What does an AI proof of concept actually cost in Australia?
An AI proof of concept in Australia costs between $15,000 and $60,000 AUD and takes 2 to 6 weeks to complete. The price varies based on data complexity, the number of scenarios being tested, and how many systems need to connect. Kernel Flow has run dozens of AI PoCs across wholesale, manufacturing, insurance, and professional services businesses.
A proof of concept is the single most important investment in any AI project. It tests whether your idea works with your actual business data, not sample data or theoretical scenarios. Skipping it to reduce upfront costs consistently produces the most expensive outcomes.
What should an AI proof of concept actually deliver?
A proof of concept is working software, not a presentation or a demo. It proves or disproves that AI can solve your specific problem at an acceptable level of accuracy and cost. At Kernel Flow, every PoC is built with your real data and produces a clear go/no-go recommendation backed by evidence.
Working prototype with real data: The system is built and tested using your actual business data, not synthetic or cherry-picked samples.
Accuracy and performance metrics: Every PoC produces measurable results against your defined success criteria, including speed and throughput benchmarks.
Technical architecture for production: A clear blueprint shows how the system scales from prototype to full deployment inside your existing software environment.
Production cost estimate: You receive a realistic cost projection for the full build and ongoing operations before committing any further budget.
Go/no-go recommendation: An honest assessment of what works, what does not, and whether the full investment is justified by the evidence.
What are the three AI proof of concept pricing tiers?
Proof of concept costs fall into three tiers based on the complexity of the use case. Each tier has a defined scope, clear deliverables, and a realistic timeline. Choosing the right tier depends on the number of data sources, system integrations required, and the stakes of getting it wrong.
Simple PoC: $15,000 to $25,000 AUD (2 to 3 weeks): Tests a single, well-defined use case with one data source and no system integrations. Typical examples include classifying customer emails into categories, extracting key fields from documents at 90-plus percent accuracy, or summarising meeting transcripts. Delivers a data assessment, working prototype, accuracy metrics, and a findings report.
Standard PoC: $25,000 to $45,000 AUD (3 to 5 weeks): Tests use cases involving multiple data sources, business logic, or one to two basic system integrations such as reading from a CRM or knowledge base. Covers most business use cases across sales, operations, and finance. Delivers detailed data assessment, workflow logic testing, performance benchmarks, a technical architecture document, and a production cost estimate.
Complex PoC: $45,000 to $60,000 AUD (4 to 6 weeks): Tests high-value use cases with multiple decision paths, several system integrations, compliance requirements, or difficult data quality. Examples include loan application workflows, multi-system data reconciliation across legacy platforms like SAP or older ERPs, and clinical report triage. Delivers everything in the standard tier plus compliance and security assessment, edge case and failure mode analysis, and a phased production implementation plan.
What factors drive an AI proof of concept price higher?
Data quality is the biggest cost driver in any proof of concept. When business data is inconsistent, incomplete, or spread across multiple systems in different formats, significant time goes into data preparation before AI testing can begin. Kernel Flow budgets 20 to 40 percent of PoC time for data work. Any implementation team that does not ask about data quality early is assuming it is clean, which is a risk to your budget and your results.
Multiple testing scenarios: Each scenario requires its own test data, evaluation criteria, and tuning. Prioritise the two or three highest-value scenarios for the PoC and save the rest for the production build to keep costs controlled.
Legacy system integrations: Connecting to a modern API with clear documentation is straightforward. Connecting to a legacy system with poor documentation, no sandbox environment, or limited API support adds significant time just getting the connection operational.
Compliance and regulatory requirements: Use cases in insurance, financial services, or healthcare that involve regulatory obligations require additional compliance and security assessment, which adds both time and cost to the PoC scope.
How should CEOs and COOs evaluate a proof of concept proposal?
A credible proof of concept proposal specifies which use cases will be tested, what data is required, how success will be measured, and what the production pathway looks like. Proposals that focus on general AI capabilities rather than your specific problem are advisory engagements, not PoC builds.
Kernel Flow produces working systems, not slide decks. Every PoC engagement ends with running software, measurable accuracy results, and a production estimate you can take to your board or leadership team with confidence. For businesses running between $10M and $50M in revenue, a well-scoped PoC reduces implementation risk and protects capital before committing to a full AI system build.