From a pilot that works to a service that runs.
Move the AI initiative your business already believes in from a working pilot to a governed production service, reusing the integrations, the data access and the deployment lane the last one built.
Today, the pilot is the easy part.
With Mia-Platform, production is the expected part.
The pilot worked. Then it stalled on integration with core systems, on data access, on security review and on the IT backlog, where it has been waiting since.
The integrations, the governed data access and the deployment lane already exist, so the initiative meets a path instead of a queue.
Each new initiative restarts from zero, with a new vendor or a new integrator, and nothing the last one built is there to pick up.
Each initiative inherits the components the previous one left behind, so the second one costs less and lands sooner than the first.
Initiatives multiply without the organization transforming: a portfolio of demos that never pay back, while competitors reach the market first.
Initiatives compound into a capability, and the list of what runs in production grows instead of the list of what was tried.
A lane that is already there when the pilot is ready.
Five steps, and the last one is the point: what this initiative builds is what the next one starts from.
Onboard
The pilot gets an owner, an identity and a place in the catalog, so it stops being a project running beside the platform and starts being part of it.
Connect
It reuses the integrations with core and legacy systems that are already on the platform, with data access that is governed rather than granted by exception.
Promote
It reaches production through the same deployment and runtime path as everything else, with the approval gate that path already has.
Govern
Compliance and cost control are part of the lane: the initiative has a budget and an audit trail from day one, not after the first invoice.
Reuse
What this initiative built stays on the platform as components, so the next one starts from them instead of from a blank page.
What the lane gives the initiative.
Four things a pilot normally has to build for itself, and then leaves behind when it stops.
Reusable integration with core and legacy systems
The connection to the ERP, the policy system or the mainframe is built once and catalogued, so the next initiative that needs it calls it instead of negotiating for it.
Governed data access
The agent reads the data it needs through access rules that are declared on the data itself, so the security review has something to review rather than a new exception to approve.
Deployment and runtime on the platform
The initiative goes live on the same golden path, environments and approval gates that the rest of your software already uses, instead of on infrastructure invented for it.
Built-in compliance and cost control
Budget, audit trail and the obligations that come with putting AI in front of customers are part of the lane from the first day, not a project bolted on before go-live.
Why the usual answers stop short.
Every route to production works once. The question is what the second initiative inherits from the first.
Each row is the same job: the answer most organizations reach for, and what changes here.
An agent builder inside an ecosystem you already buy from. Fast, until the process crosses a system that vendor does not run.
Across your systems
The lane reaches the core and legacy systems the process actually touches, wherever they run.
A bespoke project, scoped to this initiative and delivered as a one-off.
Compounding, not bespoke
What this initiative builds becomes the foundation the next one starts from, under your IT's governance.
A point solution that owns a slice of the process, and the data that goes with it.
Your platform, your data
The service runs on the platform you own, and the data stays where your governance already applies.
A decade of acquisitions had left Helvetia Italy Group with a fragmented landscape where integrating a single new channel took six to nine months. Decoupling data from the core systems, then composing new services as reusable assets, turned that into a lane the business could use again and again.
Reported by the customer
9
AI use cases live across Claims, Non-Life and Life
-30%
time-to-market
-18%
average project costs
What you will be able to measure.
Two numbers belong to the platform and one belongs to the initiative. All three are measured on your own baseline.
Speed
How long the crossing takes.
- Time from pilot to production
Scale
Whether the second one is cheaper than the first.
- AI initiatives live in production
- Share of components reused across initiatives
Business outcome
The number the initiative was started for.
- Handling time, conversion, or whatever the business case promised
We publish the indicators, not an average improvement: the numbers above the section come from one named customer, and yours will come from your own baseline.
What makes it work.
Four parts of the platform carry this use case. The rest of it is there when you need it.
AI Foundry
Gives the pilot agent an identity, a Playbook and limits, so what goes live is something you can describe.
Context Catalog
Holds the components the initiative reuses, and the governed access to the data it reads.
AI-Ready API
Exposes core and legacy systems as governed endpoints an agent can call, instead of a new integration per initiative.
Console
Carries the deployment path, the environments and the approval gate the initiative goes live through.
The objections this raises, answered.
It is not an additional technology to adopt. The initiative runs as a new lane on the same platform IT should be running for software delivery anyway, under the same governance, the same environments and the same approval gates. In practice the business case for the initiative helps fund the foundation IT already wanted.
Where this goes next.
Scale Agentic Engineering
Turn your standards and knowledge into rules every AI agent follows, and prove the investment pays off.
ExploreRun the AI-Native SDLC
Agents working through every stage of delivery, under guardrails your platform team controls.
ExploreAI Governance
Control AI spend, risk and compliance at the point where AI runs.
Explore