A decision agent that runs inside your own cloud.
It learns how your business works, takes on the messy data you already have and delivers trustworthy analysis with a clear recommendation. Your data never leaves your perimeter.
A live pilot on your real data in 12 weeks, at cost.
How many critical roles are at retirement risk in the next 12 months?
Headcount by area, hierarchy level and eligibility date
Answer ready
Retirement risk in critical roles
Critical, immediate succession risk across several operating areas.
- 6 employees in critical positions are at retirement risk within the next 12 months.
- 5 of those 6 (83%) are already eligible and still working: the highest level of urgency.
- Quality concentrates the most risk: 2 critical positions, both Team Leaders.
Start with the two Quality Team Leaders: they are 33% of the risk and have no successor on file.
What it is
Not a dashboard. Not a rental either.
Most analytics projects end in more charts and a bill that never stops. Nivii deploys a decision agent on your own data, inside your own environment, and trains your team to run it along the way. It is the shortcut to a capability that takes 18 to 24 months to build from scratch, and you put it to the test before signing anything: no ROI, no contract.
Pilot · 12 weeks · at cost
01
NDA and kickoff
We sign the agreement and you hand over a read-only extract of your data.
02
Building the pilot
A live pilot on your real data, solving one concrete use case.
03
Go / no-go decision
Tuning workshops with your team and a week of testing, up to the call on moving ahead.
- At cost, on Nivii's cloud
- No commitment to continue
Production · 12 months · in your own cloud
04
Rollout
In production inside your own cloud within four months, with your team running the agent.
05
Maintenance and tuning
Fixed-term support, with the agent sharpening on real usage.
06
Hand over or continue
Full handover: architecture, prompts, documentation and a team already trained.
- A single upfront setup
- Fixed-term support
- No recurring per-seat licences
Why Nivii
Enterprise AI is, above all, a trust decision.
Your data never leaves your cloud
Deployment happens inside your own environment: AWS, GCP, Azure, on-prem or SAP BTP. The only outbound call is to the model provider, and even that goes away with in-cloud models.
It speaks your business
It learns your KPIs and your terminology, so “margin” or “active customer” mean what they mean inside your company.
Messy data is the starting point
Disconnected, imperfect sources are the norm. Dedicated cleaning agents put the most reliable ones to work first: value in weeks, not a data project that never ends.
From weeks to minutes
The analysis that meant pulling data from three teams, a BI ticket and a week of waiting comes back in minutes, in plain language, showing the source and the logic.
Proactive, not only reactive
It surfaces the insight before anyone thinks to ask: the failure signal before the downtime, the attrition risk before the resignation, the margin leak before quarter-end.
Use cases
The questions change by industry. The pattern doesn't.
In every large operation the data already exists, scattered across systems that don't talk to each other.
Industrial maintenance
At an industrial plant, the agent cross-referenced 38 months of notifications and work orders stored in SAP: 23 breakdowns had flagged degradation before they broke, with an average of 16 days to intervene. On the equipment with the highest corrective cost, the ratio of repairing to preventing was 29 to 1. None of it was new data: it was written down and nobody cross-checked it.
Sales
The alerts live in the conversation. Dashboards show the numbers. Calls tell the story: friction that hasn't turned into a complaint yet, buying signals that never reach the CRM, shifts in how the brand is perceived.
People / HR
It consolidates employee information spread across systems, identifies candidates to cover critical roles and cross-references salaries with attrition signals, while the key resignation can still be prevented.
Supply chain
It forecasts demand and stock with the sources you already have, imperfect ones included, and shows the assumption behind every number so the planner can argue with it.
Distribution & sell-through
What's moving, where and why, without waiting for the channel to report. The read arrives while the campaign can still be corrected.
Energy operations
A vertical under development. Write to us if you want to know what we are building.
Architecture & security
Enterprises trust Nivii: the power is in our architecture.
The deployment is dedicated, inside your own AWS, Azure, GCP or SAP BTP account. There is no shared tenant: no other customer's data sits next to yours, and your team can see which images run, what traffic leaves and what resources are consumed.
100%
inside your own cloud, on Kubernetes, with GitOps and versioned charts your team can audit.
One data egress
the call to the model, and nothing else. It goes away if your policy requires it.
Zero
telemetry back to Nivii infrastructure: logs, metrics and traces stay in your cluster.
The only data that leaves the perimeter is what goes to the model.
Worth stating plainly. Inference travels over TLS to Anthropic, OpenAI or Google, with retention turned off where the provider supports it and contracts that forbid training on API data. Everything else stays inside your account: the data, the logs, the metrics, and even the trace of every prompt, which is stored inside your own cluster.
If your policy won't allow even that call, the public provider is swapped for models hosted in your own cloud: Bedrock, Azure OpenAI, Vertex AI or SAP AI Foundry. It is a configuration change, not a redesign.
Federated identity
SSO against your corporate IdP over SAML 2.0 or OIDC: Entra ID, Okta, Google Workspace, ADFS, Ping. MFA and lockout policies stay yours, and when someone leaves the company they lose access the same moment.
Permissions by business area
Each person sees only the data of the areas they belong to. The permission is derived from the token, not from what the request asks for, and in Postgres the isolation goes down to the connection pool. It can be narrowed by plant, region or column sensitivity.
Encryption with your keys
Volumes, database and buckets encrypted with KMS, and TLS 1.3 at the ingress. If your policy demands it, the deployment uses your own CMKs: rotating or revoking them is enough to cut access to the data.
Secrets out of the code
Database credentials, API keys and client secrets never live in the repository, in the images or on a developer's machine. They are encrypted against your own cluster's key, and only that cluster can decrypt them.
Closed network by default
Pods have no public IP and NetworkPolicies limit each namespace's egress to an allowlist. A compromised workload has nowhere to exfiltrate to.
Audit trail you can query
Every query, login and permission change is recorded with user, area and dataset. Your admins query it from the platform and export it to the corporate SIEM: Splunk, Datadog or Elastic.
And if your security team asks for it: pod-to-pod mTLS · joint pen-test before production · SBOM and vulnerability report · air-gapped deployment · PII masking in the interface and in prompts · just-in-time access
FAQ
What technical teams ask us
Bring one business question you cannot get answered fast enough.
Bring your data, however messy and disorganised it is. We'll show you what Nivii can do with it, and the potential it holds for your business.