Transform
Let AI assistants work with your systems, safely
We make your custom and older software agent-ready with MCP, the open standard AI tools use to connect to business systems. Your team's AI assistants and agents get the actions they need, with permissions, audit trails, and limits you control.
AI assistant
Your systems
Your AI tools can't reach the systems that matter
Your core system has no connector.
Popular SaaS tools ship AI connectors. Your in-house claims, policy, or lab system never will.
Exposing everything is risky.
Wiring an AI agent straight into every API endpoint gives it more access than any employee would have, with no record of what it did.
Workarounds leak data.
When AI can't reach the system, people copy and paste records into chat tools instead, outside any control.
What we do
Make your software agent-ready
MCP (Model Context Protocol) is an open standard that lets AI assistants and agents use business software through a defined set of actions, such as “look up a policy” or “create a support ticket”. We build MCP servers for the systems no vendor will cover: your custom, in-house, and older applications. We agree with you what the AI should be able to do, design a small set of clear actions for it, and put security around every one.
- A short list of well-designed actions, not every endpoint you have
- Access rules per user, role, and action, read-only by default
- Identity checks before anything touches personal data
- An audit log of every request: who asked, what the AI did, and what came back
- Rate limits and safeguards so an agent can't overload or misuse the system
- One working agent use case on top, so you see value from the first release
What you get
Agent-ready integration (MCP server)
A secure server that exposes the agreed actions of your system to AI tools that support MCP.
Action design document
Which actions exist, what each can read or change, and who may use them.
Security layer
Authentication, permissions, identity verification, rate limits, and audit logging.
Test suite
Automated tests, including tests that prove one user or client cannot reach another's data.
A working agent use case
For example, an internal assistant that answers staff questions from your system, or a customer agent that checks a status after verifying the customer.
Handover and runbook
Documentation for your team, or ongoing operation through Managed Services & Support.
How it works
- 1About 1 week
Map
Choose the system and the jobs AI should help with. Agree what it may read, what it may change, and what stays off-limits.
- 2
Design the actions
Turn those jobs into a small set of clear actions with defined inputs, outputs, and permissions.
- 32–4 weeks
Build and secure
Build the MCP server, connect it to your system's API or database, and add authentication, audit logging, and limits. If the system has no usable API yet, we add one first.
- 4
Prove it with a real agent
Connect an AI assistant or agent, run it on real tasks, and test what happens when it is asked to do something it shouldn't.
- 5
Go live and monitor
Release with monitoring, then add actions as use grows.
Typical duration: 3–6 weeks for one system, depending on whether it already has a usable API.
Built on integration work we already run in production
The skills behind this service, connecting AI and other software to business systems with authentication, identity checks, and isolation between clients, are already in production in our own platform and in client systems.
3 / 5
Client tenants and agents running on one AI agent platform, with cross-tenant access denied by tests
Seeded environments and test suite
1,297
Conversations served on the first production deployment, an insurer's public support channel, late January to mid-September 2026
Production snapshot
End to end
IVR request to SMS link to customer session proven, with idempotent delivery and replay protection
End-to-end tests, August 2026
1,400+
Automated tests across 12 backend modules and about 100 API endpoints on our AI agent platform
Source
Case study
one2one AI Support Agent Platform
Turns any business API into an action an agent can take, each with its own authentication and scoped per client, with a one-time code check before personal data.
Case study
Smart IVR
An authenticated, IP-allowlisted API for a telephony provider, with idempotent delivery and replay protection.
Case study
Health Insurance Claims Administration Platform
Exchanges policy, claim, and customer data with insurers' core systems through a middleware API.
Why Biznovelty
We build agents, not just connectors.
Our own AI agent platform calls business systems in production, so we design actions from the agent's side as well as the system's.
Security comes first.
Scoped credentials, identity checks before personal data, and isolation that is tested automatically.
We know the systems behind the API.
We build and run insurance, claims, and field operations platforms, the kind of systems that most need careful AI access.
One team from integration to operation.
The same team can modernize the system underneath, build the agent on top, and run both.
Engagement & pricing
Fixed scope per system
Quoted after the mapping step, covering the agreed actions and one agent use case.
Not sure where to start?
The AI Readiness Assessment identifies which systems and actions are worth connecting first.
Already running a system we built?
Ask about adding agent access to it.
Ongoing operation
Monitoring, new actions, and model changes through Managed Services & Support.
Frequently asked questions
A common language AI tools use to talk to software. Instead of building a separate connection for every AI tool, you build one MCP server for your system, and any assistant or agent that supports MCP can use the actions you allow.
Pick one system and one job for AI
In a 30-minute call we'll tell you what it would take to make that system agent-ready, and what AI could do with it first.