ZTABS builds AI solutions for Melbourne businesses — custom machine learning models, predictive analytics, NLP, and computer vision. Enterprise-grade AI for Australian healthcare, finance, agriculture, and resources.
AI development in Melbourne serves Australian businesses building intelligent systems in a city that has emerged as Australia's AI capital — home to the largest concentration of AI researchers outside of Sydney, anchored by world-class institutions (University of Melbourne, Monash University, RMIT), and buoyed by the Victorian Government's explicit investment in becoming the country's AI and digital hub. Melbourne's AI landscape combines deep research capability (the ARC Centre of Excellence for Automated Decision-Making and Society is headquartered here), a diverse economy that creates varied AI use cases (from agricultural tech in Victoria's farming hinterland to financial services in the CBD), and a regulatory environment shaped by Australia's AI Ethics Framework and the Privacy Act 1988. Melbourne's AI context: what shapes development here. Melbourne's AI ecosystem: Melbourne has Australia's deepest AI research bench, with more AI PhD graduates per capita than Sydney and direct research-to-industry pipelines. University of Melbourne: top-ranked globally for AI research — machine learning, NLP, and computer vision groups publishing in top-tier venues (NeurIPS, ICML, CVPR). Monash University: strong in data science and healthcare AI — the Monash Data Futures Institute connecting research to industry. RMIT: applied AI — computer vision, robotics, and industry partnerships. CSIRO's Data61: Australia's national science agency's data and digital arm — headquartered in part in Melbourne, conducting AI research with direct industry application. Victorian Government: the state investing in AI through the Victorian AI Strategy — funding AI skills development, supporting AI adoption in government services, and attracting AI companies to Melbourne. Australia's AI regulation: Australia's approach to AI governance — not legislating an AI Act (unlike the EU) but establishing voluntary frameworks. AI Ethics Framework: 8 principles including accountability, transparency, fairness, and human-centred values — voluntary but increasingly referenced in procurement and industry standards. Privacy Act 1988: governing personal data handling — AI systems processing Australian personal information must comply with Australian Privacy Principles (APPs). Notifiable Data Breaches: mandatory breach notification — AI systems experiencing data breaches must notify the OAIC (Office of the Australian Information Commissioner) and affected individuals. Consumer Data Right (CDR): open banking and energy data — AI systems in banking and energy accessing consumer data under CDR must meet strict security and consent requirements. Australia's industry landscape for AI: Melbourne's diverse economy creating varied AI demand. Financial services: Melbourne hosting major banks' technology centres (ANZ headquarters, NAB headquarters) and a thriving fintech ecosystem. Healthcare: Australia's largest health and medical research cluster — Parkville Precinct housing multiple hospitals, research institutes, and biotech companies. Agriculture: Victoria producing a third of Australia's food — AI for precision agriculture, livestock monitoring, and supply chain optimisation. Manufacturing: advanced manufacturing in Melbourne's northern and western suburbs — quality inspection, predictive maintenance, and process optimisation. Professional services: Australia's second-largest professional services market — law, consulting, and accounting firms adopting AI for document review, research, and analysis.
Melbourne's AI development demand by sector: (1) Financial services: banking and fintech. Finance AI: fraud detection (ANZ, NAB, and Melbourne fintechs — real-time transaction fraud detection. AI: models analysing transaction patterns, identifying anomalies, and scoring fraud probability. Melbourne specifics: Australian transaction patterns, PayTo integration, and NPP (New Payments Platform) real-time fraud requirements), credit scoring (alternative credit scoring — AI models using non-traditional data to assess creditworthiness. CDR data: with consumer consent, AI accessing banking transaction data for more accurate credit assessment. Melbourne fintech: startups building AI-powered lending decisions), customer intelligence (banking customer analytics — predicting churn, identifying cross-sell opportunities, and personalising offers. AI: customer behaviour models trained on Australian banking patterns. Scale: ANZ and NAB having millions of customers — models needing to perform at scale), and regulatory compliance (RegTech AI — automated compliance monitoring, suspicious transaction detection, and regulatory reporting. AUSTRAC: Australian Transaction Reports and Analysis Centre requiring financial institutions to monitor and report suspicious transactions — AI automating detection). (2) Healthcare and life sciences: Melbourne's health precinct. Health AI: medical imaging (AI-assisted radiology and pathology — detecting abnormalities in medical images. Melbourne: Parkville Precinct hospitals generating massive imaging volumes. AI: models assisting radiologists with detection of cancers, fractures, and other conditions. TGA: Therapeutic Goods Administration regulating AI medical devices — classification determining regulatory pathway), drug discovery (pharmaceutical AI — Melbourne's biotech companies (CSL, Opthea) and research institutes using AI to identify drug candidates. AI: molecular modelling, protein structure prediction, and clinical trial optimisation), clinical decision support (AI assisting clinicians with diagnosis and treatment planning. Melbourne hospitals: Royal Melbourne Hospital, Peter MacCallum Cancer Centre, and Alfred Health implementing clinical AI. Evidence: AI synthesising clinical evidence, patient data, and treatment guidelines into decision support), and population health (public health analytics — Victorian DHHS using data to monitor population health trends, predict disease outbreaks, and optimise resource allocation. AI: predictive models for hospital demand, disease surveillance, and health service planning). (3) Agriculture: Victoria's farming sector. AgTech AI: precision agriculture (crop monitoring — satellite and drone imagery analysed by computer vision to assess crop health, detect disease, and optimise irrigation. Victorian agriculture: broadacre cropping in the Wimmera, horticulture in the Yarra Valley, and dairy in Gippsland. AI: models trained on Australian crop varieties and conditions), livestock (livestock monitoring — AI analysing sensor data from cattle and sheep for health monitoring, breeding optimisation, and production prediction. Scale: Victoria's dairy industry worth A$4.7B — AI improving herd management and milk production), supply chain (agricultural supply chain optimisation — demand forecasting, logistics optimisation, and quality prediction. AI: predicting harvest volumes, optimising transport routes, and managing cold chain. Export: Australian agricultural exports to Asia requiring quality assurance AI), and water (water management — AI optimising irrigation scheduling based on weather prediction, soil moisture, and crop requirements. Victoria: water scarcity making efficient irrigation critical — AI reducing water usage by 15-30% while maintaining yield). (4) Resources and mining: Victoria and broader Australian mining. Mining AI: exploration (mineral exploration — AI analysing geological data, satellite imagery, and historical drilling data to identify exploration targets. Victoria: gold mining heritage — modern AI helping identify remaining deposits), predictive maintenance (mining equipment maintenance — AI predicting failures in trucks, conveyors, and processing equipment. Scale: single mining truck costing A$5M+ — unplanned downtime extremely expensive), autonomous systems (autonomous mining vehicles — AI enabling autonomous haul trucks, drill rigs, and load-haul-dump vehicles. Melbourne: many mining companies headquartered in Melbourne — AI development done in Melbourne for operations across Australia), and safety (mine safety AI — AI monitoring safety conditions, predicting hazards, and analysing incident patterns. Workers: protecting workers through AI-powered hazard detection and early warning). (5) Professional services and legal: Melbourne's professional sector. Professional AI: document review (legal document review — AI analysing contracts, due diligence documents, and regulatory filings. Melbourne law: large firms (Allens, Herbert Smith Freehills, King & Wood Mallesons) adopting AI for document review. Volume: M&A due diligence rooms with 100,000+ documents — AI reducing review time by 60-80%), research (professional research AI — law, consulting, and accounting firms using AI to research case law, regulations, and market data. Australian: AI trained on Australian legal and regulatory corpus), audit (audit AI — accounting firms using AI for transaction analysis, anomaly detection, and risk assessment. Scale: AI analysing millions of transactions versus human sampling of hundreds), and recruitment (HR AI — recruitment automation, candidate matching, and workforce planning. Australian HR: AI helping Melbourne employers navigate Australia's complex employment landscape — awards, enterprise agreements, and visa requirements).
Our Melbourne AI development follows a methodology for Australian business requirements: (1) Discovery (weeks 1-2): understanding the AI opportunity. Problem definition: precisely defining the business problem AI will solve — what decision needs to be improved, what process needs automation, or what pattern needs detection. Australian context: understanding Australian regulatory requirements, industry standards, and data availability. Data assessment: evaluating available data — volume, quality, relevance, and accessibility. Privacy: assessing personal information under the Privacy Act — determining what data can be used for AI training and under what conditions. Feasibility: technical feasibility assessment — can AI realistically solve this problem? What accuracy is achievable? What data would be needed? Honest assessment: not every problem benefits from AI — we provide candid evaluation, sometimes recommending simpler approaches. (2) Data engineering (weeks 2-4): building the data foundation. Data collection: gathering and consolidating training data from relevant sources. Australian data: working with Australian data formats, standards, and sources — ABS (Australian Bureau of Statistics) data, AIHW (Australian Institute of Health and Welfare) data, and industry-specific Australian datasets. Data cleaning: handling missing values, inconsistencies, and errors — Australian data having specific characteristics (geographic distances creating sparse data, seasonal patterns following Southern Hemisphere calendar). Feature engineering: creating meaningful features from raw data — transforming business data into inputs that ML models can learn from. Australian features: features reflecting Australian business context — financial year (July-June), seasonal patterns (summer December-February), and geographic factors (Melbourne-centric with vast distances to other centres). Privacy: de-identification, anonymisation, and consent management — ensuring training data complies with Australian Privacy Principles. Health data: additional protections for health information under the Privacy Act — health AI data requiring enhanced privacy measures. (3) Model development (weeks 3-6): building AI models. Algorithm selection: choosing appropriate ML approaches — supervised learning (classification, regression), unsupervised learning (clustering, anomaly detection), reinforcement learning (optimisation, control), or deep learning (image recognition, NLP, time series). Australian: model selection considering Australian data characteristics — smaller datasets than US/China (23 million population), specific industry patterns, and Australian language variations. Training: iterative model training — training/validation/test splits, hyperparameter tuning, and cross-validation. Evaluation: rigorous model evaluation — accuracy, precision, recall, F1 score, and business-specific metrics. Fairness: evaluating model fairness across Australian demographic groups — ensuring AI doesn't discriminate based on protected attributes (race, gender, age, disability). Explainability: building interpretable models or adding explainability layers — Australian consumers and regulators increasingly expecting AI decisions to be explainable. (4) Integration (weeks 5-7): deploying AI into business systems. API: exposing AI models through APIs — REST or gRPC endpoints for real-time inference. Batch: batch prediction pipelines for large-volume offline processing. Edge: edge deployment for latency-sensitive or connectivity-limited applications (agriculture, mining). Australian infrastructure: deploying on Australian cloud regions — AWS Sydney, Azure Australia East, or GCP Sydney — ensuring data residency for Australian data. Monitoring: model monitoring — tracking prediction accuracy, data drift, and model degradation over time. Alerting: automated alerts when model performance degrades — preventing silent model failure. (5) Governance (ongoing): responsible AI operation. AI governance: establishing governance processes — model documentation, decision audit trails, and regular model reviews. Australian AI Ethics: aligning with Australia's 8 AI Ethics Principles — documenting how the AI system addresses each principle. Privacy compliance: ongoing Privacy Act compliance — maintaining records of processing, responding to access requests, and notifying breaches. Continuous improvement: model retraining on new data, expanding to new use cases, and adapting to changing business requirements.
Agriculture / AgTech / Predictive Analytics
A Melbourne-based agricultural technology company (A$28M revenue — providing farm management software to 4,500 farms across Victoria, NSW, and South Australia, 95 employees, headquarters in Richmond) needed AI to transform their platform from record-keeping to predictive decision support. (1) Crop yield: Victorian broadacre farmers (wheat, barley, canola) making planting and input decisions based on experience and historical averages — not predictive models. Current: farmers estimating yield based on "good year/bad year" intuition. Impact: suboptimal fertiliser application (under-applying losing yield, over-applying wasting money and environmental damage), planting decisions based on historical rather than predicted conditions, and harvest logistics planned without accurate volume predictions. Data: 7 years of farm management data from 4,500 farms — planting dates, input applications, soil tests, yield records, and weather station data. The opportunity: predicting yield at paddock level 60-90 days before harvest — enabling optimised input decisions, better forward contracts, and efficient harvest logistics. (2) Livestock health: dairy and beef operations monitoring herd health manually — visual observation, scheduled veterinary visits, and reactive treatment. Issue: livestock health problems detected late — by the time visual symptoms appear, the animal has been unwell for days. Dairy: mastitis detection costing Victorian dairy industry A$180M/year — early detection reducing treatment cost and milk loss. Data: milk production records, activity sensors (pedometers), and weather/housing data — patterns preceding health events detectable by AI but invisible to human observation. (3) Water management: Victorian water scarcity — farms allocated water rights with limited and variable supply. Current: irrigation scheduled by calendar or visual assessment — not optimised for actual crop water needs. Waste: estimated 25-35% of agricultural water use being inefficient — either over-irrigating (wasting water) or under-irrigating (reducing yield). Data: soil moisture sensors, weather forecasts, satellite imagery (NDVI vegetation index), and crop growth models — sufficient data for AI-optimised irrigation scheduling. (4) Market timing: farmers selling commodities at harvest (spot price) — rarely optimising timing for better prices. Current: "sell when the truck arrives" approach — no predictive price modelling. Victoria: wheat prices fluctuating A$50-100/tonne within marketing year — timing selling to capture better prices adding A$15,000-50,000 per farm annually for average broadacre operations. Data: historical commodity prices (ASX grain futures), global supply/demand data, weather patterns (El Niño/La Niña), and shipping indicators — predictable patterns that AI can model. (5) Platform differentiation: the company's farm management software competing with John Deere Operations Center, FarmBot, and AgWorld — needing AI capabilities to differentiate and increase platform stickiness. Churn: annual customer churn at 18% — farmers switching to competitors or reverting to spreadsheets. Value: farmers asking "what should I do?" not just "what did I do?" — the platform currently answering the latter but not the former.
We built a predictive analytics AI platform integrated into the farm management software over 10 weeks. (1) Yield prediction model: paddock-level crop forecasting. Architecture: ensemble model combining gradient-boosted trees (XGBoost) for tabular data with convolutional neural network (CNN) for satellite imagery — producing paddock-level yield predictions at 60, 45, and 30 days before harvest. Data inputs: historical yield records (7 years × 4,500 farms), soil characteristics (texture, organic carbon, pH, from soil test records), weather data (Bureau of Meteorology — rainfall, temperature, solar radiation, and evapotranspiration), satellite imagery (Sentinel-2 — NDVI, NDWI vegetation indices at 10m resolution every 5 days), planting data (crop variety, planting date, seeding rate, fertiliser applications), and topography (elevation, slope, aspect from SRTM digital elevation model). Training: 31,500 paddock-year records (4,500 farms × 7 years) — split 70/15/15 train/validation/test. Victorian specifics: model trained on Victorian conditions — cool temperate to semi-arid climate, southern hemisphere growing season, and Australian crop varieties (Scepter wheat, Compass barley, InVigor canola). Accuracy: yield prediction within ±8% of actual at 60 days before harvest, improving to ±5% at 30 days — compared to farmer estimates averaging ±22% deviation. (2) Livestock health model: early disease detection. Architecture: time-series anomaly detection using LSTM (Long Short-Term Memory) neural networks — monitoring daily patterns and detecting deviations that precede health events. Data inputs: daily milk production per cow (volume, components), activity data (pedometer — steps, lying time, rumination), milking parlour data (flow rate, conductivity), and environmental data (temperature, humidity). Training: 2.3 million cow-day records from 380 dairy farms — labelled with health events (mastitis, lameness, metabolic disorders) from veterinary treatment records. Victorian dairy: model calibrated for Victorian dairy conditions — pasture-based systems (different patterns from housed US/European systems), seasonal calving patterns, and Victorian breeds (predominantly Holstein-Friesian). Early detection: model detecting mastitis 2.8 days before clinical symptoms — detecting 78% of cases with 6% false positive rate. Lameness: detecting 3.1 days early with 72% accuracy. Metabolic: detecting ketosis 1.9 days early with 68% accuracy. (3) Irrigation optimisation: AI-driven water scheduling. Architecture: reinforcement learning agent — optimising irrigation scheduling by balancing crop water needs, soil moisture, weather forecasts, and water allocation constraints. Data inputs: soil moisture sensors (real-time), weather forecasts (7-day BOM forecasts), satellite vegetation index (crop stress indicator), crop growth model (FAO-56 Penman-Monteith evapotranspiration), water allocation (remaining seasonal allocation), and energy costs (pumping cost varies by time of day). Optimisation: the RL agent learning to schedule irrigation that maximises yield per megalitre of water — considering the farmer's total seasonal water allocation and uncertain future rainfall. Victorian water: model understanding Victorian water allocation framework — high-reliability versus low-reliability water rights, and temporary water trading. Result: reducing water usage by 22% while maintaining yield within 2% of fully irrigated maximum — or increasing yield by 12% with the same water allocation. (4) Market intelligence: commodity price prediction. Architecture: transformer model for time-series price prediction — predicting wheat, barley, and canola prices at ASX delivery points. Data inputs: ASX grain futures (10 years of daily data), global commodity markets (CBOT wheat, Euronext canola), shipping rates (Baltic Dry Index — grain freight indicator), global production forecasts (USDA WASDE, ABARES), weather patterns (El Niño Southern Oscillation — strong correlation with Australian grain production), and AUD/USD exchange rate (Australian grain priced in global market). Prediction: 30-day price direction with 64% accuracy, 90-day trend direction with 71% accuracy — not claiming to predict exact prices, but identifying whether prices are likely higher or lower than current. Actionable: combined with yield prediction — farmer knowing approximately how much grain they'll harvest and approximately where prices are heading — can make informed forward selling decisions. Victorian pricing: model understanding Victorian basis (difference between local price and ASX) — local delivery point pricing versus port pricing. (5) Platform integration: seamless farmer experience. Dashboard: predictive insights displayed alongside existing farm management data — yield predictions on paddock maps, health alerts in herd management, irrigation recommendations in water management, and price outlook in marketing. Alerts: push notifications for actionable predictions — "Cow 247 showing early mastitis indicators — check within 24 hours," "Paddock 12 yield forecast revised down 15% — consider additional nitrogen," "Wheat price likely to rise A$8-12/tonne over next 30 days — consider delaying sale." Farmer-friendly: predictions presented in farming language, not data science — yield in tonnes/hectare (not model outputs), health in plain language (not anomaly scores), and pricing in dollars/tonne (not probability distributions). Mobile: all predictions accessible on mobile — farmers in the paddock accessing insights. API: predictions available via API — enabling integration with other farm tools, grain marketing platforms, and advisory services.
Results over 12 months across participating farms. Yield prediction: 2,800 farms using yield prediction in first season. Accuracy: ±7.2% at 60 days before harvest (target: ±8%), ±4.8% at 30 days (target: ±5%). Farmer value: improved forward contracting — farmers securing forward contracts at predicted volumes with less risk of under/over-delivery. Fertiliser optimisation: yield predictions enabling variable-rate nitrogen application — estimated A$12-18/hectare saving (A$8,400-12,600 per average 700ha Victorian broadacre farm). Total value: estimated A$45M in improved decisions across participating farms (yield prediction + input optimisation). Livestock health: 380 dairy farms using health prediction. Mastitis: early detection in 78% of cases (2.8 days earlier than clinical detection). Treatment cost: mastitis treatment cost reduced 35% through earlier intervention (less severe infections requiring less treatment). Milk loss: mastitis-related milk loss reduced 42% (earlier treatment meaning faster recovery). Per-farm value: average A$18,000/year saving per dairy farm from health prediction. Total value: A$6.84M across participating dairy farms. Water: 520 irrigated farms using optimisation. Water saving: 22% average reduction in water usage (maintaining yield within 2% of unrestricted irrigation). Water value: at A$150-400/ML temporary water price — average A$12,000-32,000 per farm in water cost saving or tradeable water value. Yield improvement: farms with constrained water allocation seeing 12% yield improvement through optimised scheduling. Total value: estimated A$12.5M in water savings and yield improvement across participating farms. Market: 1,200 farms receiving price intelligence. Timing: farmers using price predictions delaying or advancing grain sales by average 3-6 weeks — capturing A$8-15/tonne improvement over harvest-time selling. Per-farm value: average A$7,000-21,000 per broadacre farm. Total value: estimated A$14.4M in improved grain marketing across participating farms. Platform: customer churn from 18% to 9% (50% reduction) — farmers finding predictive features too valuable to leave. New customers: 1,100 new farm sign-ups attributed to AI features (word of mouth from existing users). Revenue: platform subscription revenue increased A$4.2M (from increased retention + new customers + premium AI tier pricing). Investment: A$580,000 development cost (10-week project). Annual operation: A$180,000 (cloud compute, satellite data subscriptions, and model maintenance). Platform revenue increase: A$4.2M/year. Farmer value created: A$78.7M across all participating farms. ROI: platform revenue alone delivers 7.2x first-year return on development investment.
Integrate GPT-4, Claude, Gemini, or open-source models into your product with custom fine-tuning for your domain.
Autonomous agents that handle complex workflows — lead qualification, support, data processing, and operations.
Turn your documents, data, and knowledge base into searchable AI-powered systems with accurate, cited answers.
Custom ML models for forecasting, classification, anomaly detection, and recommendation engines.
Image analysis, document understanding, text classification, sentiment analysis, and entity extraction.
Smart search, content generation, summarization, personalization, and intelligent recommendations for your SaaS.
Melbourne's AI development market includes consultancies (PwC, Deloitte, Accenture — enterprise AI consulting at A$350-600/hour), Australian AI companies (Canva AI, Appen, Harrison.ai — product-focused, not custom development), research institutes (CSIRO Data61, university spin-outs — research-grade but sometimes slow to productionise), cloud vendors (AWS, Azure, GCP — AI platform services with professional services), and boutique firms (Melbourne AI consultancies — deep technical capability but varying industry knowledge). ZTABS provides AI development with Australian industry understanding — agricultural, healthcare, financial services, and resources domain expertise — combined with production-grade engineering, Privacy Act compliance, and AI Ethics Framework alignment.
AI development in Melbourne ranges from A$80,000-200,000 for focused AI solutions (single model, defined problem, 6-10 weeks) to A$200,000-600,000 for comprehensive AI platforms (multiple models, system integration, production deployment, 10-20 weeks). Annual operation: A$30,000-120,000 (compute, data feeds, and model maintenance). ZTABS provides AI development with Australian regulatory and industry expertise.
Verified reviews from real client engagements — sourced from our public testimonial archive and Clutch profile.
My experience is throughout positive. Communication, service, the short response times and the flawless execution of a challenging topic was absolutely great. ZTABS is definitely my first choice again.
Christian Neff
Bank Software Advisory · Bank Software Advisory
Fintech
Fantastic Agency! I couldn't fault them even if I tried. They always go above and beyond to meet your expectations and always produces quality work. Thank you ZTABS.
Stephanie Kal
CEO · Beauty Finder Australia
Marketplace
It has been great working with ZTABS. They bounce off the ideas along the way. Amazing Experience.
Joel Rowe
CEO · Drill Quoter
Marketplace
We don't just contract — we ship and operate our own software. 17 products in production.
Common questions about AI development for Melbourne businesses
The Privacy Act 1988 and Australian Privacy Principles govern how personal information is used in AI. Training data containing personal information requires lawful collection, clear purpose, and appropriate security. Health data has additional protections. We build Privacy Act compliance into AI development from the start.
ZTABS is a remote-first AI development agency serving Houston businesses — including llm integration & fine-tuning, ai agents & automation, rag & knowledge systems. We work with Energy & Oil/Gas, Healthcare & Biotech, Aerospace & Defense companies in Houston, TX via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
AI Development in New York, NYZTABS is a remote-first AI development agency serving New York businesses — including llm integration & fine-tuning, ai agents & automation, rag & knowledge systems. We work with Finance & Fintech, Media & Advertising, Fashion & Retail companies in New York, NY via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
AI Development in Los Angeles, CAZTABS is a remote-first AI development agency serving Los Angeles businesses — including llm integration & fine-tuning, ai agents & automation, rag & knowledge systems. We work with Entertainment & Media, E-commerce & DTC Brands, Gaming & AR/VR companies in Los Angeles, CA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Web Development in Melbourne, AustraliaZTABS is a remote-first web development agency serving Melbourne businesses — including full-stack development, progressive web apps, api development. We work with EdTech, HealthTech, AgriTech companies in Melbourne, Australia via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Web Design in Melbourne, AustraliaZTABS is a remote-first web design agency serving Melbourne businesses — including ui/ux design, responsive design, custom interfaces. We work with EdTech, HealthTech, AgriTech companies in Melbourne, Australia via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Digital Marketing in Melbourne, AustraliaZTABS is a remote-first digital marketing agency serving Melbourne businesses — including search engine optimization, pay-per-click advertising, social media marketing. We work with EdTech, HealthTech, AgriTech companies in Melbourne, Australia via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
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