AI Integration & MCP Server Development
Our AI integration work connects language models to the ERP, CRM, internal APIs, and older systems you already run, through APIs and MCP servers, with access limited to what each job needs.
Language model
Asks for data and actions
MCP SERVERS AND APIS
- ERP
- CRM
- Internal APIs
- Legacy
SYSTEMS YOU ALREADY RUN
What Is MCP Integration?
Model Context Protocol integration connects AI models to business systems through the Model Context Protocol, an open standard for describing what a system can do in terms a model can use. Think of it as API documentation written for AI. It tells the model what happens when it calls each tool, so the model picks the right one.
An MCP server sits in front of a system, such as an ERP, a CRM, or an internal API, and lists the tools it offers. Any agent or assistant that speaks MCP can then use them. MCP isn't required, but it usually cuts the tokens a model spends working out how a system behaves.
Do we need MCP to connect AI to our systems?
Not always. One API call to a language model works well for a single job, like checking an invoice inside an existing BI job. MCP pays off when several agents need the same systems, or when internal APIs aren't documented.
WHEN YOU NEED IT
Signs Your AI Needs Connecting
AI tools stall when they can't see your data or reach your systems. Watch for these signs.
AI can't see your data
Your assistant answers general questions but knows nothing about your orders, inventory, or customers, because nothing connects it to those systems.
Every SaaS vendor sells its own AI
Each product adds its own AI layer at its own price, and none can see the data in the others.
Internal APIs nobody documented
Your systems have APIs, but they were never published or documented, so neither people nor models know what each one does.
Key rules live in older systems
An ERP or an AS/400 holds years of business logic, and replacing it to add AI would cost more than it returns.
People copy AI answers by hand
Staff ask an AI tool a question, then re-key the answer into the ERP or CRM, so the work takes as long as before.
Token bills you can't explain
Models spend tokens working out raw data and undocumented systems on every request, and the bill climbs with each new use.
OUR APPROACH
How We Connect AI to What You Run
Calling a language model is one of the simplest integrations there is. The hard part is the systems it talks to, such as an ERP, with its calls, error checks, and retries. We build that layer once, so every agent and assistant can use it. Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
Bolted-on AI
- An AI add-on for every product
- Data copied into another platform
- Rules written into prompts
- A new portal for every tool
Connected AI
Keep the system, let AI drive it
We don't replace systems that hold good business logic. The AI works through them. For a large car retailer, an AI assistant guides associates through the existing sales system.
Hard rules stay in APIs
Rules that can't be broken live in code, exposed to the model through MCP, not in a prompt it might ignore. A car title doesn't release until every document is on file.
Least access by default
Each connection gives the model only the permissions its job needs. Analysis runs in a sandbox, not your production data warehouse, and no model gets permission to delete.
Your data, your model accounts
Where we can, we connect AI through your own model accounts and data stores, so you own the prompts, the tuning, and the data.
Will MCP last?
Nobody knows how long any standard will last. Right now MCP is the strongest option for describing systems to AI, and because an MCP server is a thin layer over your APIs, the APIs stay useful if the standard changes.
INTEGRATION PATTERNS
Ways to Connect AI to Your Systems
There are five common ways to connect a language model to business systems, and most companies use more than one. The right one depends on what each system exposes.
| PATTERN | WHAT IT MEANS | WHEN IT FITS |
|---|---|---|
| API call | An existing job sends data to a language model in one request and uses the answer | One task inside a job you already run, like checking an invoice |
| MCP server | A layer in front of a system that describes its tools to any model or agent | Several agents need the same system, or its APIs aren't documented |
| Hard gates | Business rules exposed as APIs the model must call and can't override | A mistake would be costly or irreversible |
| Read-only queries | The model reads data through approved, read-only database queries | A product has no usable API, or analysis needs raw data |
| Screen automation | An agent works through the application's screens the way a person would | There is no API and no database access |
WHAT WE BUILD
What We Connect and Build
We build the connectors, MCP servers, and integration code that let AI work with the systems your business already depends on.
For connecting business systems to each other, see systems and enterprise integration.
MCP servers for your systems
MCP servers in front of internal APIs, CRMs, ERPs, databases, and older systems, even file transfers to an AS/400. Once a system has one, any agent can use it.
A support agent across six systems
For a national luxury florist, Claude connects through MCP to ticketing, the code repository, the database, inventory, fulfillment, and AWS CloudWatch logs, and escalates to a person when needed.
AI over ERP and manufacturing data
For a global RF and microwave manufacturer, a multi-agent platform answers plain-language questions across BOM, parts, inventory, order, and sales data. See AI-powered analytics.
AI inside an existing application
For a national direct lender, Azure OpenAI Service is built into the internal loan system rather than bolted alongside it. It checks tax documents and reports to a human reviewer.
AI search on a document hub
For a national veterinary management group, we added AI search with citations to the document hub that hundreds of clinics already use.
AI where people already work
AI delivered through Teams, Slack, email, or the CRM, so nobody logs into another portal. A purchasing assistant can be a contact in Teams.
HOW IT WORKS
How an Integration Project Runs
An integration project runs in five steps, from mapping what your systems expose to a connected agent in daily use.
STEP 1
Map the systems
Often as part of an AI readiness assessment, we list the systems a use case needs and what each exposes, such as APIs, databases, or files.
STEP 2
Pick the pattern
For each system we choose an API call, an MCP server, a hard gate, read-only queries, or screen automation, and set its permissions.
STEP 3
Build the connectors
We build the MCP servers and API code, with error handling, retries, and logging, and test them against real requests.
STEP 4
Connect the AI
The model or AI agent gets the tools it needs, with a person approving any action that changes data.
STEP 5
Reuse and expand
Each connector serves the next use case too, so the second agent comes faster.
Security for every connection
Each connector uses its own credentials and only the permissions its job needs. Analysis reads from a sandbox where possible, write actions wait for a person, and every call is logged. See AI governance and security.
What your team gets at handoff
You own everything we build, including the MCP servers and connectors. Services we can provide at handoff include knowledge transfer and documentation of each connector and its permissions. Ongoing support is also available.
RESULTS
Integration Results
One support agent reaches six systems through MCP, and a multi-agent platform puts ERP and manufacturing data behind plain-language questions.
5,500
Employees served by one AI knowledge base
Manufacturing
A GenAI Knowledge Base for a Building Materials Manufacturer
Challenge: Operational documents were spread across many systems, and employees struggled to find specific answers.
Result: Employees ask questions in plain language and get answers almost instantly, from a knowledge base built on Amazon Bedrock and OpenSearch.
Read the building materials case study<10 min
Per listing, down from up to a full day
Real Estate
AI Listing Creation for a Real Estate Client
Challenge: Realtors spent hours entering MLS listings by hand on outdated platforms.
Result: An Amazon Bedrock platform and a Chrome extension cut listing creation from up to a full day to under 10 minutes.
Read the real estate case study"JBS feels more like an extension of our internal team than a traditional vendor relationship."
Senior IT Leader, AmeriVet Partners
TECHNOLOGY
How MCP Connects a Model to Your Systems
In an MCP setup, the model doesn't call your systems directly. It asks an MCP server for a tool, the server checks permissions and calls the system's API, and the result comes back in a form the model can use.
We choose technology for the job. Tools we have connected AI with include Claude and Claude Code, Azure OpenAI Service, Amazon Bedrock, and AWS CloudWatch. Low-code tools that can call an API can call a language model too.
ASK FOR A TOOL
AI agent
MCP server
Permissions
YOUR SYSTEMS
ERP
CRM
Legacy
BEFORE ANY CHANGE
Agent proposes
The change
A person approves
Or rejects
COST AND TIMELINE
What Drives Integration Cost and Timeline
Cost and timeline depend mostly on how many systems the AI must reach and what each one exposes. A documented API is a small job, and an undocumented system with no API is a bigger one.
- 01Number of systems to connect
- 02APIs and documentation available
- 03Read-only or write access
- 04Approval steps required
- 05Security and compliance rules
- 06Request volume and token usage
How the timeline is set
Timelines depend on the systems involved, and the assessment or proof of concept sets them. Older systems without APIs take longer, because the connection has to exist before the AI can use it.
How cost is set
We don't quote a number before we understand the systems. The assessment maps what each one exposes and turns that into an estimate per connector. Connectors are reused across use cases, so later projects build on earlier ones. If a connector won't pay for itself, we'll say so.
ENGAGEMENT MODELS
Ways to Work With Us
Each model stands alone or leads into the next. Compare all the ways to work with us.
Assessment & Strategy
For companies that need clarity before committing to a build.
Start here if you're not sure what to change first. We review the system component by component, weigh difficulty against business impact, and recommend a path for each part, including what to leave alone. You get a roadmap you can act on with us or on your own.
- Technical assessment
- Architecture review
- AI opportunity mapping
- Modernization roadmap
- Prioritized recommendations
Best when: You need to understand the opportunity, risk, architecture, or roadmap before investing in a larger build.
Projects
For companies ready to build or modernize a defined system.
For a defined scope, senior engineers lead delivery from architecture through release. We work in stages, write tests for core components, and ship through CI/CD. You own everything we build, plus knowledge transfer and documentation tailored to your team.
- Modernization projects
- Custom applications
- Cloud platforms
- Data systems
- Integrations
- AI agents
Best when: You have a clear business problem, workflow, or platform that needs to be built, modernized, or connected.
Teaming / Managed Delivery
For companies that need experienced delivery capacity.
When you need more than a single project, services we can provide include architectural guidance for your existing team, hands-on development, QA, and ongoing support. Senior US-based engineers lead our builds, and nearshore support is an option for ongoing work.
- Senior-led teams
- Embedded engineering
- Ongoing development
- Knowledge transfer
- Flexible support
Best when: You need reliable engineering capacity, technical leadership, or ongoing delivery support without adding unnecessary management overhead.
WHY JBS DEV
How to Choose an Integration Partner
An integration partner should know your older systems as well as the AI models. Ask these five questions.
- Have you connected AI to real business systems?
- Yes. We've connected AI to a luxury florist's ticketing, inventory, fulfillment, and logging systems, to an RF and microwave manufacturer's ERP and manufacturing data, and to a lender's internal loan system.
- Can you work with systems that have no API?
- Yes. We build MCP connectors for undocumented internal APIs and older systems, even file transfers to an AS/400. Where there's no API at all, read-only queries or screen automation can fill the gap.
- Will you replace our existing systems?
- Not to add AI. Systems with good business logic stay, and the AI works through them.
- Who controls what the AI can do?
- You do. Each connection has its own permissions, actions that change data wait for a person's approval, and every call is logged.
- Who does the work?
- Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
JBS Dev is a senior-led custom software development company, founded in 1999, that builds, modernizes, and integrates enterprise systems around the technology clients already own.
FAQ
AI Integration FAQs
What do AI integration services include?
They cover connecting language models and agents to your business systems: mapping what each system exposes, building MCP servers and API connectors, setting permissions and approval steps, and logging what the AI does. Ours connect to the ERP, CRM, internal APIs, and older systems you already run, without replacing them.
What is the Model Context Protocol?
The Model Context Protocol, or MCP, is an open standard for describing a system's tools to AI models. An MCP server tells the model what each tool does and what happens when it runs, much like API documentation written for a model. Any agent that speaks MCP can then use those tools.
Is MCP secure?
MCP is as secure as the permissions behind it. We give each MCP server its own credentials and only the access its job needs, keep analysis in a sandbox, require a person's approval for actions that change data, and log every call. Rules that can't be broken stay in APIs, not prompts.
Can AI work with our legacy systems?
Yes, in most cases without changing them. We put an MCP server or API layer in front of the system, so AI can read approved data and call approved actions while the system's own rules still apply. Where no API exists, read-only queries or screen automation can work.
Should we use NetSuite AI or SharePoint AI features, or build our own?
Use a vendor's built-in AI when it does the job inside that one product. Build your own when the AI needs data from several systems, when your data can't leave your control, or when per-product AI fees add up. Some SaaS contracts restrict AI use. The assessment compares both options.
Do we need MCP, or will an API call do?
An API call is often enough. If one job needs one answer, such as checking an invoice inside an existing BI job, a single request to a language model works. MCP helps when several agents need the same systems, when internal APIs aren't documented, or when token costs climb.
How much does it cost to connect AI to our systems?
It depends on the number of systems, the APIs and documentation available, read or write access, approval steps, security rules, and request volume. We don't quote a number before we understand the systems. The assessment maps each one and turns that into an estimate per connector.
Can our own team maintain MCP servers?
Usually, yes. An MCP server is a thin layer over APIs your team already knows, and AI coding tools can help write new ones. Services we can provide include knowledge transfer, documentation of each connector and its permissions, and ongoing support.
