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Case Studies

Deployments We've Designed, Built & Delivered

The clearest evidence of a deployment partner is systems running in production. Voorna and Luniby Farm are worked examples of Sovata's deployment methodology — architected, built and delivered end to end with the same governance and security approach we bring to enterprise clients. Private AI deployment scenarios for New Zealand organisations follow below.

Example deployments

In production — designed and deployed end to end

These are not products Sovata sells — they are production deployments that demonstrate the full methodology: secure infrastructure, role-based access, governed data handling and architecture ready for AI orchestration. Both are live today — click through to examine each deployment and read the client's own words.

The Voorna online voting platform — designed and built by Sovata AI.
Online Voting SaaSIn production

Voorna: Rebuilding Online Voting as a Secure, Scalable SaaS Platform

A worked example of Sovata’s deployment methodology: secure production architecture, role-based access, governed data handling and integrated payments — deployed end to end and architected for AI orchestration and automation.

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The Luniby Farm livestock and farm management platform — designed and built by Sovata AI.
Agriculture Technology (AgTech)

Luniby Farm: An AI-Ready Livestock & Farm Management Platform

A worked example of Sovata’s deployment methodology in the field: structured data architecture, role-based access, secure cloud sync and business process automation — the same governed foundation Sovata builds for enterprise AI and knowledge retrieval.

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Private AI deployment scenarios

The outcomes a private AI deployment is designed to deliver

These scenarios show the outcomes a private AI deployment delivers across different New Zealand sectors — with client names and confidential details withheld.

01 · Professional services

A staff knowledge assistant that keeps client data in-house

Challenge. A mid-sized New Zealand advisory firm had institutional knowledge locked in a handful of senior staff and a document library nobody had time to search — while client confidentiality ruled out pasting anything into public AI tools.

Approach. A private AI instance was deployed inside the firm’s own infrastructure, trained on approved internal policies and precedents, with clear governance on what it could access and surface.

Outcome. Staff get instant, accurate answers from internal knowledge without confidential material ever leaving the firm’s control, and senior people spend less time answering repeat questions.

02 · Public sector

Reducing administrative load while respecting data sovereignty

Challenge. A public-sector agency wanted to cut the time staff spent on routine enquiries and document review, but citizen data meant offshore processing was off the table.

Approach. An onshore, governed deployment handled routine enquiry triage and document summarisation, with humans accountable for every decision that affected a citizen.

Outcome. Faster handling of routine work, sensitive data kept onshore and under New Zealand control, and a clear audit trail for oversight.

03 · Regulated industry

Case and compliance support within defined boundaries

Challenge. A regulated New Zealand organisation needed to speed up case handling without ceding consequential decisions to automation or breaching its compliance obligations.

Approach. A private AI instance triaged incoming cases, summarised context and drafted first-pass responses — with every decision of consequence kept with a human, inside boundaries agreed during deployment.

Outcome. Quicker case throughput and more consistent first drafts, while accountability and compliance controls stayed firmly with the organisation.

FAQ

Questions about our case studies

These reflect real private AI deployments — we keep client names and confidential details out of public case studies. We can walk through relevant named examples under NDA on a discovery call.

Yes. On a discovery call we can talk through work most relevant to your sector and the specific outcomes you’re trying to achieve, within the confidentiality our clients rightly expect.

We define success in business terms up front — time saved, cost reduced, service improved — and capture a baseline so the result can actually be measured rather than assumed. See our AI readiness and ROI guidance in the blog for how we approach this.

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