The AI assistant that could talk about the work but never touch it.
A team rolls out an AI assistant and it answers general questions well. Then someone asks it to pull up a real customer record, check the actual inventory level, or open a ticket in the project tool, and it can't. It knows the internet. It doesn't know your business.
MCP is the standard that closes that gap. An MCP server exposes your data and tools through one interface, and the assistant goes from describing the work to doing it: reading the real record, checking the real number, writing the real ticket.
That server is the product. The chat window is just where the work shows up.
A language model trained on public data can generate text, summarise documents, and answer general questions. That's useful. What's more useful is an AI that can look up your actual customer record, check your real inventory level, or create a ticket in your actual project management tool. MCP is the standard that makes this possible: we build the MCP server, you get an AI assistant that knows your business, not just the internet.
According to McKinsey's State of AI 2025, 78% of organizations now use AI in at least one business function, yet only 28% have connected their applications effectively, according to MuleSoft's 2025 Connectivity Benchmark. The gap between AI adoption and real system integration is where MCP servers close the loop.
RaftLabs has shipped 100+ products since 2015 for clients across the US, UK, Europe, Canada, the GCC, South Africa, and Southeast Asia, including Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. The senior engineers who assess your MCP requirements also build the server: no offshore handoff after the contract is signed, and the team you meet in week 1 ships in week 12.
An MCP server pays off when your AI needs to act on real systems, not just answer.
Everything on the left should already be true for your operation. Even one thing on the right, and a plain AI assistant is the smarter starting point.
A fit01Your team already uses AI assistants like Claude but can't connect them to your actual business data.
02You have databases, APIs, file systems, or business tools you want the AI to read from and act on.
03You want AI workflows that take action on your systems, not just generate text, and budget for a build from $15,000.
Not a fitYou only need an assistant to draft and summarise text, with no systems to connect.
The data you need lives nowhere your infrastructure can reach.
You want an off-the-shelf chatbot, not a secure, permission-scoped integration layer.
What we build
What we build into your MCP server
01Database and data access tools
MCP tools that give AI assistants structured, permission-scoped access to your databases: parameterised SQL with input validation, clean structured results, and explicit access controls per tool. Each query tool defines which tables it can read, and write tools log every change with timestamp, session identifier, and before/after values, without your team building a custom API endpoint for every question. Built for PostgreSQL, MySQL, SQL Server, and MongoDB.
02API and system integration tools
MCP tools wrapping your internal APIs and third-party business systems, translating natural language requests into authenticated, validated API calls. Token refresh, retry logic with backoff, and response schema validation are built into the tool layer so expired credentials and rate limits never surface as AI errors, and each tool is tested against your actual API environment before deployment, not just mocked. Covers OAuth 2.0 flows and systems like Salesforce, HubSpot, Jira, Linear, and Slack.
03File and document access tools
MCP tools for reading, listing, and searching your document repositories, with resource URIs scoping access to exactly the prefix or site granted, never the full storage account. Extraction pipelines handle PDFs, Word documents, and plain text with structure preserved, and semantic search over a vector index retrieves relevant sections by meaning, returned with filename and page number so every answer is traceable. Works across S3, GCS, Azure Blob, SharePoint, and Confluence.
04Action and workflow tools
MCP tools that let AI take actions: creating records, updating fields, sending messages, and completing multi-step tasks without a human clicking through each step. Guardrails sit at every level, preview-and-confirm before changes execute, idempotency keys so retries never duplicate records, per-client rate limiting, and an audit log for every action, with destructive actions requiring a separate confirmation tool call enforced by design. Runs over SSE and stdio transports, verified with MCP Inspector.
05Authentication and security
Security architecture designed to satisfy your compliance team, not just make the AI work. Scoped API keys grant each client only its defined tools, OAuth 2.0 PKCE lets the AI act with a user's own permissions rather than a broad service account, PII is redacted at the tool response layer before anything reaches the model, and every request is logged and exportable for SOC 2 evidence.
06Agentic workflow orchestration
Multi-step AI workflows that chain your MCP tools into end-to-end automated processes: agents that complete a sequence of actions, not just single-turn queries. Built for stateful workflows with conditional branches, human-in-the-loop gates pause high-value or irreversible actions for confirmation, and error handling separates retryable failures from those needing a person. Orchestrated with LangGraph.
Tell us which systems you want your AI assistant to work with.
Walk us through the tools and data. We'll design the MCP server and give you a fixed cost.
How it works
From scope to shipped
Every MCP project follows the same four phases. Scope is locked and price is fixed before development starts.
- Week 1
01Discovery and scope
We map the systems to connect, the tools to expose, and the access controls required. You leave week 1 with a written scope document listing every tool, resource, and permission boundary, plus a fixed-price quote. No development starts without your sign-off.
- Weeks 2-3
02Architecture and security design
We design the MCP server architecture: tool schemas, authentication model, audit logging, and data redaction rules. Every security decision is made here, not retrofitted before launch. The spec is locked before the build starts.
- Weeks 4-12
03Build, integrate, and QA
Working MCP server at a staging environment by the end of sprint one. Bi-weekly demos against your actual systems. QA runs in parallel with every sprint using MCP Inspector and real integration tests, not mocks.
- Weeks 12+
04Deploy and post-launch support
Production deployment with monitoring and audit logging activated on launch day. 8 weeks of post-launch support included. Tool changes and new integrations are scoped and priced as change requests.
We price by project, not by the hour. After scoping, you get a fixed quote: a defined scope, a timeline, and a price. Where you land depends on scope, not negotiation:
- Focused MCP server, $15,000-$35,000
- 5-10 tools connecting to 2-3 systems, scoped, built, and deployed.
- Complex MCP server, $30,000-$60,000
- Many tool types, complex authentication, and multiple system integrations with advanced access controls.
The cost depends primarily on the number of systems to integrate and the complexity of the access control requirements. We scope every project before pricing it.
What it costs
Fixed price, scoped before development starts.
A scoped MCP server connecting your AI to the databases, APIs, and tools you choose, with the access controls and audit logging it needs to run in production.
$15,000-$60,000Fixed cost by project. Production-ready in 12 weeks. Scoped in detail before development starts.
The cost depends on the number of systems to integrate and the complexity of your access control requirements. You know the number before development starts.
Fixed price
We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a priced change request: agreed, or dropped. It never absorbs into the project or appears on the final invoice.
Compliance built in
GDPR, HIPAA, and SOC 2 requirements are scoped in week 1, not retrofitted before launch. We have shipped HIPAA-compliant AI systems for US healthcare clients and GDPR-compliant products for European markets.
