Solve8 is an Australian AI consultancy. We help Melbourne organisations decide where AI is worth the money, then build it: automating the admin and document work that eats finance and operations teams' weeks, connecting systems that do not talk to each other, getting Microsoft 365 ready for Copilot, and writing the governance a risk committee needs before it signs off.
Melbourne teams also use us for IT consulting in Melbourne when they want an independent view on software selection, Microsoft 365 or a cloud move, and for custom software development when an internal app, portal or integration has to be built to fit.
Engagements are scoped in writing with a fixed fee per phase. Start with our AI strategy and advisory service or go straight to business process automation.
We start from the problem in front of you. The organisations below come in every size, from CBD practices to suburban care providers and Victorian public sector teams, and they share a kind of work automation handles well. If one sounds like your Monday morning, a scoping call will tell you quickly whether there is a project worth doing. Financial services teams should also see AI for financial services.
Invoices, expense claims and supplier statements keyed in by hand, and a month end that depends on one person's spreadsheet. Extraction with a person checking exceptions removes most of the retyping.
Law, accounting, engineering and consulting practices spend hours moving content between documents, a practice system and the billing platform. Most of that is extraction and drafting with a person checking the result.
Enquiries arrive by email, web form and phone, and someone sorts them by hand before anyone can answer. Classification and drafted replies, with a person sending, shorten the queue without hiding it.
Care notes, incident reports and funding claims pass through several systems before anyone can report on them. The new Aged Care Act and NDIS registration changes both add evidence a regulator can ask to see.
Departments, councils and the firms that deliver services for them work under Victorian privacy and data security rules on top of the federal ones. AI work has to show where data goes before it starts.
Plants and warehouses in the south east and the west run production, quality, freight and finance in separate systems, and someone reconciles them by hand every month.
Enquiries from Melbourne tend to fall into a handful of patterns. Each has a well understood fix, and sometimes the right fix involves no AI at all. Our comparison of automation and manual processes covers how to tell the difference.
Extraction reads the document, matches it to the purchase order and receipt, and routes mismatches to a person. Clean records go straight into Xero, MYOB or the ERP.
Invoice processing automation guideWe move the source data into a model that refreshes on a schedule, so month end becomes a review of figures already there instead of a week of copying and pasting.
Data quality before AIOlder platforms can usually be reached through the database, scheduled exports, or RPA as a last resort. We pick the least fragile route and write it down.
Integrating legacy systems with AIAASB S2 reporting needs figures someone can trace back to a source document. We build that evidence trail before anyone designs a dashboard.
Audit-ready climate disclosureA governed internal assistant, with data held in an Australian region or on your own servers, gives people a sanctioned option and gives IT a log.
How the wrong AI tools leak dataAdvisory work produces a ranked, costed roadmap the board can approve or reject on its merits, with the governance questions answered in writing.
Take the AI readiness assessmentA common starting point is a vendor demo that impressed the executive team and a budget line with nothing agreed underneath it. Advisory work settles what the money should go on, in a costed document you own.
We interview the people who run each process, look at the systems and the data, and test every candidate for payback, data quality and governance. Ideas we reject are written up as well, with the reason, so they do not come back next year without new information.
Our article on AI strategy compared with AI implementation explains when to start with advice and when to go straight to a build, and what AI implementation actually costs breaks a budget down phase by phase.
Candidate processes ranked by payback and difficulty, so the quick wins are visible before the hard ones.
Which systems connect, where the data lives, and what has to change before any model is useful.
Build and running costs set against current effort, written to survive a CFO's questions.
Privacy Act, Victorian privacy and health records obligations, data residency and human review points.
A pilot on one process first, so the approach is proven before a program of work is funded.
We name the tools that fit, including ones we do not sell. You own the document and can deliver it with anyone.
See what this would take in your business
A free 30-minute review on Microsoft Teams. Bring the process you want fixed and leave with a clear first step.
Professional firms, care providers and agencies in Melbourne mostly run on Microsoft 365, and Copilot is usually the first AI tool the leadership team asks about. Copilot answers from whatever each user can already open in SharePoint, OneDrive, Teams and Outlook. Microsoft documents this directly: Copilot only surfaces organisational data that the individual user has at least view permission to.
In a law or accounting practice that means a client matter folder shared too widely years ago can come back in answer to a plain English question. The Digital Transformation Agency's evaluation of the Australian Government Copilot trial reported the same issue: trial participants saw Copilot surface sensitive information that had never been stored or permissioned properly.
So readiness work happens before the licences are bought: audit, fix or contain, then pilot.
Sites open to everyone, anonymous sharing links, broken inheritance and sites with no owner.
Where payroll, client files, health records and board papers sit, and whether sensitivity labels cover them.
Restricted Content Discovery keeps risky sites out of Copilot answers while the clean-up finishes.
A group with clean access, measured tasks, and an agreed rule for when to buy more licences.
In many organisations the figure in the leadership meeting, the one in the finance system and the one in a manager's spreadsheet are three different numbers. We build Power BI models that refresh from the source systems, with a written definition for each measure, so the argument about which number is right happens once. Reporting is also where a lot of AI work should begin, because a model is only as useful as the data underneath it.
Groups with several business units can read what AI implementation actually costs before scoping a reporting program.
For a Tier 1 infrastructure contractor, we replaced emailed spreadsheets with standardised templates, SharePoint and Power BI for carbon reporting.
Read the Tier 1 carbon reporting case studyOnce the model is trusted, managers can ask it questions in plain English instead of waiting for an analyst. The answer cites the measure it used, so nobody has to guess where a figure came from.
Groups that run several Xero organisations need consolidated reporting first. A product already does that, so we point you to it before building anything custom.
ReportingMate for consolidated Xero reportingPower Automate follows fixed rules between systems that have connectors. RPA drives an application through its screens, which reaches an old system with no API and breaks when the vendor redesigns a screen. An AI agent reads input that varies.
Many teams already own Power Automate, and often a flow is the right answer. This is the test we apply.
A good design often uses both: a flow moves structured data between systems and a model handles the one step that needs reading or judgement. For the wider choice, see our comparisons of AI and RPA and of Zapier, Make and n8n. If an agent is the answer, our notes on running AI agents in production cover the monitoring and ownership it needs after go-live, and system integration is usually where most of the effort goes. Where no platform does the job, we take it on as custom AI development.
For Victorian manufacturers and food processors, the data that would justify an AI project usually sits in a historian, a quality spreadsheet and the maintenance system, none of them set up to be read together.
We work on the IT side of the plant boundary, reading from historians and plant databases without writing back to control systems. Typical work includes reconciling historian production figures with the ERP, cleaning up OT tag naming so data can be compared across lines and sites, and building the data foundation industrial AI needs. For reporting, see PI System data in Power BI.
Our principal consultant spent 18 years on projects for industrial and energy companies, including Schneider Electric and Senex Energy, gained while working with previous consulting employers. These companies are not Solve8 clients.
Regional sites are covered the same way, with on-site days scoped in. See our page on manufacturing and processing for more.
See what this would take in your business
A free 30-minute review on Microsoft Teams. Bring the process you want fixed and leave with a clear first step.
Victorian organisations deal with two layers of privacy law. The federal Privacy Act 1988 covers most private sector businesses. Victorian public sector bodies work under the Privacy and Data Protection Act 2014, and health information is covered by the Health Records Act 2001. AI raises questions none of these anticipated: where a prompt is processed, whether a vendor trains on it, and who can explain an automated decision to the person it affected.
We default to Australian cloud regions. For sensitive workloads we deploy open models on your own servers or private cloud so prompts and documents stay inside your environment. See Australian compared with offshore AI development.
From 10 December 2026, amendments to the Privacy Act require privacy policies to describe substantially automated decisions that significantly affect individuals.
Privacy Act rules for automated decisionsThe systems we build log inputs, outputs and the person who approved each exception, so an auditor or a regulator can trace a figure back to its source and see who signed it off.
AI agent governance and human overrideOrganisations that already run ISO certified management systems can extend them to AI under ISO/IEC 42001 instead of writing a separate policy set from scratch.
Engagements are scoped and fixed per phase, so stopping after discovery is a legitimate outcome and you are never locked into a build you have not approved.
A free 30 minute call, then a written scope with a fixed fee for the first phase. Nothing starts until you approve it.
Workshops in the room with the people who do the work, and the rest over Microsoft Teams.
Short build cycles against your real data, in your tenancy or an Australian region. One process is piloted before any rollout.
Documentation, training and admin access stay with you. Your team runs it from there, or we look after it under a managed service.
After handover some organisations keep us on through managed AI services for monitoring and changes. Teams with their own developers sometimes prefer team augmentation, where we work inside their delivery process. Our AI consulting buyer's guide and how to choose an AI consultancy list the questions to ask any firm, including us.
Melbourne has plenty of AI agencies. We are a small consultancy that will tell you which parts of a process should stay manual.
The consultant who scopes the job also builds it. No account manager passes requirements to a junior developer you never meet.
Privacy, data residency and human review points are written into the scope before the build, which is what regulated Melbourne buyers need to get approval.
We build and operate our own software, including Carbonly for emissions data and SupportAgent for on-premise incident investigation.
Australian business, working nationally
Australian regions or your own servers
Governance written into every scope
Registered Australian business
Our principal consultant brings 18 years of enterprise delivery experience from projects for BHP, Rio Tinto and Senex Energy, delivered with previous consulting employers. These companies are not Solve8 clients. You work directly with senior people, not a sales team handing you off to juniors.
Practical guides on the questions that come up most in Melbourne scoping calls
What Melbourne and Victorian organisations ask before they commit to an AI project
Have a question about AI for your Melbourne organisation that is not covered here?
Solve8 works with organisations in every state and territory. See all the places we work.
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