Mirai is a horizontal enterprise AI platform for building, operating and governing intelligent systems that improve business performance, moving enterprises from AI experimentation to production.
Trusted & built by operators at leading firms
The Problem
Employees use ChatGPT, Claude and copilots. Most enterprises are piloting agents. Yet work still moves through people crossing ERP, CRM, spreadsheets and email, and AI is making individuals faster without changing how the organization performs.
Getting a model to answer well is a demo. Getting it to read your ERP, act inside your CRM, respect your approvals, survive an audit and run reliably every day is the actual work.
What most enterprises end up building
Use case 1
Use case 2
Use case 3
The Mirai Platform
Intelligent outcomes
Your enterprise systems
The same layer, whichever function you start with.
Why Mirai
Every use case starts further ahead than the last.
Exceptions resolved by people are captured as context. The next system starts with what the last one learned. A rule set in collections is there for reconciliation.
Governed once, not project by project.
Every agent and workflow runs under the same identity, permissions, evaluations and audit trail, instead of one AI stack per function, each with its own gaps.
You never start from a blank screen.
Agent patterns, integrations, approval mechanisms and governance controls ship with the platform. The first deployment configures what exists rather than building it.
People who know the process, not just the model.
Your workflows carry years of rules and exceptions. Domain experts and AI engineers adapt the platform to how your operation actually runs, not how it is documented.
Where To Begin
01
Pick the problem where better context or faster decisions moves a number you already report. Live in production in weeks, measured on that number.
02
The second workflow reuses what the first one established, so it lands faster and costs less to stand up.
03
Finance, operations and service stop solving the same problems separately. Each system starts better informed than the last.
04
AI is no longer a project alongside the work. It is how the work runs, with the controls and reporting your business already expects.
In Their Words
They asked about our patient flow before they said a word about AI. Six weeks in, our unit heads had dashboards they actually open every morning, not because we mandated it but because the insight is useful. The quality of the team showed in the questions they asked.
Our close used to consume the first week of every month. We are saving close to 20 man days a month now, and that time goes to review and strategy instead of reconciliation. What I valued most was the transparency. We always knew what was working, what was not, and why.
They rebuilt our outbound motion in six weeks and the funnel improved 2.5x. The talent density is unusual. The people who scoped it were the people who built it, and they told us plainly when an idea of ours would not work.
Questions
Your data stays within your environment and your contractual boundary. It is not used to train foundation models, and it is not pooled across customers. Access is governed by the same identity and permission rules as the rest of your stack, and every retrieval and action is logged.
Yes. The platform can be deployed in your cloud tenancy or ours. Which model you use is a configuration choice rather than an architectural one, so you can run frontier models, models hosted in your own region, or private models, and change that decision later without rebuilding the workflows on top.
Weeks rather than quarters, because the first deployment configures agent patterns, integrations, approval mechanisms and governance controls that already exist rather than building them. The honest variable is your side: how accessible the source systems are, and how quickly the people who own the process can give us their exceptions and rules.
One workflow that matters, access to the systems it touches, and time from the people who actually run the process. You do not need a data lake, a completed migration, or an in-house AI team. If your data is messy, that is normal and it is part of the work rather than a prerequisite for starting it.
A copilot makes one person faster at their task. It does not read your ERP, act inside your CRM, respect your approval chain, or carry an audit trail. Mirai operates at the level of the process rather than the individual, which is why the measurable change shows up in a business number rather than in time saved per person.
Pricing has two parts: a platform subscription, and the deployment work for each use case. The second falls sharply after the first workflow, because the integrations, context and controls are already in place. We scope the first engagement against the business number you are trying to move, so you can judge it on return rather than on rate.
We stay accountable after go-live rather than handing over and leaving. Over time your own teams can take on as much of the operation as you want, and because the workflows, rules and context live in the platform rather than in our heads, taking it further in-house does not mean rebuilding it.
Then we will tell you. Some problems are better solved by fixing a process, a rule or an integration, and saying so early costs you less than discovering it three months into a deployment.
Start with one problem where better context, faster decisions or fewer handoffs would move a number you already report. We will tell you honestly whether AI is the right answer for it.
Talk to us