AI you can explain, audit and defend.
Government and Defence organisations hold some of the most complex information in the country: decades of records in many formats and systems, often in more than one language, under strict rules about who can see what. AI can turn that information into accurate, usable insight, but only if it is built for the environment it has to work in.
Built for organisations that have to account for every output
Accuracy over fluency
Outputs are grounded in authoritative sources and carry citations a reviewer can check. When the answer isn’t in the source material, the system says so instead of guessing.
Sensitive data stays inside the boundary
We run AI processing alongside governed data rather than moving it into separate environments, designed from the start for Australian hosting and alignment with the ISM and the PSPF.
Assurance throughout delivery
Responsible AI risk assessment, adversarial testing, and alignment with Australian Government AI policy and the DTA AI Technical Standard are part of how we deliver, not a final step before go-live.
People stay accountable
AI supports analysts, specialists and decision makers. Each deployment sets out what the system does, what a person reviews, and what is escalated.
What we build for government and Defence
Document and language intelligence
Reading, extracting and translating information in any format, and turning it into the output people need.
Governed knowledge assistants
Answering only from approved sources, citing every response, and staying within set boundaries.
AI assurance and observability
Monitoring for drift, out-of-date sources, hallucination and misuse, with full traceability of every output.
Secure data foundations
Platforms with classification, lineage and access control built in.
Faithful to the source, not just fluent
Much of an organisation’s most valuable information is held in documents that were never designed to be searched, compared or reused: reports, contracts, scanned forms, spreadsheets, correspondence and records, often in several languages and built up over many years. We build AI that reads all of it accurately and turns it into what people actually need.
Any input
Native and scanned PDFs, Word documents, spreadsheets, images, email, forms, tables and free text.
Structure kept intact
Tables stay tables, forms stay fields, and context is kept, instead of everything being flattened into plain text.
Faithful translation
Content moves between languages with meaning, terminology and layout preserved, supported by glossaries for specialist and technical language.
Any output
Translated documents, structured datasets, summaries, briefs, searchable knowledge bases, or direct feeds into other systems.
Every output traceable
Each result links back to the passage it came from, with confidence indicators and human review where the stakes call for it.
General-purpose AI tools are fluent, but not always faithful to the source. In high-stakes work, a confident mistranslation or a missed row in a table is worse than no answer. We design for faithfulness: outputs grounded in source material, controlled terminology, testing against reference sets before deployment, and monitoring once live that detects when quality starts to slip.
Every implementation is built in three layers.
- 01
Context
Documents are ingested, structured and indexed so the system works from current, approved material rather than general training data.
- 02
Reasoning
The model extracts, translates, summarises or answers, and breaks complex requests into steps where needed.
- 03
Validation
Guardrails confirm each output comes from the source, protect sensitive information, and flag anything that needs a person to review it.
100,000+
US Government contractor · Vivanti
We brought more than 100,000 files from OneDrive, SharePoint, laptops and legacy systems into one governed repository. We removed duplicates, automated classification, extracted parties, dates and departments, and gave staff plain-language search with answers that cite the source document.
AI fails quietly. We make it auditable.
Conventional systems fail visibly: they crash, throw errors or stop responding. AI produces fluent, confident answers that are wrong, drifts as the world changes around it, or keeps working from data that went out of date days ago, while every dashboard still shows green. For organisations that have to account for every decision, that silent failure is the real risk.
Data
Freshness, quality, lineage and access, because AI is only as reliable as the data behind it.
AI
Drift, grounding, evaluation and cost, with a full record of every request and response.
Security
Misuse, unusual access and prompt injection, detected from the same telemetry.
The result is AI you can audit. You can show what data produced an output, who accessed which records, and whether the system is staying within its defined limits, and you can reconstruct a decision months after it was made. We build it from the first release, as the infrastructure for AI governance.
One team that knows the technology and the environment
Delivering in Defence takes more than good technology. Capability has to move through governance, accreditation, security and stakeholder processes that are specific to the Australian Defence environment.
Vivanti partners with Profectus, a Canberra-based ICT consultancy that has delivered for the Department of Defence and the Federal Government for years. Together we combine Vivanti’s data and AI engineering with Profectus’s experience in Defence governance, assurance and delivery, so what we build is ready for how Defence actually runs.
Member of the Defence Industry Security Program
Certified to ISO 27001:2022
Security-cleared people up to NV2
Access to the major Commonwealth ICT procurement panels
Profectus in Defence AI
MADE AI for Defence Export Controls
Profectus governed, assured and managed delivery of MADE AI, the first AI capability developed within the Department of Defence: a multilingual knowledge assistant that gives cited legislative guidance in more than 100 languages. Profectus brought Defence, Microsoft, Accenture and the Defence Digital Transformation Agency under one governance model and took the capability from concept to go-live in five months. The work covered:
- Responsible AI guardrails embedded throughout the build
- Oversight of design and independent testing of the hybrid semantic and keyword retrieval system
- End-to-end scenario testing of multilingual accuracy and citation integrity
- Assurance of compliance with the Defence ISM and Australian data sovereignty requirements
- A managed transition into operations, including service continuity planning
- 150
- staff hours a week freed for higher-value work
- 100+
- languages, with cited guidance
- 5 months
- from concept to go-live
MADE AI was delivered by Profectus, and gives government a reusable model for responsible AI deployment. Read the full case study at profectus.com.au.
From pilot to production, one scoped use case at a time
Most AI programs stall because they start too big, point AI at data that isn’t ready, or never agree what success looks like. We work the other way round, using a seven-step framework.
- 01
Choose the right first problem
Weigh value, feasibility, data readiness and risk. Workflows heavy in documents and language are often the strongest place to start.
- 02
Define success first
Agree one measurable outcome before anything is built.
- 03
Prepare only the data needed
Curate the specific data the use case requires, to the standard it requires.
- 04
Match the task to what AI does well
Extraction, translation, summarisation, classification, retrieval and drafting are where AI performs reliably today.
- 05
Keep AI next to the data
Process within the governed environment rather than moving data out of it.
- 06
Deploy with oversight
Use human review, output validation and guardrails proportionate to the risk, and redesign the workflow around the new capability.
- 07
Measure, then scale
Report the result honestly, including what didn’t improve, and use it to fund the next use case.
Start with a conversation. Then see it working.
It usually starts with a conversation about the problem, followed by a demonstration on representative, unclassified material. From there we prove the capability on a clearly scoped piece of work before scaling it.
Request a demo