Enterprise AI is powerful. It should also be accountable.
Datagras builds SAVI, so finance, engineering, operations and leadership can see what AI costs, where it is used and what it delivers.
Why Datagras exists
Visibility fell behind
AI adoption moved faster than control. Cost and usage data ended up scattered across tools, so teams struggled to see what AI was doing, let alone measure its value.
One accountability layer
Datagras is the company behind SAVI. It connects AI usage to teams, projects and outcomes, so finance, engineering, operations and leadership work from the same facts.
Not a regional problem
Every organisation scaling AI asks the same thing: how do we stay in control without slowing down? SAVI is built to answer it, for any enterprise, at any scale.
Where enterprise AI is going
AI spend will become one of the most scrutinised lines in the enterprise budget, and teams will need a clear way to answer for it.
Monthly bills and spreadsheet reconciliation will not survive agentic AI at scale.
Accountability does not slow innovation. Teams that govern AI well move faster because they know what is working.
Shadow AI is a visibility problem. You cannot govern what you cannot see.
Built governance-first.
Accountability by design.
SAVI was built with governance at its core. Teams work within clear boundaries, issues surface as they happen, and every agent action can be traced, from cost and model use to execution flow. Sensitive details in prompts are flagged, and a signed log records governance changes.
What we built for one market
every market now needs.
The governance gap SAVI was built for is now global. Every enterprise scaling AI faces the same demand: see what AI costs, who authorised it and what it decided, with plain-English explanations when something needs attention.
How it is built
Auditability over abstraction
Every decision SAVI makes traces to a signal, a rule or a threshold. No black boxes. Compliance teams can follow the chain from alert to action.
Real integrations, not dashboards
Governance that stays in its own screen does not govern anything. SAVI sends alerts straight into Slack and PagerDuty, where your team already works.
Precision before coverage
One detection you can trust beats ten you have to investigate. We raise the bar on signal quality before we widen what we monitor.
Customer-led, always
We build what customers need in production. The roadmap is pulled by real operational requirements, not by what looks good in a demo.
Built for change
The AI ecosystem keeps shifting, with new models, new providers and new rules. SAVI is built so your team does not have to start over each time.
Built differently, on purpose
Transparency by design
Every alert is traceable and every decision is backed by a visible rule, so teams that need auditability are not left with assumptions.
Governance first
We sit where governance, transparency and accountability meet. Our home market, Australia, sets a high bar for AI oversight, and we build to it.
Customer-led roadmap
Every feature exists because a customer needed it in a real environment. Nothing is added for show.
Enterprise AI has grown into a major line item, and the tools that give teams visibility into cloud, security and compliance did not cover it. That gap felt too obvious to ignore, so we built the accountability layer for AI.
See what your AI is actually costing you.
Book a spend audit and we will show you where your AI spend goes, team by team.