Sales
Deal status, contact history and next steps from the CRM, and updates logged after a call.
“Summarise the Harbor deal and log today’s call as a next step.”
MCP servers & integrations
Connect AI to the
systems you already run.
The Model Context Protocol (MCP) is the open standard that lets AI assistants read from and act on your business systems. We build MCP servers for your CRM, ERP, databases and internal tools, with the permissions and audit trail you need.
- Claude
- ChatGPT
- Microsoft Copilot
- Your local LLM
MCP server
Your tools, exposed safely
- Defined actions
- Per-user sign-in
- Approval for changes
- Audit log
- CRM
- ERP & accounting
- Databases
- Files & tickets
Why MCP
One connector.
Every assistant.
Before MCP, connecting an AI assistant to a system meant a custom integration for every pair. MCP standardises the connection: build one server for your CRM and any MCP-capable assistant can use it, from Claude and ChatGPT to a model running on your own hardware.
Build once
The same server works across assistants and developer tools, today and when you switch.
You decide what AI can do
Each action is defined, scoped to the user and logged. Nothing else is reachable.
Your systems stay put
No migration and no new database. The server talks to the tools you already use.
Use cases
Ask in plain English.
Get it from the source.
Typical requests by team. These are examples, not client results.
Finance
Invoices, payment status and customer balances from the accounting system.
“Which invoices over $5,000 are more than 30 days overdue?”
Operations
Stock levels, open orders and delivery dates from the ERP.
“Can we ship 400 units of SKU 1180 by Friday?”
Support
A customer’s ticket history, and new tickets created from a conversation.
“Open a ticket for this outage and link the last two incidents.”
Data
Read-only questions on your database, answered without writing SQL.
“Revenue by region last quarter, compared with the same quarter last year.”
Engineering
Coding assistants with access to your internal APIs, docs and issue tracker.
“Which service owns the /orders endpoint, and what changed in it last week?”
How it works
From your system to every assistant.
An MCP server describes a set of tools an assistant may call. The work is in choosing those tools well and making them safe.
-
Map the actions
We list what assistants should be able to read and do in each system, and separate read-only actions from anything that changes data.
-
Build the server
With the official TypeScript or Python SDK: tools, resources and prompts designed for how a model actually uses them, tested against your real system.
-
Secure it
OAuth 2.1 sign-in per user (with PKCE), least-privilege scopes, confirmation for actions that change data, rate limits and an audit log of every call.
-
Deploy it
Locally for desktop assistants, or as a remote server over HTTPS inside your network or cloud, monitored like any other service.
-
Roll it out
Connected to the assistants your team uses, with documentation for each tool and a short training session.
Security
Powerful access
needs firm limits.
An assistant that can act on your systems can also be misled, for example by instructions hidden in an email or a web page it reads. We design for that from the start, and test it as part of our security assessments.
Least privilege
Each tool gets only the access it needs, under the signed-in user’s own permissions.
A person approves changes
Actions that write, send or delete ask for confirmation first.
Prompt-injection testing
We try to hijack the assistant through the content it reads, and fix what we find.
Full audit trail
Every call is logged: who, which tool, what data, when.
MCP or a custom integration
Why a standard wins.
| Criterion | MCP server | One-off integration |
|---|---|---|
| Works with | Any MCP-capable assistant or agent | The one app it was built for |
| Switching AI vendors | Keep the server; connect the new assistant | Rebuild the integration |
| Permissions | Per tool and per user, with sign-in | Often one shared key |
| Audit | Every call logged in one place | Depends on each build |
What you get
Yours to keep.
- MCP serversWith the source code, in your repository.
- Security reviewIncluding prompt-injection tests and the fixes made.
- DeploymentLocal or remote, with monitoring and alerts.
- Tool documentationWhat each tool does, who may use it, what it can change.
- Assistant setupConnected to the assistants your team uses.
- RunbookHow to add tools, rotate keys and review logs.
Technology
Built on the open standard.
Protocol & SDKs
- Model Context Protocol
- TypeScript SDK
- Python SDK
Transports & auth
- Streamable HTTP
- stdio
- OAuth 2.1 with PKCE
Assistants
- Claude
- ChatGPT
- Microsoft Copilot Studio
- VS Code
- Cursor
- Local LLMs
Hosting
- Your servers
- Docker
- AWS
- Azure
- Cloudflare
When MCP isn’t
the right tool.
- You need one fixed report every Monday. A scheduled export or a dashboard is simpler and cheaper.
- The system has no API and no database access. We look at exports first. Automating the screen is a last resort.
- Nobody uses an AI assistant yet. Start with the assistant and the habit, then connect your systems.
How we work together
Start small.
Decide on facts.
Every project goes through the same three stages. You decide at the end of each one whether to continue, and you can stop after any stage and keep everything built so far.
-
Stage 1
Assessment
We look at your process, your data and your systems, then tell you plainly whether AI is worth it, which approach fits, and what it would cost to build and run.
You get a written recommendation with scope, timeline and cost.
-
Stage 2
Pilot
A working version on your real data, used by a few of your people, with results measured against questions and cases from your own business.
You get a working pilot and an evaluation report.
-
Stage 3
Production & support
We secure it, connect it to your systems, roll it out to your team and keep it running: monitoring, model updates and improvements.
You get a production system, documentation and ongoing support.
Yours to keep
- Source code, in your own repository
- Prompts, configuration and infrastructure setup
- Test sets and evaluation reports
- Fine-tuned model weights, if any
- Documentation and runbooks
Not included in our fees
- Hardware, if you buy or rent GPUs
- Cloud hosting
- AI model usage, if you use a cloud API
- Third-party software licences
We list them in the assessment, so the full cost is clear before you commit.
Questions
What is MCP, in one sentence?
The Model Context Protocol is an open standard, introduced by Anthropic in 2024, that defines how AI assistants discover and call tools and data sources, so one connector works with many assistants.
Which assistants support it?
Claude, ChatGPT, Microsoft Copilot Studio, VS Code, Cursor and a growing list of other assistants and agent frameworks, including ones that run open-source models on your own hardware.
Can an assistant change or delete data on its own?
Only if you allow it. We start read-only, and actions that change data ask the user to confirm. You choose, tool by tool, what needs approval.
Which systems can you connect?
Anything with an API or a database: CRMs, ERPs, accounting software, ticketing tools, file storage and in-house applications. For systems without an API, we look at exports or a database connection.
Does it work with a private, local model?
Yes. MCP does not depend on any AI vendor. Combined with an open-source model on your hardware, neither your data nor your tool calls leave your network.
Do we have to replace our current tools?
No. The MCP server sits in front of your existing systems and uses their APIs. Nothing is migrated.
Other AI solutions
Start with
a conversation.
No call needed to start. Send a short brief and we’ll reply in writing with what we’d do and what it would take.
Houston, Texas · Working with clients worldwide