It works on the mock. Does it work on a real SAP?
If your team already shipped the MCP server — with Claude, in a week — and what you are missing is a real system to prove it on, we run it in our sandboxes and send back what broke, why, and the fix.
Your agent can’t get into the customer’s ERP.
We build the connectors that let it in.
We build the MCP connectors for your AI product — so your agents can read and act in your customers’ SAP, NetSuite, Salesforce and whatever else they run, with guardrails on everything that writes.
shipped into your integrations library
The problem
The hard part of an AI product
The hard part of an AI product
is everything it touches.
01
Your product can plan, summarize and decide. Then a customer asks it to check an invoice in SAP and there is nothing for it to call. The demo is impressive right up to the moment someone asks it to do real work in a real system.
02
One is on NetSuite, the next on Dynamics, the third has an on-prem SAP nobody has upgraded in years. Building a separate bridge for each of them turns your AI product into a services company, one customer at a time.
03
Hand a model your whole OpenAPI spec and it will find eighty ways to pick the wrong endpoint. What makes an agent reliable is a short list of well-named tools with tight inputs — and a write path that cannot double-post when the model retries.
You bring the agent — we give it something safe to call. Worth a read first: How to design MCP tools.
What we build
Fewer tools. Better names.
Fewer tools. Better names.
An agent that chooses correctly.
The difference between an agent that works and one that frustrates people is almost never the protocol. It is whether someone sat down and decided what the agent should be allowed to do, and what to call it.
Fixed
Price per system
Tool design first, then build and test against a real sandbox. Quoted before we start.
Read + write
With a safety net
Confirmation, dry-run and idempotency on anything that changes data in the customer’s system.
Per customer
Scoped sign-in
Your auth model, enforced so a single customer’s agent can only ever reach their own data.
What we connect to
The systems your customers actually run.
The systems we keep sandboxes for, which is why we can price them in a day.
SAP S/4HANA
Invoices, purchase orders, vendors, stock
SAP ECC
The on-prem systems nobody has migrated yet
SAP Business One
SMB finance, sales and inventory
SAP SuccessFactors
People, org data, time off
SAP Ariba
Sourcing, contracts, supplier data
Oracle NetSuite
Bills, records, saved searches
Oracle Fusion
ERP and HCM cloud modules
Dynamics 365 F&O
Finance, supply chain, manufacturing
Dynamics 365 BC
Business Central sales, stock and ledger
Salesforce
Accounts, opportunities, custom objects
HubSpot
Deals, contacts, tickets
Workday
People, absence, payroll
Coupa
Spend, invoices, suppliers
Rossum
Document capture and extraction
Xero
Invoices, contacts, bank feeds
QuickBooks
Invoices, payments, ledger
ServiceNow
Tickets, incidents, CMDB
Jira
Issues, projects, workflows
Snowflake
Reads for context and reporting
BigQuery
Analytics reads and exports
…and anything else with an API. If we can reach it, we can give your agent a safe way to use it.
Fixed quote · scoped up front · code in your repo
Need your agent talking to one of these? Click to book a call and we’ll price it.
What every MCP connector includes
A small set of tools named for what a user would ask, not for your endpoints
Input schemas tight enough that the agent cannot guess wrong
Sign-in per customer, scoped so each customer’s agent only ever sees their own data
A write path with confirmation, dry-run and idempotency — a retry never posts twice
Permissions mapped to the roles your product already has
Responses shaped for a context window, not a 400-row payload
Handling for the custom fields and non-standard objects every enterprise adds
Logs of every tool call: what was asked, what ran, what came back
Tested against real agent clients, plus a test suite your CI can run
Documentation, runbooks, and a handover call with your engineers
Pick the right service
Is the thing calling it a person, or an agent?
Build an MCP connector when…
Your product has agents in it. What happens next is decided at runtime by a model, not by a workflow someone configured. You need the system described in terms of what can be done, with guardrails on anything that writes.
That is this page.
Build an API connector when…
Your product syncs data, shows it on screens, and runs workflows people set up. The logic is yours and it is decided before anything runs. Most products need these first, and many ship both in the end.
How we work
From “the agent can’t do that” to shipped.
01
Design the tools
We go through what your users ask the agent to do, then work out the smallest set of tools that covers it. You approve that list before we write the connector.
02
Build it in your stack
Connector, sign-in, guardrails and logging — in your repo, tested against a real sandbox and against the agent client you actually ship.
03
Hand it over
Docs, runbooks, a test suite for your CI, and a walkthrough with your engineers. Eight hours of support after launch, included.
Compare your options
Four ways to get your agent into the customer’s systems.
| In-house | Generate from OpenAPI | AI dev shop | Inovaflow | |
|---|---|---|---|---|
| Time to ship | 3–6 weeks, learning first | An afternoon to a draft, weeks to something usable | 2–4 weeks | Weeks, not months |
| Tool quality | Gets better after real usage | One tool per endpoint | Usually shaped like the API | Designed around what users ask |
| Enterprise systems | Steep curve, one at a time | Only what the spec covers | Rarely ERP-capable | SAP, NetSuite, D365, Salesforce, Coupa |
| Write safety | Usually left till later | Not handled | Varies | Confirmation, dry-run, idempotency, logs |
| Who owns the code | You | You | Depends on contract | You — in your repo |
| When the protocol moves | Your problem | Regenerate and re-test | Depends on retainer | Adapter layer we maintain |
← scroll →
Deciding whether to build it yourself? Read MCP server: build or outsource.
Is this for you?
We are selective. You should be too.
Good fit
- Agentic B2B SaaS products whose agents need real enterprise data
- Teams shipping AI features that have to act, not just answer
- Products that have to work with SAP, NetSuite, Dynamics or Salesforce on the customer side
- Anyone whose next enterprise deal includes a security review
Not a fit
- Products with no agent in them — you want API connectors
- A build for a single customer — see custom MCP servers
- Chatbots over your own documentation
- Full-time embedded hires
Why teams choose Inovaflow
Agent design and enterprise systems, in one team.
We have shipped these, not just read the spec
MCP servers running in production over live products and enterprise systems — plus partner status with HiBob, Gong and Greenhouse.
Ours are already running in production
These tool sets run in production elsewhere, so we have watched real agents use them — which tool gets picked wrongly, where a retry creates a duplicate, what security review asks. You get the version that already survived all three.
Sandboxes already running
Live SAP, Oracle, Coupa, Workday and Dynamics 365 environments on our side, and we are not limited to those, so tool design starts on day one instead of after an access request.
FAQ
MCP connector questions, answered.
It is a connector in your product’s integrations library, built so your agents can use it. Same job as a normal connector — reach the customer’s SAP, NetSuite or Salesforce — but instead of a sync you defined in advance, it gives the agent a set of tools it can choose from while it works.
They are sister services and plenty of products ship both. An API connector moves data on a schedule or a trigger you decided at build time. An MCP connector exposes what the system can do and lets the agent decide at runtime. Underneath they often call the same endpoints; the naming, the shape of the responses, and the safety around writes are completely different.
Only if you have agents. If your product is deterministic — screens, syncs, workflows people configure — API connectors are the right tool and you can stop there. The moment a model is choosing what to do next, it needs an interface built for it.
Both, and where that line sits is a decision we make with you. Reads are simple. Writes get confirmation steps, a dry-run mode, idempotency keys so a retry never posts twice, and a full log of what the agent did. Enterprises ask about exactly this in security review, so we build the answer in rather than adding it afterwards.
Yours. The connector ships into your repository and runs inside your product, in your language and your conventions. Your customers connect their own system with their own credentials. Nothing of ours sits in the path.
Whatever your product uses — your own agent framework, or a hosted client like Claude or ChatGPT if your customers connect through those too. We test against the clients you actually ship with, because tool-selection behavior is not identical between them.
Usually a couple of weeks for the systems we already have sandboxes for, including the tool design at the start. That is working days — two days of work usually spreads across three or four calendar days once questions and reviews are taken into account. If you have a date to hit, our expedited add-on reserves the capacity and commits to calendar days instead.
It has moved more than once, most notably around transports. We keep the tool layer separate from the protocol layer, so a revision is a small adapter change instead of a rebuild. We wrote up the last big one: when MCP went stateless.
Let’s give your agent something to call.
Tell us which system your customers run and what you want the agent to be able to do. You get a proposed tool list, a delivery date, and a fixed quote — usually within 24–48 hours.
or email us directly — hello@inovaflow.io