeping the core system stable.Â
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What is the business case, and how do we justify it to leadership?
The payback comes from two places: reliability and security. More reliable MCP servers reduce broken workflows and cut operational overhead. Secure identity flows reduce impersonation and token leakage risk. They also lower financial and compliance exposure. Â
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