Zeroform.ai.

The problem

Early-stage residential design lacks a real-time sustainability and performance decision-support layer. Architects can't rapidly evaluate how design choices affect BASIX compliance, NatHERS performance, or construction cost while they design.

Instead, feedback is delayed through fragmented, manual assessor workflows, slowing iteration and increasing rework risk. The result is redesigns, project delays, cost overruns, and compromised design outcomes — a serious drag in a market under pressure to deliver housing faster.

The solution

ZeroForm.ai is an early-stage sustainability co-pilot for Australian residential architects and building designers, focused on NSW small-scale residential projects during Schematic Design.

An architect describes a project by voice or text; the tool extracts confirmed facts and AI assumptions, identifies missing inputs, asks the highest-impact priority questions, and returns readiness guidance across Water, Thermal/NatHERS, Energy, and Materials with a risk scorecard and recommended design moves. Critically, it is explicitly not a consultant, assessor, certifier, or formal compliance tool — the output is an export-ready summary that streamlines handoff to consultants and assessors.

How it works

The product uses GPT-5.5 as the primary model, chosen for complex reasoning across incomplete design inputs, BASIX/NatHERS concepts, uncertainty handling, and practical trade-offs. The UI sends project inputs to a backend that enriches the request with prompt instructions and retrieved evidence via the Responses API; the model returns structured JSON rendered as confirmed facts, assumptions, missing inputs, priority questions, risk scorecards, and recommendations. A smaller model such as gpt-5.4-mini can handle lower-risk extraction and classification.

RAG is not used in the MVP — it relies on local workflow logic, structured prompts, and safety guardrails, with future retrieval planned over authoritative BASIX, NSW Planning, NatHERS, and NCC sources. Guardrails ban "guaranteed pass" language, use approved risk labels (Low to High and Unknown), separate assumptions from facts, and require architect validation before assumptions inform suggestions. Early evaluation is strong: the deterministic local rules-engine passed 11 of 11 cases and a live OpenAI prompt evaluation passed 19 of 21 checks (90%).

Who it's for

The product is B2B, sold to individual or small-scale residential architects and designers working on bespoke developments. Their goal is to design and get approved residential developments that meet client needs and satisfy compliance requirements.

Revenue is a subscription or one-off payment per project. Because ZeroForm.ai operates near regulated assessment workflows, the design deliberately keeps formal compliance with official BASIX/NatHERS tools, sustainability consultants, and accredited assessors.

Why it matters

Australia faces a structural housing undersupply against a government target of 1.2 million new homes by 2029 — roughly 240,000 homes a year, versus about 177,000 delivered in 2024. Meeting it requires a 35–40% increase in annual construction, even as slow, manual approval processes and rising costs slow delivery.

As an early-stage startup, the launch plan is staged: internal testing with 5–10 project examples, expert review with a sustainability consultant, a closed pilot with 3–5 architects, then broader beta once hallucination and workflow risks are controlled. Success is measured by useful early-stage guidance with zero fabricated pass, score, certificate, or rating claims.

At a glance

Project

Zeroform.ai

Built by

Robert Petrovic

One-liner

Zeroform.ai is an AI sustainability co-pilot for Australian residential architects working on early-stage approval readiness.