One customer’s ERP breaks your MCP connector.
We build the MCP server for that one account.
Your agent already works for most customers. Then one arrives with a heavily customized SAP, approval logic nobody else has, or a system that is not in your library at all. We build the MCP server for that account — or the extra tools inside the one you already have — without changing what everyone else gets.
sap ecctheir us plant
netsuitetheir finance
dynamics 365their eu entity
oracle fusiontheir procurement
Their AI mandate just became your deadline.
Your connector works everywhere else. Then this account turns out to run an SAP nobody has upgraded in years, with three approval steps and a pile of custom fields the standard tools know nothing about. The agent either cannot see the data or quietly gets it wrong.
Someone on your team starts hand-writing tools for that one account. It works. Then it drifts away from the product, and when it breaks six months later nobody remembers how it was put together.
The custom matching rule, the non-standard object, the extra approval step — none of that should end up in the connector every other customer depends on. But your agent still has to work for this account, and probably before the renewal.
You say yes to the customer; we build the tools. Worth a read first: How to design MCP tools your agent won’t misuse.
Custom-built MCP servers.
The tools this account needs.
What every custom MCP build includes.
A single customer, or your whole product?
Build it for one account when…
That customer’s system or process is genuinely different — customizations, unusual approval logic, or a tool nobody else runs. Putting it in the shared connector would push one account’s rules onto everyone.
You are on the right page.
Build it into the product when…
The behavior is the same for everyone on that system. It should be built once, documented, and maintained as part of your product rather than re-quoted per account.
From AI request to working agent.
Design the surface with the customer
We work out what their team will actually ask for, and which of those should be able to write. Both you and they approve the tool list before we build.
Build, guard and test
Server, tenant-scoped auth, guardrails and audit trail, tested against the assistant clients that customer uses. Security pack drafted alongside.
Ship under your name
Deployed in your infrastructure, connected in their client, documented for your support team. Eight hours of post-launch support included.
Who should build the tools this account needs?
| Your own team | AI dev shop | Let the customer build it | Inovaflow | |
|---|---|---|---|---|
| Start date | Competes with the roadmap | 1–3 weeks to find and brief | Their queue, their pace | Within days |
| Knows MCP and their ERP | One side, usually | MCP yes, ERP rarely | Their systems, not your product | Both — that is the job |
| Stays out of your shared connector | Tends to drift into product code | Depends who reviews it | Out of your hands entirely | Isolated by design |
| Write guardrails | Usually left till later | Varies | Their rules, not yours | Confirmation, dry-run, audit trail |
| Security review | Your team writes the pack | Rarely included | Their standards, unknown | Review pack included |
| Cost model | A sprint plus opportunity cost | Hourly, and it drifts | Free for you — but you lose control of it | Fixed quote |
Background reading: Custom MCP servers that connect business systems.
We are selective. You should be too.
Good fit
- B2B SaaS vendors with a single customer pushing an AI or agent initiative
- Strategic accounts where saying yes quickly protects a renewal or expansion
- Use cases with real actions, not just a read-only demo
- Situations that will face an enterprise security review
Not a fit
- A connector every customer can use — see MCP connectors
- Classic data integration with no AI layer — see custom integrations
- Speculative AI requests where nobody can name a workflow
- Building a chatbot inside your own UI
Shipped the MCP server already, but it keeps breaking in production?
You do not need it rebuilt. You need it run against a real ERP by people who know where agents go wrong. We validate your server in our sandboxes and send back a report of what broke, why, and how to fix it.
- Every tool run in our real, seeded SAP, NetSuite, Dynamics, Coupa and Salesforce sandboxes
- Agent-specific checks: wrong tool choice, invented fields, double writes on retry
- A report of what broke, why and the fix, ranked by severity. One retest included
Agent design and enterprise systems, in one team.
Production MCP servers, not pilots
We have shipped agent tooling over live products and enterprise systems, and hold partner status with HiBob, Gong and Greenhouse.
We answer the security questions
Tenant scoping, credential handling, retention and audit come as a written pack — because that is where enterprise AI projects usually stall, not in the build.
Both halves in one team
Most AI shops have never touched an ERP; most ERP shops have never designed a tool surface. This work needs both at once.
Custom MCP questions, answered.
An MCP server — or a set of extra tools inside the one you already run — built for a single named customer’s system, so your agent can work there properly. It is shaped around that account’s process and stays scoped to their tenant.
A connector is built once and behaves the same for every customer on that system. This is for the account whose setup is not like the others — custom approval logic, an unusual system, customizations the shared connector should not carry. If the behavior should be identical for everyone, it belongs in the connector instead.
Often that is the cleanest option. We add customer-specific tools alongside your existing ones, gated so only that tenant sees them, and keep them separate enough that your shared tool set stays clean.
Yes, with guardrails we design deliberately: confirmation on anything destructive, a dry-run mode, idempotency keys so a retry never posts twice, and a full audit trail. We agree what the agent may write with you and the customer before building, because that conversation is the risky part, not the code.
That is a real part of the work. You get a pack covering data flow, tenant scoping, credential handling, retention and logging, written for a security reviewer rather than a developer. Enterprise AI requests stall in review far more often than in build.
Then it should stop being customer-specific. We build with that in mind — the account-specific parts stay separate from the parts that would work for any customer — so moving it into your shared connector is a scoped piece of work rather than a rewrite.
Yours. It ships into your repository and runs inside your product, in your language and conventions. The customer connects their own system with their own credentials. Nothing of ours sits in the path.
Usually a couple of weeks once the tool design is agreed, though it depends entirely on how unusual the customer’s setup is. We scope it properly first and give you a fixed price before anything starts.
Say yes to the AI request.
Tell us which customer is asking and what they want their assistant to do. You get a proposed tool surface, a delivery date, and a fixed quote — usually within 24–48 hours.
or email us directly — hello@inovaflow.io