Grants, AI readiness and honest pricing — no gated PDFs.

The AU/NZ Startup Grant & MVP Funding Guide (2026)

Most founders leave real money on the table simply because grant programs are scattered and slow to research. Here's the short version, current as of mid-2026.

Australia

• R&D Tax Incentive — a 43.5% refundable offset on eligible R&D spend for companies under AUD $20M turnover. If you're writing genuinely novel software (not just configuring off-the-shelf tools), a chunk of your build cost is likely eligible. Talk to an R&D tax specialist before you file, not after.
• NSW MVP Ventures Program — matched funding plus mentoring for MVP-ready NSW startups. Useful specifically for the build-and-validate stage, which is exactly where Sentriqa plays.
• Export Market Development Grant (EMDG) — reimburses up to 50% of export promotion costs, capped at $770k. Relevant once you're marketing into NZ or the US.
• Accelerating Commercialisation — matched funding to help commercialise novel products; worth checking eligibility if your product is IP-led.

New Zealand

• NZ's government AI strategy deliberately favours adoption and application over foundational research — good news if you're applying AI to a real business problem rather than building a model from scratch.
• The NZIAT AI Platform funding ramps up from July 2026 — worth tracking if your MVP has an AI core.
• Auckland remains NZ's highest-density startup and capital hub — where we're launching first.

The practical sequence

Grants fund validation, not vibes — most programs want to see a real product or a credible plan to build one. Our usual sequence with founders: scope the MVP and fixed price first, map it against 2–3 relevant grants, then apply in parallel with the build so the money and the product land close together.

The Mid-Market AI Readiness Checklist

Around 95% of AI pilots stall before production — almost never because the model is bad. It's integration, data and governance. Score yourself honestly against this before you brief anyone (including us).

Data

• Do you know where the relevant data actually lives (which systems, whose spreadsheets, what's still on paper)?
• Is it clean enough to trust, or will prep eat the budget? (Realistically, data prep is 30–50% of most AI project costs — budget for it explicitly.)
• Who owns data quality once the system is live?

Integration

• Which systems does this need to talk to (CRM, helpdesk, ERP)? Each integration typically adds 1–3 weeks.
• Is there a single, named owner on your side who can approve access and unblock IT questions fast?

Governance

• What's the human-in-the-loop point — where does a person check the AI's work before it matters?
• How will you measure whether it's actually working (accuracy, time saved, escalation rate)?
• Who's accountable if it gets something wrong?

If you scored "not sure" more than twice, that's exactly what a paid AI Readiness Audit is for — it's a fixed-fee way to get honest answers before you commit to a build.

What a Fundable MVP Actually Costs in 2026

Founders usually hear one number from one agency and have no way to sanity-check it. Here are real, current bands.

Where founders waste budget

• Scope creep before a fixed quote. Every "just one more feature" before launch pushes both cost and timeline.
• Skipping the prototype step. A visual prototype before building removes the most expensive kind of rework — the kind after code is written.
• Ignoring data prep costs on AI features. Budget 30–50% of an AI feature's cost for data cleanup, not just the model integration.
• Paying pure-onshore rates for commodity work. A blended onshore-lead / offshore-build model (our model) typically lands 40–70% below pure onshore pricing for the same quality bar — the savings should go into iteration, not into an agency's margin alone.

The AI Tools We Actually Use (and Why "Just Use ChatGPT" Isn't a Strategy)

Every client eventually asks which AI we use. The honest answer: it depends on the job, and we deliberately stay model-agnostic rather than betting a client's product on one vendor.

Models

• Anthropic Claude — our default for reasoning-heavy tasks, coding assistance, and anything that needs to follow careful instructions reliably.
• Google Gemini — strong for multimodal work (documents, images) and tight integration with Google Workspace-heavy businesses.
• Meta Llama / Mistral — open-weight options where a client needs on-prem or cost-sensitive deployment, or fine-tuning control.
• Perplexity — for research and retrieval-style tasks where fresh, cited web information matters.
• ElevenLabs — voice and audio interfaces where a product needs to sound natural, not robotic.

Infrastructure

• LangChain for agent orchestration, Pinecone for vector search at scale, Supabase for fast, secure app backends, and Vercel for shipping web apps that stay fast globally.

The actual point

The model is rarely the bottleneck in 2026 — reliability is. Every AI feature we ship gets evals (automated tests of whether it's actually right), guardrails (limits on what it's allowed to do), and a human-in-the-loop checkpoint somewhere that matters. That discipline, more than any specific model choice, is what separates a shipped AI feature from a demo that quietly gets switched off three months later.