AI portfolio
Bring every investment into view before judging its return.
Security & architecture
Govern AI without
sending your
data to us.
Governe’s control plane and runtime plane run single-tenant inside your environment. Requests are inspected and governed locally before reaching an approved AI provider.
Data handling
What our control plane sees — and what it never does.
| Data | Reaches Governe control plane? | Notes |
|---|---|---|
| Prompts, completions, embeddings, documents | Never | Exist only inside your single-tenant runtime plane and the model providers you route to. |
| Classification findings (e.g. "38 PII fields detected, blocked") | Yes | The finding travels; the text that produced it does not. This is what makes reporting possible without exfiltration. |
| API keys & credentials | Hashes only | Raw keys never leave your environment; revocation propagates in ≤ 60 seconds. |
| Usage & cost telemetry | Aggregates, async | Buffered locally (durable queue), batched upstream; survives network loss. |
| Inventory metadata (app names, owners, models, vendors) | Yes | The minimum needed to attribute activity and report on it. |
| Policy definitions & audit events | Yes | Signed policy travels down; enforcement decisions are logged for evidence. |
| Training data for our models | Never | We do not train on customer data. Contractually. |
Design principles
Built for the failure cases.
Inspection is local by construction
Classification models and detection rules run inside your single-tenant runtime. There is no code path that ships prompt or document content to our control plane — not for analysis, not for debugging, not for model improvement.
Runtime never calls home in the request path
Every enforcement decision is made locally from signed policy snapshots. Governe cloud latency — or an outage — can never slow or block your AI traffic.
Offline-first, fail-safe by policy
Intermittent or air-gapped networks enforce from cache with a configurable staleness window — fail-closed or allow-with-audit, your choice.
Bounded overspend via budget leasing
Budgets are enforced as leases, so even a disconnected runtime cannot exceed its granted allowance.
≤ 60-second revocation
Compromised keys and revoked agents propagate to every enforcement point within a configurable SLA of one minute.
Evidence by default
Every policy change, approval, and enforcement decision is recorded — versioned, backward compatible, ready for auditors.
Deployment
Run it where your risk profile says.
Recommended for most enterprises
Customer-deployed
- Full data residency and control — Kubernetes, VM, or bare metal
- Works offline from cached, signed policy
- You operate the gateway in your VPC or data center
- Local storage for audit buffer (SQLite/Badger)
Managed
Governe-operated · single-tenant
- We operate the gateway for you — same architecture, same guarantees
- Deployed in your isolated, single-tenant VPC
- Encrypted storage, Kubernetes-managed
- Same boundary: single-tenant, and your content never reaches our control plane
Why not the tools you already have?
Three categories. Three blind spots.
Each of these solves a real problem. None of them answers "what data went to which model, sent by whom, and was it worth it?"
DLP & CASB
They watch the network.
Built for files and web sessions, not prompts. They can tell you someone visited a chatbot — not that they pasted 1,240 customer records into it, or that an agent did it 400 times an hour.
No visibility inside the promptAI gateways & routers
They enforce traffic.
Routing, guardrails, and budgets — but only for the requests developers deliberately send through them. The SaaS copilot your marketing team turned on last Tuesday never touches it.
Blind to everything outside the gatewayGRC & governance platforms
They document policy.
Registries, frameworks, and attestations — a careful record of what AI should do, assembled from what people remembered to declare.
No evidence of what actually happenedGoverne does discovery and enforcement — on the same record.It finds AI nobody registered, inspects what those requests actually carry, blocks what shouldn't leave, and prices the rest. Because it runs single-tenant in your network, seeing inside your prompts never means shipping them to us.
Straight answers
What a skeptical CISO asks next.
How can you classify our prompts without reading them?+
Because the part of Governe that reads them runs on your infrastructure, not ours. Every deployment is single-tenant inside your network. Inspection, PII and IP detection, redaction, and enforcement all happen there. What crosses to the control plane is the finding — "1,240 PII fields, blocked, Support team, ChatGPT" — never the text that produced it.
How do you see AI we don't route through you?+
Three ways, layered. Identity and SSO signals show which AI services people have authenticated to. Network egress and SaaS admin data show which AI vendors are receiving traffic. Expense and billing data catch the tools bought on a card. Anything you route through the gateway gets full content-level inspection; everything else still gets discovered, attributed, and priced — so nothing sits entirely outside the record.
Is this just another AI gateway?+
The gateway is one enforcement point, not the product. A gateway only governs what developers choose to send through it. Governe's job is the whole estate — including the SaaS copilots, embedded vendor AI, and personal accounts that will never touch your gateway — and then pricing all of it against what it returned. If you already run LiteLLM or similar, Governe sits above it rather than replacing it.
Does it slow down or break our AI traffic?+
Enforcement decisions are made locally from cached, signed policy — there is no call to Governe in the request path, so our latency or availability can never affect yours. You start in observe-only mode and see what would have been blocked before anything actually is.
We already have DLP. Doesn't that cover this?+
DLP was built for files moving across a network boundary. It can see that a browser session went to a chatbot; it generally can't see the 1,240 customer records inside the prompt body, can't tell an employee from an autonomous agent, and has no concept of which model or vendor is on the other end. It also has nothing to say about cost or return.
What about agents that act on their own?+
Agents are treated as first-class actors with their own identity, credentials, human owner, and approval record. You can see which agents can act autonomously, what data each has reached for, what it sent onward, and who signed off. An agent without a named owner is itself a finding.
What if our network is intermittent or air-gapped?+
Enforcement is offline-first. The runtime works from cached, signed policy with a configurable staleness window and fail-closed or allow-with-audit behaviour, your choice. Telemetry buffers locally in a durable queue and retries.
How fast do we see something real?+
The 30-minute assessment produces your first exposure map. Read-only discovery live in about a week; content inspection and first policies in roughly two; full estate coverage across teams and providers in four to six.
Compliance status — stated plainly
We publish our posture honestly rather than borrowing badges. Controls are designed against SOC 2 and ISO 27001; HIPAA-ready deployment patterns are supported. Certification status below is kept current — ask us for the detailed control mapping under NDA.
SOC 2 — aligned · audit in progress
ISO 27001 — aligned
HIPAA — deployment-ready
No training on customer data — contractual
Security contact: security@aiebo.ai · We welcome coordinated disclosure.
Leadership overview
See what’s changing. Know what needs attention.
Compare AI adoption, spend, budgets, and policy events across business units, projects, models, and vendors.
AI spend$184.6K of $237K78% of budget · on track
AI requests12.4M↑ 18% adoption
Protected events1.64MRedacted or blocked
AI spend by business unitTHIS MONTH
Business unitProjectModelVendor
Attention needed2 ITEMS
BudgetSales Copilot may exceed budget by 14%.
Premium-model usage increased 28%.
Policy42K requests blocked before reaching a model.
Most originated from three unapproved tools.
Traffic policy status12.4M requests
87% allowed12.7% redacted0.3% blocked
Illustrative figures. Your operating view is built from your own traffic.
AI ROI
Make AI investment defensible.
Governe connects request-level cost and observed adoption to outcomes your business defines. Leaders can distinguish measured evidence from assumptions—and decide where to scale, optimize, or stop.
See the measurement framework ↓01
CostMeasured by project, model, vendor, and request
DIRECT02
AdoptionObserved by team, workflow, and frequency
DIRECT03
OutcomeConnected to business-defined results
INSTRUMENTEDScaleFund what is working
Identify projects with sustained adoption and credible business impact.
OptimizeCorrect inefficient usage
Find model, vendor, and workflow choices that raise cost without improving results.
StopChallenge weak investment
Surface spend with low adoption, unclear ownership, or no measurable outcome.
Three layers
Not all of ROI is equally knowable.
Treating a hard number and a soft estimate as the same thing is how AI business cases lose credibility with a CFO. Governe keeps them separate on purpose.
LAYER 01
Cost
Directly observable, to the request. Every call carries a model, a team, an application, and a price.
- Spend by team, application, model, and vendor
- Token and request volume over time
- Card and expense spend reconciled against providers
- Duplicate capability across vendors
LAYER 02
Adoption
Also directly observable, and the number most business cases quietly stop looking at after launch week.
- Active users by week, not cumulative signups
- Month-three retention per application
- Depth of use: occasional vs. embedded in workflow
- Abandoned initiatives still being billed
LAYER 03
Outcome
Where honesty matters most. Governe instruments the metric you nominate per initiative and tracks it against cost — it does not invent one.
- You define the metric: handle time, cycle time, deflection rate, error rate
- Governe tracks it alongside spend and adoption
- Baseline and post-deployment comparison
- Explicitly flagged when no outcome metric was ever set
The portfolio view
Every line item should end in a decision.
Not a dashboard someone screenshots for a slide. A defensible call, with the numbers attached.
Measure what is knowable
Keep observed facts separate from business assumptions.
CostDirectly measuredAdoptionObserved over timeOutcomeYou define the metric
Act on the evidence
Every line item should resolve to a defensible action.
ScaleProven valueConsolidateDuplicate capabilityStopNo justified return
What we will and won't claim.
Worth reading before you evaluate anyone in this category, including us. The gap between these two columns is where most AI ROI reporting quietly falls apart.
We will tell you
Exactly what you spent, by team, model, vendor, and application — reconciled against what was actually billed, including expense-card spend nobody attributed.
We won't tell you
That AI saved your organization "the equivalent of 40 FTEs." That number requires assumptions only you can make, and stating it as a product output would be dishonest.
We will tell you
Which initiatives still have real users in month three, and which quietly died while the invoice kept arriving.
We won't tell you
That a generic industry benchmark applies to your workflows. We'll show you your own baseline instead.
We will tell you
When an initiative has no outcome metric attached at all — which is itself one of the most useful findings in the first month.
We won't tell you
A single blended "AI ROI" percentage for the whole company. It would average away the exact differences the number is supposed to inform.
Straight answers
What a skeptical CFO asks next.
How do you measure ROI without making it up?+
We measure what's measurable and are explicit about the rest. Cost, usage, and adoption are directly observable — spend per team, per model, per application, and whether people still use the thing after month two. Business outcome is instrumented against metrics you define per initiative. Governe won't invent a productivity multiplier; it makes the real numbers impossible to avoid.
How do you capture spend that never went through procurement?+
Three sources, reconciled. Provider billing APIs for the accounts you know about. Expense and card data for the ones you don't — AI spend on a corporate card is one of the most common blind spots we find. And identity signals showing which AI services people have authenticated to, which surfaces paid tools nobody expensed correctly.
We already have a FinOps tool. Why isn't that enough?+
FinOps tools tell you what a workload cost. They can't tell you what data that workload sent to a frontier model, whether an autonomous agent generated the spend, or whether anyone still uses the application. Governe holds cost on the same record as content and adoption — which is what turns a spend report into a decision.
Can we attribute cost to a project or budget rather than a team?+
Yes. Budgets are enforced as leases against whatever dimension you care about — project, cost centre, team, application, or agent. Because it's a lease rather than a report, a disconnected runtime still can't exceed its granted allowance, so the budget is a control rather than a postmortem.
How long before the ROI picture is trustworthy?+
Cost attribution is meaningful within the first billing cycle. Adoption needs a quarter to say anything honest about retention. Outcome depends on you nominating a metric — teams that pick one in week one have something defensible by end of quarter; teams that don't, don't. That last part isn't a product limitation, and we'd rather say so upfront.
Ready to see your AI clearly?
In a focused walkthrough, map your AI traffic, spend, budgets, and data exposure—then see where Governe gives you control.
Book a walkthrough