Top 10 AI Startups to Watch in Australia in 2026

Table of Contents

• Intro: Why Australia’s 2026 AI setlist matters
• Heidi Health
• Harrison.ai
• Lorikeet
• Relevance AI
• ArchiStar
• Andromeda Robotics
• Brainfish
• TrueState
• Kapture
• Understanding Zoe
• How to use this list and what to watch next
• Frequently Asked Questions

Heidi Health

Problem they’re solving

In Melbourne’s health-tech setlist, Heidi Health is the GP track on repeat. Australian doctors routinely spend as long on documentation as they do with patients, thanks to Medicare compliance, fragmented practice software and clunky templates. Heidi tackles this by acting as a vertical AI medical scribe tuned to the realities of bulk billing, item numbers and local clinical terminology, rather than generic US-style hospital workflows.

Product & approach

Heidi listens to consults, auto-drafts structured notes, suggests diagnoses and codes encounters for billing. Its edge is deep localisation: everything from phrasing to prescribing shortcuts is built around Australian primary care, with variants for UK and now US settings. That kind of vertical depth is exactly what reports like PwC Australia’s “AI-native enterprise” analysis point to as the next wave of AI value: systems embedded directly into front-line workflows, not just bolted on.

Funding, traction & customers

Backers have noticed. Heidi closed a US $96.6m (≈ AUD $147m) Series B in October 2025, led by Blackbird Ventures, putting it among the most heavily funded health-tech startups in the country. It’s already widely used by clinicians across Australia and the UK, with a recent push into the US market.

“The AI start-up growing faster than Canva.” - The Australian Financial Review

Why it matters in 2026

Heidi Health has become the benchmark for Australia’s emerging strength in vertical healthcare AI. For engineers and data scientists, it shows how tightly coupled clinical models, reimbursement logic and UX have to be to win trust in Medicare-linked environments.

• Integration with major GP and EMR systems across Australia and APAC
• Expansion beyond GPs into specialists and allied health
• Whether it remains independent or becomes a prime acquisition target for global health platforms

For anyone building an AI career out of Melbourne’s biomedical precinct, Heidi is proof that “AI-native” clinical tools from Australia can scale faster than our last generation of SaaS darlings.

Harrison.ai

Problem they’re solving

In hospital radiology and pathology, scan volumes keep climbing while specialist headcount barely moves. Turnaround times stretch, after-hours rosters blow out, and second reads become a luxury. Harrison.ai steps into that gap with clinical decision support that helps radiologists and pathologists read more studies, more consistently, without pretending the clinician can be automated away.

Product & approach

From its Sydney base, Harrison.ai builds computer-vision models powering two flagship ventures: Annalise.ai for radiology and Franklin.ai for pathology. Both are co-developed with major diagnostic providers, meaning models are trained on enormous real-world datasets rather than tidy research samples. As SmartCompany’s profile of Harrison.ai notes, the focus is squarely on easing pressure in overstretched systems by augmenting clinicians, not replacing them.

Funding, traction & customers

Investors have backed that “industrial-strength” approach in a big way. Harrison.ai raised a US $179m (≈ AUD $273m) Series C in early 2025 from Blackbird and Horizons Ventures, putting it among Australia’s best-capitalised AI scaleups. Through Annalise and Franklin, its tools are now deployed in hundreds of clinics globally across Australia, the UK, Europe and Asia, leveraging long-standing research ties with UNSW and large imaging networks. In the broader funding picture, healthcare-focused AI has been one of the clearest winners in what Scale Suite’s 2025 funding review calls a consolidation around “category leaders”.

Why it matters in 2026

Where Melbourne’s Heidi Health rewir es GP consults, Harrison.ai is the hospital-grade engine room of Australia’s vertical AI story. For engineers and clinicians along the Sydney-Melbourne corridor, it shows what it takes to export regulated, safety-critical AI: multi-year data partnerships, deep clinical validation, and global regulatory strategies.

• Winning additional approvals in North America and more of Europe
• Extending from image interpretation into workflow optimisation and triage
• Building towards IPO readiness as Annalise and Franklin revenues mature

For anyone wanting to work on “hard-mode” AI - where errors really matter - Harrison.ai is one of the clearest proving grounds in the country.

Lorikeet

Problem they’re solving

Most of us know the pain of shouting into a chatbot void: rigid scripts, endless loops, and a “transferring you to an agent” message as soon as things get tricky. Boards aren’t impressed either; they now want AI that can actually own an outcome, not just deflect tickets. That’s the gap Lorikeet is attacking in the Sydney SaaS and fintech heartland.

Product & approach

Lorikeet builds agentic AI concierges for customer service. Instead of a static FAQ bot, its agents plug into CRMs, billing platforms and internal tools, then reason, act and learn across the whole journey: updating records, processing refunds, rebooking services and escalating only when needed. In round-ups of top Australian AI startups, Lorikeet is consistently highlighted as part of a new wave of outcome-focused “AI staffers” in customer operations.

Funding, traction & customers

That thesis has serious money behind it. Lorikeet raised a US $54m (≈ AUD $82.5m) Series A in late 2025, led by QED Investors with local heavyweights Blackbird, Airtree and Square Peg on the cap table. Revenue grew 10x in the 12 months to late 2025, off the back of customers like Airwallex, Linktree and Eucalyptus trusting Lorikeet’s agents with live customer flows. Andreessen Horowitz’s 2025 “AI Apps 50” ranked Lorikeet 8th globally, notably ahead of Canva on startup AI spend.

Why it matters in 2026

Lorikeet is one of the clearest expressions of what commentators mean when they say Australian firms will “treat AI like staff” by 2026, a shift explored in detail by ChannelLife’s coverage of enterprise AI adoption. For engineers working along the Sydney-Melbourne corridor, Lorikeet shows how quickly agentic systems move from proof-of-concept to mission-critical when they plug into real systems and carry real accountability.

• Scaling from Aussie unicorns into global fintech and SaaS accounts
• Balancing autonomy with compliance and brand safety in regulated sectors
• Forging deeper partnerships with big local buyers like CBA and Telstra

If your career plan involves building or governing AI agents in production, Lorikeet is the customer-ops play to watch.

Relevance AI

Problem they’re solving

Across Australia’s banks, telcos and SaaS scaleups, teams have proof-of-concept agents running in notebooks, but very few have the infrastructure to turn them into reliable “AI employees”. Standing up orchestration, evaluation, versioning and monitoring from scratch is slow, expensive, and beyond what most data or ops teams can own on their own. That’s the gap Relevance AI is carving out from its base in Sydney’s startup belt.

Product & approach

Relevance AI offers a low-code platform for building and orchestrating specialised AI agents that process unstructured data, trigger workflows, and integrate with CRMs, data warehouses and SaaS tools. Instead of selling a single vertical app, it positions itself as the “SaaS for AI agents” layer in APAC, bundling prompt and tool orchestration, evaluation, human-in-the-loop review and analytics. As The Australian’s coverage of its 2025 raise notes, the team has been deliberate about avoiding hype spikes in favour of hard-nosed productivity gains across global teams.

Funding, traction & customers

That pragmatic focus attracted a US $23m (≈ AUD $35m) Series B in mid-2025, led by Insight Partners, backing Relevance AI as one of the region’s core agent platforms. It now serves 6,000+ companies globally, from scrappy SMEs to larger enterprises, with use cases spanning support, marketing ops and data operations. In broader ecosystem rundowns of top AI enterprises, commentators on Medium’s analysis of Australian AI leaders consistently cite Relevance AI as an example of “quietly compounding” infrastructure rather than headline-chasing apps.

Why it matters in 2026

Where Lorikeet is a vertical bet on customer service, Relevance AI is a bet on the plumbing every enterprise will need as agents proliferate. For engineers along the Sydney-Melbourne corridor, it’s a live case study in building multi-tenant, multi-agent platforms: permissions, observability, compliance features and integrations that let banks, retailers and SaaS firms roll out agents safely inside existing stacks. If you want to work on the rails rather than any single carriage, this is one of the clearest infrastructure plays in the Australian AI setlist.

ArchiStar

Problem they’re solving

Every developer, architect and council planner knows the slog: weeks to pull zoning data, months to iterate designs, and even longer to navigate state and local rules. In the middle of a national housing supply crunch, those delays translate into fewer dwellings and higher costs. ArchiStar’s bet is that a lot of this bottleneck isn’t design talent, it’s the sheer complexity of planning schemes, overlays and feasibility maths.

Product & approach

ArchiStar combines generative design with rich property and planning datasets to automate three things that normally chew up entire project teams:

• Site feasibility and yield analysis for potential developments
• Planning and zoning compliance checks against local/state rules
• Massing and layout options that are buildable and policy-aligned

By encoding planning regulations, zoning constraints and design standards directly into its models, ArchiStar exemplifies the “vertical AI” shift that property investors have been tracking. Proptech Australia’s analysis of VC flows notes that AI-first tools like ArchiStar are increasingly central to how local governments accelerate assessments.

Funding, traction & customers

The company has raised US $22.7m (≈ AUD $34.5m) through a Series B in mid-2024, positioning it among Australia’s best-funded proptech players. It now works with major state governments to help speed up permit and code assessments, while developers, architects and planning consultants use the platform to stress-test sites before they commit serious capital. Broader funding reviews, such as Gaia EnviroTech’s wrap of 2025 startup funding, highlight construction and climate-aligned AI as rising priorities for local VCs - a tailwind ArchiStar is well placed to ride.

Why it matters in 2026

ArchiStar shows what happens when AI is woven into the regulatory fabric of a country rather than tacked on as an afterthought. For engineers and urbanists, it’s a blueprint for high-impact careers at the junction of AI, planning policy and housing affordability, with clear upside as more states, and eventually New Zealand and wider APAC, look to standardise faster, data-driven approvals.

Andromeda Robotics

Problem they’re solving

Walk into almost any Australian aged-care home and you’ll see the same pattern: dedicated staff, not nearly enough of them, and residents who still don’t get the level of social interaction they need. Workforce shortages and tight funding settings make it hard to add more humans to the roster, even as care standards rise. Andromeda Robotics is targeting that gap directly, using AI-led social robots to provide consistent companionship without pretending to replace nurses or carers.

Product & approach

The company’s flagship humanoid robot, Abi, is built for emotional engagement rather than industrial automation. Abi uses natural language processing in 90 languages, remembers prior conversations, and personalises games, music and exercises for each resident. Over time, it learns patterns in mood and engagement, giving staff another signal for who might need extra attention. This focus on social presence, not just task automation, sets Andromeda apart from traditional robotics plays highlighted in broader AI round-ups such as Gem Corp’s overview of Australian AI leaders.

• Conversational support and reminiscence therapy-style chats
• Group activities, quizzes and gentle physical exercises
• Dashboards that surface resident engagement trends for staff

Funding, traction & customers

In early 2026, Andromeda Robotics closed a AUD $23m Series A at roughly a AUD $100m valuation, led by Forerunner Ventures. Abi is already deployed in 22 aged-care facilities across Australia, including providers such as Mecwacare, and the startup maintains strong research links with Monash University and Melbourne’s health-tech ecosystem. Its progress mirrors a broader shift toward care-focused AI that commentators at Dynamic Business’s 2026 startups-to-watch list see as a major local strength.

Why it matters in 2026

Andromeda sits right at the intersection of hardware, AI and social policy - a uniquely Australian sweet spot given our ageing population and recent royal commissions into care quality. For engineers, it’s a rare chance to work on embodied AI rather than pure software; for policymakers, it raises live questions about how AI-driven social interaction should be measured and regulated inside NDIS and aged-care frameworks.

• Can Abi consistently improve resident wellbeing and staff efficiency at scale?
• How will regulators incorporate social robots into care standards and funding models?
• Will global robotics or health-tech giants move to partner or acquire as clinical data matures?

Brainfish

Problem they’re solving

Most support teams are still propped up by static FAQs and dusty Confluence pages that never quite match what customers are actually doing in the product. That gap turns into higher ticket volumes, longer handle times and inconsistent answers across time zones - a real tax on fast-growing SaaS and Australian SMEs. Brainfish is going after that “Tier 0 and Tier 1” layer, where better knowledge, surfaced at the right moment, can quietly deflect thousands of tickets a month.

Product & approach

Brainfish is a generative AI-powered knowledge base that ingests product docs, historical tickets and behavioural data, then serves up tailored, real-time guidance. Instead of a static article, users see dynamic, step-by-step help that adjusts to context, with a multimodal engine capable of combining text, visuals and walkthroughs. This lines up with what SME-focused advisors at Bizcap’s review of AI tools for small businesses call one of the clearest ROI plays: automating repetitive customer queries to free up lean teams.

Funding, traction & customers

Founded in 2022 by Daniel Sandaver and Ajay Prakash, Brainfish raised a US $10m (≈ AUD $15.2m) Seed round in mid-2025. The product is gaining traction with Australian SMEs that are outgrowing basic FAQs and want to replace traditional Tier 1 and Tier 2 docs without building an in-house ML team. In global rundowns of AI startups to watch, such as Startup Savant’s 2026 list, this kind of “ops-first” AI is increasingly highlighted as a durable niche, even when it doesn’t make mainstream headlines.

Why it matters in 2026

Brainfish is a textbook example of the “boring but profitable” end of Australia’s AI setlist: no flashy avatars, just lower cost-to-serve and happier customers. For engineers along the Sydney-Melbourne corridor, it’s a live case study in applying retrieval, fine-tuning and evaluation to messy, real-world documentation. Watch for whether Brainfish stays focused on knowledge orchestration or gradually expands into full support automation - pairing tightly with tools like ticketing systems and in-app chat to become the backbone of self-service support.

TrueState

Problem they’re solving

For banks, energy providers and critical-infrastructure operators, the enthusiasm for AI crashes quickly into APRA, ASIC and security teams. They need models in production, but every question is about where the data lives, who touched it, and how to audit it. Generic US-hosted tooling rarely clears those bars. TrueState is stepping into that gap with MLOps built from day one for highly regulated, sovereignty-conscious organisations.

Product & approach

TrueState is building an MLOps infrastructure platform with security, governance and compliance baked in rather than bolted on. Think model deployment, monitoring and lifecycle management inside sovereign Australian environments, with access controls, lineage tracking and audit-ready reporting as first-class features. It’s exactly the kind of “are we actually in control of this?” layer that IP and risk specialists are calling for in analyses like IPWatchdog’s review of AI investment ROI.

Funding, traction & customers

Founded in 2023, TrueState raised a US $1.5m (≈ AUD $2.3m) Seed round in mid-2024, backed by Airtree, and is now building towards a Series A. It’s targeting customers in finance, energy and government - exactly the sectors facing the tightest obligations under Australia’s critical-infrastructure and financial-services regimes. In broader market round-ups like Jaarvis’ survey of Australian AI development leaders, demand for secure, explainable and well-governed AI platforms is consistently singled out as a key growth driver.

Why it matters in 2026

TrueState is still early, but strategically important. If Australia wants to lead in AI for banking, grids and defence, it needs homegrown tools that satisfy local regulators and security teams while still letting engineers ship fast. For practitioners along the Sydney-Melbourne corridor, this is a glimpse of a fast-emerging career niche: not building the models themselves, but owning the trust and rails that let the rest of the ecosystem scale safely.

Kapture

On remote mine sites from the Pilbara to Queensland’s basins, emissions from diesel generators and heavy equipment are still treated as an unavoidable cost of doing business. Traditional carbon capture is bulky, expensive and usually designed for centralised plants, not scattered generators at camps and processing hubs. Kapture is tackling that reality head-on: using AI to make it viable to trap and repurpose exhaust where it’s produced, without adding a “green premium” that miners will baulk at.

The startup combines AI-driven computer vision, specialised sensors and materials science to watch exhaust streams in real time, then optimise capture and conversion into inputs for cement or fertiliser. Models continuously adjust for load, temperature and fuel quality so operators don’t need on-site data scientists. This kind of sector-specific innovation is exactly the type of climate and industry play highlighted in analyses of high-impact AI business models in Australia, where mining and energy are seen as outsized opportunity areas.

• Computer vision tracks plume characteristics and alerts on anomalies
• Control systems tune capture parameters for maximum efficiency
• Captured material is stabilised into commercially useful by-products

Kapture is still early-stage, with funding rounds not publicly disclosed, but it has already won international startup competitions and completed on-site pilots in Western Australia’s mining sector. The company also partners with Deakin University’s Recycling and Clean Energy Hub to refine capture media and conversion pathways, a classic example of Australian deep-tech spin-out thinking. In a region where large infrastructure plays like Firmus are attracting hundreds of millions to expand AI-driven energy and compute capacity across Asia-Pacific, as reported by the Economic Times’ coverage of Australian AI infrastructure funding, Kapture represents a more targeted but potentially powerful lever on emissions.

For engineers and data scientists eyeing roles with BHP, Rio Tinto or Fortescue, Kapture hints at what the next decade looks like: AI systems embedded directly into physical kit, shaving tonnes off Scope 1 emissions while keeping operating costs flat. It’s AI not as an app, but as part of the mine.

Understanding Zoe

Problem they’re solving

For parents of neurodivergent kids, the first diagnosis often arrives as a 20-40 page report full of acronyms, psychometric scores and references to therapies they have never heard of. The next step is usually the NDIS maze: forms, planning meetings and funding jargon that can overwhelm even well-resourced families. Understanding Zoe is aimed squarely at this gap between expert language and everyday decision-making.

Product & approach

Understanding Zoe uses natural language processing and personalised AI to turn dense clinical, educational and allied health reports into plain-language explanations and concrete next steps. Parents upload documents; Zoe parses goals, recommendations and observations, then generates:

• Easy-to-read summaries of what the diagnosis actually means day to day
• Checklists of therapy options and questions to raise with clinicians
• Structured information parents can reuse in NDIS access and planning meetings

Co-founders Laetitia Andrac and Johan Erchoff built the product from lived experience of the Australian system, making Zoe an archetypal “vertical AI” play in social and health policy rather than another generic chatbot. In ecosystem surveys like Tracxn’s mapping of Australian AI startups, this kind of niche depth is increasingly recognised as a competitive edge.

Funding, traction & ecosystem position

Understanding Zoe raised AUD $770k in Seed funding in late 2025 from Verge HealthTech Fund and completed the Techstars Sydney 2024 program, plugging it directly into the Sydney startup corridor. Early traction is coming from Australian families navigating new neurodiversity diagnoses and NDIS plans, with usage clustered around paediatric clinics and school referrals.

Why it matters in 2026

On a funding league table, Zoe is a B-side; on an impact chart, it punches far above its weight. As global surveys like Failory’s rundown of AI startups show, the most durable companies often solve messy, local problems inside public systems. For engineers and product managers, Zoe is a reminder that some of the most meaningful AI careers in Australia will be built not around ad impressions, but around helping families understand their own paperwork.

• Validation studies on reduced delays and better NDIS engagement
• Partnerships with clinics, schools and advocacy groups
• Careful expansion into other NDIS-like schemes in New Zealand, the UK and Canada

How to use this list and what to watch next

Read it like a setlist, not a scoreboard

Like any Hottest 100, this Top 10 is less a definitive ranking and more a mirror of what voters value right now. Here, the “voters” are boards and VCs backing vertical AI and agentic workflows that can prove ROI inside real hospitals, contact centres, mines and government systems. Treat each startup as a track in a larger mix: together they sketch how Australia is moving from generic experimentation to deeply embedded AI.

If you’re an engineer or student

Use this list to reverse-engineer where skills are compounding fastest along the Sydney-Melbourne corridor. Heidi Health and Harrison.ai show the bar for clinical AI; Lorikeet and Relevance AI reveal what production-grade agents actually look like; TrueState hints at the governance and MLOps expertise big employers will pay for. To go deeper, cross-check these names against talent hotlists like Matchstiq’s Top 100 Australia and look at who they’re hiring, not just who they’re funding.

• Pick a vertical (health, law, mining, climate, govtech) and learn its data and regulation
• Understand the infra layer: orchestration, observability, compliance and security
• Seek out teams treating AI as “staff”, not as a side project

If you’re a founder or investor

The pattern across these ten is clear: capital is consolidating around clear category leaders, with late-stage cheques concentrating in Sydney while Melbourne leans into health-tech and robotics. Analyses like InvestorDaily’s breakdown of Australia’s AI funding point to fewer, larger rounds and a bias toward teams with proprietary data and strong distribution. Use this list to stress-test your own thesis: are you backing another point solution, or something that can become part of the rails others build on?

What to watch next

Finally, read the gaps in the predictions sheet. Defence, agriculture, climate resilience, and regional tech hubs from Brisbane to Perth are under-represented here, even as grants and corporate demand ramp up. The next wave of “deep cuts” will come from those margins. Keep an eye on seed rounds outside the main corridor, follow researchers spinning out of universities and CSIRO, and keep asking, every time a new list drops: who’s missing, and what does that say about where Australia’s AI still needs you most?

Frequently Asked Questions