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Enterprise AI Integration

MCP Integration Services - Connecting Enterprise Systems to AI Agents

We design and deliver production-grade Model Context Protocol servers that connect your SAP, Salesforce, SharePoint, and proprietary data sources to enterprise AI agents. Every integration ships with authentication, rate limiting, observability, and governance controls built in from day one.

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500+
Enterprise Clients
10,000+
Engineers Deployed
50+
Countries Served
99.4%
CSAT Score
48h
Team Assembly

The Challenge

Enterprise AI Initiatives Stall Without Reliable Data Access

Most enterprise AI pilots succeed in isolated demos but fail to reach production because AI agents cannot reliably access the data they need to be useful. Organisations spend months building fragile one-off connectors to ERP systems, CRM platforms, and internal APIs - only to find those connectors break with each upstream API update, cannot handle concurrent agent requests, and lack the audit trails that compliance teams require.

74%
of enterprise AI projects delayed by integration complexity
6-18mo
average time lost to custom connector rework
$2.4M
average cost of failed enterprise AI integration per initiative
3x
more AI use cases unlocked per dollar with standardised MCP connectors

Why QuickHire

Why Enterprises Choose QuickHire

01

Protocol-First Architecture

We build every integration to the MCP specification rather than proprietary connector formats. This means your AI agents can swap underlying models without rewriting integration code.

02

Enterprise Security by Default

Authentication, RBAC, and encryption are engineered into every MCP server from the initial design - not bolted on after the fact. We align to your existing identity provider from day one.

03

Full Observability Stack

Every MCP server ships with Prometheus metrics, OpenTelemetry traces, and a Grafana dashboard. Your operations team has immediate visibility into every agent-to-system interaction.

04

Concurrency and Rate Control

We implement adaptive throttling and connection pool management so AI agents can operate at scale without overwhelming downstream enterprise systems or competing with transactional workloads.

05

Compliance-Ready Documentation

Data-flow diagrams, control mapping documents, and SBOMs are delivered alongside code artefacts. Compliance and audit teams have the evidence they need without chasing engineers for documentation.

06

Versioned and Maintainable

MCP tool definitions are semantically versioned and contract-tested on every CI build. When upstream APIs change, we detect and resolve drift before it causes production incidents.

Challenges

Common Enterprise Pain Points

01

Fragmented Enterprise Data Landscape

Large enterprises typically have dozens of siloed systems - ERP, CRM, ITSM, HRMS, document management - each with different API styles, authentication schemes, and data formats. Building AI agents that can reason across these systems requires a unifying integration layer that does not exist out of the box. Without it, AI use cases remain constrained to single-system queries that deliver limited business value.

02

Security and Compliance Exposure

Connecting AI agents directly to enterprise systems without structured access controls creates significant compliance risk. AI models that can freely query any data source may inadvertently expose personal data, commercially sensitive information, or regulated records in their reasoning context. Enterprises need a governance layer that enforces data scoping, logs every access, and maps controls to regulatory requirements.

03

Rate Limiting and System Stability

Enterprise systems such as SAP and Salesforce impose API rate limits that are designed for human-paced usage, not the high-frequency call patterns of AI agents processing concurrent user requests. Without intelligent throttling and request queuing, AI-driven integrations can destabilise transactional systems that the business depends on for day-to-day operations.

04

Schema Drift and Maintenance Burden

Enterprise system APIs change continuously - new API versions are released, fields are deprecated, authentication flows are updated, and endpoint paths change. One-off integration code built for AI systems accumulates maintenance debt rapidly, requiring engineering time that should be invested in new AI capabilities rather than keeping existing connectors functional.

05

Lack of Internal Protocol Expertise

MCP is a relatively new standard and most enterprise engineering teams do not yet have hands-on experience designing tool definitions, implementing streaming responses, or handling the edge cases that arise in production AI agent workflows. Attempting to learn the protocol while delivering production integrations on a business timeline creates avoidable risk and rework.

Our Approach

A Structured MCP Integration Practice Built for Enterprise Scale

Our MCP integration practice combines deep knowledge of the Model Context Protocol specification with broad enterprise systems expertise to deliver integration infrastructure that AI agents can depend on in production. We follow a discovery-design-build-govern delivery model that produces maintainable, auditable, and extensible MCP servers - not prototype-quality glue code.

01
MCP Server Development
Custom MCP servers built in TypeScript or Python exposing read and write tools for your specific enterprise systems, with tool definitions optimised for the token constraints of production AI agent workflows.
02
Authentication and Identity Integration
Full integration with your enterprise identity provider - Azure AD, Okta, or Ping - with claim-based access control that scopes agent data access to authorised domains without requiring per-request credential prompts.
03
Tool-Use Pipeline Design
We design the agent tool-use orchestration layer that determines when and how AI agents invoke MCP tools, including multi-step workflows, fallback handling, and human-in-the-loop checkpoints for high-impact actions.
04
Governance and Compliance Framework
Audit logging, data classification tagging, rate limiting policies, and control documentation delivered as a complete governance package aligned to your SOC 2, ISO 27001, or GDPR obligations.

Delivery Models

How We Deliver

Focused Connector Sprint

A single enterprise system connected via MCP - ideal for validating the approach with a high-value use case such as Salesforce opportunity data or SAP inventory queries.

Timeline
4-6 weeks
Team Size
2-3 engineers
Multi-System Integration Programme

Three to six enterprise systems connected under a unified MCP gateway, with centralised authentication, observability, and governance across all connectors.

Timeline
10-16 weeks
Team Size
4-6 engineers
Enterprise MCP Platform Build

A fully productionised MCP integration platform covering all priority systems, with CI/CD pipelines, multi-tenant support, managed operations handoff, and internal team enablement.

Timeline
20-28 weeks
Team Size
6-10 engineers

Capabilities

Technical Capability Matrix

MCP Protocol Engineering
Tool definition designStreaming response implementationSchema validationCapability advertisementError taxonomy design
Enterprise System Connectors
SAP ERP and S/4HANASalesforce CRM and Service CloudMicrosoft SharePoint and Graph APIServiceNow ITSMWorkday HCM
Security and Governance
OAuth 2.0 and OIDC integrationRBAC and claim-based scopingSecrets management (Vault, AWS SM)Audit log pipeline designData classification tagging
Observability and Operations
Prometheus metrics instrumentationOpenTelemetry trace propagationGrafana dashboard developmentAlertmanager rule configurationSLO definition and monitoring

Engagement Models

How We Engage

Choose the model that fits your programme governance, budget cycle, and team structure.

01

Staff Augmentation

Engineers embed directly under your management.

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02

Dedicated Developers

Full-time team aligned to your product roadmap.

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03

Managed Teams

End-to-end delivery with SLA-backed outcomes.

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04

Engineering Pods

Autonomous cross-functional pods per domain.

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05

Offshore Dev Centre

Permanent engineering base in India. Full IP ownership.

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06

Build-Operate-Transfer

We build and run it. You take ownership on schedule.

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Our Process

From Discovery to Delivery

1

Discovery and System Inventory

Days 1-5

We conduct structured workshops with your architects and system owners to catalogue target systems, API capabilities, authentication mechanisms, and data domains that AI agents need to access.

2

Architecture and Tool Design

Week 2

We produce an MCP tool catalogue mapping business use cases to specific tool definitions, plus an architecture diagram showing authentication flows, data paths, and governance controls for review and sign-off.

3

Development and Integration Testing

Weeks 3-10

MCP servers are built in two-week sprints with continuous integration testing against staging environments of connected systems. Auth integration, rate limiting, and audit logging are implemented in the first sprint.

4

Security Review and Compliance Documentation

Weeks 11-12

A structured security review covers authentication controls, data exposure risks, and audit log completeness. Compliance documentation - data flow diagrams, control mappings, SBOMs - is finalised and delivered.

5

Production Deployment and Enablement

Ongoing

MCP servers are deployed to production Kubernetes environments with full observability active. Internal team enablement sessions and developer runbooks ensure your team can maintain and extend the integrations independently.

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Security & Compliance

Enterprise-Grade Security by Default

✓ISO 27001 Certified✓SOC 2 Type II Ready✓GDPR Compliant✓DPDP Act Ready✓NDA on Day 1✓MSA Available✓IP Assignment Clauses✓Escrow Options

Governance

Programme Governance

✓

Tool-Level Access Control

Every MCP tool is classified by risk level - read, write, or destructive - and gated by RBAC policies tied to your enterprise identity provider. Agents receive only the tool capabilities their authorised role permits.

✓

Immutable Audit Logs

Every tool invocation is logged with the requesting agent identity, input parameters, response summary, latency, and outcome. Logs are emitted in structured JSON to your SIEM and retained per your data governance policy.

✓

Data Residency Enforcement

MCP servers are deployed within your designated cloud regions and configured to prevent data from transiting outside approved boundaries. Regional isolation is enforced at the network and application layer.

✓

Change Management and Versioning

All MCP tool definition changes follow a semantic versioning and peer-review process. Breaking changes require a deprecation notice period and a parallel-run migration plan before old versions are retired.

Team Structure

Your Enterprise Team

Each MCP integration engagement is staffed with engineers who hold concurrent expertise in AI agent systems and enterprise integration architecture - a combination that is rare in the market and essential for delivering integrations that work in production AI workflows rather than just in proof-of-concept settings. Team composition scales with engagement scope.

✓MCP Protocol Engineer
✓Enterprise Systems Architect
✓Security and Identity Engineer
✓DevOps and Platform Engineer
✓AI Agent Workflow Designer
✓QA and Contract Test Engineer
✓Compliance Documentation Specialist
✓Engagement Delivery Lead

Project Lifecycle

From Kickoff to Production

01
1 week

Discovery

System inventory, API capability catalogue, use-case-to-tool mapping, risk assessment, and scope confirmation document.

02
1 week

Architecture Design

MCP tool definition catalogue, authentication architecture diagram, data-flow diagram, governance framework design, and effort estimate.

03
6-18 weeks

Development

Production-ready MCP server code, unit and integration test suites, CI/CD pipeline configuration, and sprint demo recordings.

04
2 weeks

Security and Compliance Review

Security review report, resolved findings, compliance control mapping, data-flow documentation, and SBOM.

05
Ongoing

Deployment and Enablement

Production deployment, observability dashboards, developer runbook, knowledge transfer sessions, and support SLA activation.

Case Studies

Enterprise Outcomes

Financial Services

A global bank needed AI agents to query SAP GL and AP data for automated financial reporting without exposing raw database credentials to agent infrastructure.

We built an MCP server over SAP OData APIs with OAuth 2.0 backed by Azure AD, scoped tool access by cost centre ownership, and implemented read-only transaction query tools with field-level masking for PII.

68%reduction in financial report generation time
Healthcare

A hospital network required AI agents to access patient scheduling and clinical documentation across Epic and SharePoint without violating HIPAA data residency requirements.

We deployed regional MCP servers within the client AWS VPC, implemented claim-based patient-cohort scoping, and built audit logs meeting HIPAA access log requirements routed to their existing SIEM.

$1.2Mannual clinical admin cost reduction
Manufacturing

A manufacturer needed AI supply chain agents to query SAP inventory, create purchase requisitions, and check supplier lead times in a unified workflow.

We built read and write MCP tools over SAP S/4HANA APIs with dry-run confirmation flows for write operations and adaptive throttling preventing agent traffic from impacting transactional SAP performance.

4xfaster procurement cycle for AI-assisted purchasing

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Hiring Models

One platform, two ways to hire

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Both models use the same vetted talent network · PM always included · Multi-country billing

Frequently Asked Questions

The Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI language models communicate with external tools, data sources, and services in a structured, secure manner. It replaces brittle ad-hoc API glue code with a consistent, versioned interface that enterprise AI agents can rely on. MCP enables AI systems to read files, query databases, call APIs, and execute functions without requiring bespoke integration code for every new capability. For enterprises, this means faster AI deployment cycles, more maintainable integrations, and a clear security boundary between AI reasoning and system access.
Our MCP integration practice has built connectors for SAP ERP, SAP S/4HANA, Salesforce CRM and Service Cloud, Microsoft SharePoint, Dynamics 365, ServiceNow, Workday, Oracle EBS, Jira, Confluence, internal REST and GraphQL APIs, relational databases (PostgreSQL, SQL Server, Oracle), and cloud data warehouses including BigQuery and Snowflake. Any system that exposes an API, a database connection, or a file interface can be wrapped in a standards-compliant MCP server. We also build read-write MCP tools for workflows that require agents to update records, not just retrieve them.
Traditional API integrations expose endpoints that application code calls directly, which means AI agents must be trained or prompted with specific API schemas and error-handling logic. MCP abstracts this into a tool-definition layer that AI models can discover, understand, and invoke at runtime without hardcoded knowledge. Unlike middleware such as MuleSoft or Dell Boomi, MCP is optimised specifically for the conversational, context-window constraints of large language models - it handles streaming, partial results, and token-efficient responses. MCP also provides a standardised authentication and capability-advertisement mechanism that general middleware does not include.
Every MCP server we deliver includes OAuth 2.0 or API-key authentication at the transport layer, with support for enterprise identity providers such as Azure AD, Okta, and Ping Identity. We implement tool-level RBAC so that different AI agents or user roles can access only the specific MCP tools they are authorised to use. All inter-service communication is encrypted in transit using TLS 1.3, and sensitive credential material is stored in HashiCorp Vault or AWS Secrets Manager rather than environment variables. Audit logs capturing every tool invocation, requesting identity, parameters, and response status are emitted to your SIEM in structured JSON format.
Yes - our MCP server implementations are containerised using Docker and deployable to any Kubernetes environment, including on-premises clusters, AWS EKS, Azure AKS, and Google GKE. We provide Helm charts parameterised for air-gapped and private-cloud deployments where no outbound internet connectivity is permitted. Data residency controls are enforced at the MCP layer so that query results and retrieved documents never leave your designated cloud region or data centre. For regulated industries, we can co-design the deployment architecture with your compliance and infrastructure teams before any code is written.
We implement configurable rate-limiting middleware within each MCP server using a token-bucket algorithm, with separate limits per agent identity, per tool endpoint, and per downstream system. When an agent exceeds a rate limit, the MCP server returns a structured error with a retry-after header so the AI model can back off gracefully rather than entering a retry storm. We also build adaptive throttling that monitors downstream system response times and reduces call frequency automatically when latency spikes are detected. Concurrency controls prevent a single AI workflow from monopolising connection pools that other business processes depend on.
The discovery phase begins with a two-day workshop with your enterprise architects, AI product leads, and system owners to map which data sources and actions AI agents need to access. We catalogue existing API contracts, authentication mechanisms, data schemas, and current rate limits for each target system. We then produce a capability inventory that maps business use cases to specific MCP tool definitions, flagging gaps where new APIs or database views must be created before an MCP server can be built. The output is a detailed scope document and effort estimate reviewed and signed off before any development begins.
A single-system MCP server covering the most critical read operations - such as querying SAP purchase orders or Salesforce opportunity data - can be delivered in four to six weeks including authentication setup, tool definitions, unit tests, and security review. A broader integration covering multiple systems with read-write tools, governance controls, and full observability typically takes ten to fourteen weeks. Timeline depends heavily on the availability of system owners to provide API access and review test results during development sprints. We provide a week-by-week delivery roadmap at the conclusion of the discovery phase.
We design MCP tool definitions with explicit action classifications - read, write, and destructive - and gate write and destructive tools behind additional confirmation mechanisms in the agent workflow. Destructive tools such as record deletion or bulk updates require a secondary verification step, either a human-in-the-loop approval or a cryptographic confirmation token that the AI must request separately. We also implement dry-run modes for high-impact tools so that agents can preview the effect of an action before executing it. All write operations are wrapped in transaction boundaries where the underlying system supports them, enabling rollback if downstream validation fails.
Each MCP server exposes Prometheus-compatible metrics covering tool invocation counts, latency percentiles (p50, p95, p99), error rates by tool and error type, and connection pool utilisation. We instrument servers with OpenTelemetry traces that propagate context from the originating AI agent request through every downstream API call, making it possible to diagnose latency bottlenecks across system boundaries. A pre-built Grafana dashboard ships with each engagement so operations teams have immediate visibility without custom instrumentation work. Alerting rules for error-rate spikes and latency degradation are configured in Prometheus Alertmanager and routed to PagerDuty or your existing incident management system.
We version MCP tool definitions using semantic versioning and maintain a compatibility matrix between tool versions and the downstream API versions they depend on. When an enterprise system releases an API change - such as Salesforce deprecating a REST endpoint - we provide an impact assessment within five business days and a patched MCP server version within two sprints. Our retainer clients receive proactive monitoring of published API deprecation schedules so that MCP tool updates are planned before a breaking change takes effect. We also implement contract tests that run on every CI build to catch upstream API drift before it reaches production.
Yes - our enterprise MCP server architecture supports multi-tenancy through claim-based context injection at the authentication layer. When an AI agent presents a JWT from your identity provider, the MCP server extracts tenant and role claims and applies corresponding row-level security filters to every data query. This means a finance agent and a sales agent can share the same MCP server binary while being transparently scoped to their respective data domains. Tenant isolation is enforced at the query layer rather than relying on the AI model to request only appropriate data, which provides a defence-in-depth guarantee.
We apply a four-layer testing strategy: unit tests covering individual tool handler logic with mocked downstream dependencies; integration tests running against staging environments of the connected enterprise systems; contract tests validating that each MCP tool definition conforms to the MCP specification and that downstream APIs respond as expected; and chaos tests that inject latency, timeouts, and error responses from upstream systems to verify graceful degradation. All tests run in a GitHub Actions or Azure DevOps pipeline on every pull request. A test coverage threshold of 80 percent on handler code is enforced as a merge gate.
Our MCP server implementations are designed with compliance as a first-class concern - audit logs include data classification tags so that GDPR-covered personal data can be identified and excluded from AI context windows when required. We provide a data-flow diagram for each integration showing exactly where data is read, transformed, transmitted, and cached, which forms part of your privacy impact assessment documentation. For SOC 2 and ISO 27001 engagements, we produce control mapping documentation that links MCP server security controls to the relevant trust service criteria or Annex A controls. All code artefacts are delivered with a software bill of materials (SBOM) for supply chain transparency.
We offer three post-delivery support tiers: a standard tier covering SLA-backed incident response within eight business hours and monthly maintenance releases; a professional tier with four-hour response, proactive API deprecation monitoring, and quarterly optimisation reviews; and a managed tier where our engineers operate the MCP servers on your behalf including deployment, patching, and on-call incident management. All support contracts include access to a dedicated Slack channel for technical queries and a monthly operations review meeting. Clients on the managed tier also receive priority access to new MCP capability development as the protocol standard evolves.
Knowledge transfer is structured as a parallel track running alongside the final delivery sprint rather than a compressed handoff at project close. We hold weekly pairing sessions where your developers work alongside our engineers on actual feature development in the MCP codebase. We deliver a developer runbook covering local development setup, testing procedures, deployment processes, and common troubleshooting scenarios. A recorded walkthrough of the architecture, tool definitions, and security controls is provided for team members who join after the engagement concludes. We also offer a post-engagement clinic - four hours of scheduled Q and A sessions during the first three months of independent operation.
Industries
Financial ServicesHealthcare and Life SciencesManufacturingRetailProfessional Services