Enterprise AI is powerful.It should also be accountable.
Enterprise AI adoption moved faster than enterprise control. As usage scaled, visibility didn’t keep up - creating blind spots in cost, usage, and accountability across the stack. Costs and usage data ended up scattered across multiple tools, making it harder for teams to see what AI was doing, let alone measure its value. The accountability layer enterprises needed simply wasn’t there - so we built it.
Datagras is the company behind SAVI - the accountability layer for enterprise AI. SAVI helps every team that touches AI - finance, engineering, operations, and leadership - understand what AI is costing, where it is being used, and what value it is delivering. It connects usage to teams, projects, and outcomes, turning AI spend into a governed, attributable part of the business rather than an unclear line item.
The challenge of governing enterprise AI is not a regional problem. It is a global one. Every organization scaling AI faces the same question: how do we stay in control without slowing down? SAVI was built to answer that - for every enterprise, under any regulatory environment, at any scale.
Up to $50K+
Potential cost prevented per runaway agent incident
Up to $1M+
Projected savings potential across design-partner pilots
100%
Governance actions covered by immutable audit trail
Founder, Datagras
I spent years watching enterprises talk about AI strategy while struggling to explain what it was costing them or what it was actually delivering. The visibility tools existed for cloud, for security, for compliance. But AI had grown into one of the largest line items on the P&L with almost no accountability layer beneath it. That gap felt too obvious to ignore. So we built the layer.
Where enterprise AI is going
AI spend will become the most scrutinized line item in the enterprise budget. Most teams have no system to answer for it yet.
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.
Where we started
Built governance-first.
Before anyone required it.
SAVI was built with governance at its core - shaped from day one by the organisations where accountability matters. Teams operate within clear boundaries, issues are surfaced the moment they arise, and every action is fully transparent. From cost and model usage to execution flow, every agent action is traceable. Sensitive data is protected. Comprehensive audit logs are securely maintained. This commitment to trust, visibility, and control defines SAVI.
Where the problem is now
What we built for one market
every market now needs.
The governance gap SAVI was built against is now a global reality. Every enterprise scaling AI now faces the same demand: full visibility into what AI costs, who authorised it, and what it decided - with plain-English explanations when something needs attention, and data that stays within the jurisdiction it belongs to. SAVI was built to that bar before most markets required it.
Design principles
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 UI does not govern anything. SAVI pushes alerts straight into Slack and PagerDuty, where your team is already working.
Precision before coverage
One detection you can trust beats ten you have to investigate. We raise the bar on signal quality before expanding what we monitor.
Customer-led, always
Nothing ships unless a design partner needed it in production. The roadmap is pulled by real operational requirements.
Change-tolerant by default
The AI ecosystem never stops shifting. New models, new providers, new regulations. SAVI reflects those changes in real time without requiring re-configuration from your team.
Built differently, on purpose
Transparency by design. Accountability in every decision.
Every action is explainable. Every alert is traceable. Every decision is backed by a visible rule. Built for teams that need full auditability, not assumptions.
We design where governance, transparency, and accountability intersect.
APRA, ACCC, and Australia's National AI Plan forced ANZ enterprises to solve AI governance before most of the world had to. We built for that bar first - which means everywhere else is easier.
Customer-led roadmap.
Every feature here exists because a design partner needed it in real production environments. Nothing is added for show - everything is built to solve real operational needs at scale.
What we're writing about
"Most enterprises have a cloud cost dashboard. A security posture score. A compliance report. But ask them what their AI is costing by team, by project, by outcome - and you get silence. That's the accountability gap we're closing."
June 2025
"Agentic AI changes the math entirely. A single runaway loop can consume what a whole team would spend in a month. Monthly billing reconciliation wasn't built for that. Real-time oversight isn't optional anymore - it's the baseline."
May 2025
"The teams that govern AI well don't slow down. They move faster - because they know what's working, what's wasting money, and where to invest next. Accountability is a speed advantage, not a compliance tax."
April 2025
See what your AI is actually costing you.
Most teams find their first $50K problem in the audit, not the demo. Let's find yours.