top of page
Won by turning municipal zoning law into instantly queryable data, letting a property developer learn in seconds what used to take a planning consultant weeks to determine.
1
MODEL
BUSINESS MODEL
SaaS, Data Platform
model bm
HOW THEY BUILT IT
- Founded in 2010 (publicly launched 2018) by brothers Dr. Benjamin Coorey (an architect with a PhD in 3D generative building design) and Robert Coorey, combining architectural design expertise with AI.
- Raised a $6M Series A in 2020 led by AirTree Ventures (also an early investor in Canva), followed by an $11M round in 2022 that funded its first acquisition (Snaploader, for interactive 3D real estate marketing) and US expansion starting in Dallas.
- Built the most comprehensive planning-rule database in Australia, partnering with data providers like CoreLogic and Domain, and has since signed government contracts (Victoria, NSW, and cities in the US and Canada) to help automate building-permit assessment.
HOW TO ARCHITECT IT
1. Digitize a fragmented, jurisdiction-by-jurisdiction regulatory dataset (zoning and planning rules) that currently requires expensive expert consultants to interpret manually.
2. Serve both private-sector customers (developers, architects) and government customers (planning departments) with the same underlying data asset, since government adoption validates accuracy for private buyers and vice versa.
3. Expand internationally market by market, only entering a new country once you've mapped and validated its specific regulatory rules - the data-compilation work, not the software, is the actual expansion bottleneck.
DISTRIBUTION MODEL
Direct Sales, Partnership Distribution, Channel Sales
dm
HOW THEY OPERATIONALIZED
- Strategic partnerships with industry-leading data providers (CoreLogic, Domain) and government bodies validate data accuracy and expand distribution.
- Thought leadership through webinars and research publications builds credibility with property professionals.
- Showcasing customer success stories from marquee developers (Mirvac, Stockland, Frasers) provides social proof to a risk-averse enterprise buyer.
HOW TO REPLICATE WHAT WORKED
What worked: building a genuinely proprietary regulatory database (the most comprehensive planning-rule dataset in Australia) rather than a thin software layer over publicly available information, making the product hard for a well-funded competitor to replicate quickly.
The trap: expanding into new geographies (the US, UK) requires re-mapping an entirely new, unfamiliar regulatory system market by market - a founder copying 'digitize the regulatory data' internationally needs to budget for years of jurisdiction-specific data work per new country, not a simple software localization.
| PATTERNS OF THIS MODEL
PATTERNS IN DIGITISING JURISDICTION-BY-JURISDICTION RULES:
1. THE DATA-COMPILATION WORK, NOT THE SOFTWARE, IS THE EXPANSION BOTTLENECK. Each new market requires mapping and validating its own rule set before entry.
2. SERVE PRIVATE AND PUBLIC BUYERS FROM THE SAME DATA ASSET. Government adoption validates accuracy for commercial buyers, and vice versa.
3. AUTOMATE WHAT CURRENTLY REQUIRES EXPENSIVE EXPERT INTERPRETATION. Where consultants are the status quo, the ROI case writes itself.
4. FOUNDING TEAMS COMBINING DOMAIN QUALIFICATION AND TECHNICAL DEPTH ARE THE CREDIBILITY REQUIREMENT in regulated professional markets. Buyers check who built it before they check what it does.
What companies with this model reveal
| OPPORTUNITY INTELLIGENCE
GOLDMINE 1 — DIGITISE A JURISDICTION-BY-JURISDICTION REGULATORY DATASET.
Standard: zoning and planning rules currently require expensive expert interpretation. The compilation work — not the software — is the moat, and it is why expansion is slow and defensible.
GOLDMINE 2 — SERVE PRIVATE AND GOVERNMENT BUYERS FROM ONE DATA ASSET.
Standard: government adoption validates accuracy for developers and vice versa. Archistar signed planning-department contracts in Victoria, NSW and North American cities alongside its developer base.
GOLDMINE 3 — PAIR DOMAIN AND COMMERCIAL FOUNDERS.
Standard: an architect with a PhD in generative design plus a commercial co-founder is the combination a technology-plus-regulation thesis requires.
THE PIT — YOUR EXPANSION BOTTLENECK IS DATA COMPILATION, NOT SALES.
Every new country or state means mapping and validating its rules before a single deal. That makes international growth linear and capital-hungry in a way software growth is not — and the $11M 2022 round funding both a Snaploader acquisition and US entry is thin for that.
THE SECOND PIT — GOVERNMENT PERMIT-ASSESSMENT CONTRACTS HAVE MULTI-YEAR CYCLES AND POLITICAL RISK.
MOVE WITH CAUTION — DEVELOPER DEMAND TRACKS INTEREST RATES AND CONSTRUCTION STARTS.
Untapped Business Model / Gaps / Goldmines / Pits
Patterns & Insights
2
MARKET
mkt mt es
MARKET TYPE
Blue Ocean
WHY THEY WON
Before Archistar, no comparable platform combined zoning/planning data with AI-generated feasibility and design tools in one product - the company effectively created its category in Australia rather than displacing an incumbent. Its win came from being first to make regulatory complexity instantly queryable rather than out-competing an existing solution. Lesson: identifying a genuinely unautomated, expert-dependent bottleneck (interpreting zoning law) can create a new category rather than requiring you to win share in an existing one.
ENTRY STRATEGY
Greenfield Entry
EXECUTION
Archistar entered directly by building its own zoning and planning database from scratch in Australia, since no company had previously attempted to digitize this data at the scale and accuracy required for instant feasibility assessment - a slow, direct-build process rather than an acquisition or partnership-based entry.
FOOTHOLD STRATEGY
fs
Beachhead Strategy
Australian property developers and architects needing fast site-feasibility analysis were the initial beachhead, a market small enough for a young company to map comprehensively; from there Archistar expanded into government partnerships (automating planning approvals) and later into the US and UK markets once the core product and data model were proven domestically.
GROWTH CAMPAIGN
CAMPAIGNS THAT WORKED
- Partnership with the Victorian state government to demonstrate automated planning approval processes.
- Adoption by the NSW Government to run simulations on housing forecast models.
- Strategic partnerships with data providers like CoreLogic and Domain ensuring the platform is loaded with reliable, trusted data.
- First acquisition (Snaploader) to add interactive 3D real estate marketing capability ahead of US expansion.
KEY LEARNING
If your core asset is a proprietary, hard-to-compile dataset (like jurisdiction-specific regulations), government partnerships can serve as both a distribution channel and a credibility signal that private-sector customers respond to - being trusted by a government planning department is a stronger trust signal than any marketing claim.
gc
Market Context
| MARKET INTELLIGENCE
THE STANDARD: Identifying a genuinely unautomated, expert-dependent bottleneck creates a category rather than requiring you to win share in an existing one.
RULE 1 — WHERE INTERPRETATION IS THE BOTTLENECK, MAKING IT QUERYABLE IS THE PRODUCT. Planning rules were locked in documents and consultants' heads.
RULE 2 — REGULATORY DATA IS JURISDICTION-BOUND, SO EXPANSION IS A REBUILD PER MARKET. Each city is a data-acquisition project, not a rollout.
RULE 3 — SELL TO WHOEVER MAKES THE CAPITAL DECISION. Developers and lenders assessing site viability have budget; the professionals drawing plans do not.
RULE 4 — YOUR VOLUME MOVES WITH INTEREST RATES. Site-assessment demand disappears when development pauses, with no churn event.
MARKET TYPE: Blue Ocean (automated planning and feasibility).
| MARKET ENTRY PLAYBOOK
THE STANDARD: WHEN THE UNDERLYING DATA HAS NEVER BEEN DIGITISED, BUILDING THE DATASET IS THE COMPANY.
RULE 1 — THE UNGLAMOROUS DATA WORK IS THE BARRIER TO ENTRY.
Zoning and planning rules assembled jurisdiction by jurisdiction cannot be acquired, only accumulated.
RULE 2 — INSTANT FEASIBILITY CHANGES A WEEKS-LONG DECISION INTO A SAME-DAY ONE.
Sell to developers and lenders who lose deals to slow assessment, not to designers who enjoy the process.
RULE 3 — GEOGRAPHIC EXPANSION MEANS RE-BUILDING THE DATASET.
Each new region is a fresh capital project with no reuse of the previous one's content.
How to enter
| FOOTHOLD STRATEGY PLAYBOOK
THE STANDARD: A market small enough to map completely is a market you can own before anyone notices.
RULE 1 — CHOOSE A COUNTRY WHERE THE FULL DATA SET IS OBTAINABLE. Comprehensive planning, zoning and site data for one nation is a defensible asset; partial global coverage is not.
RULE 2 — SPEED OF FEASIBILITY CHANGES WHICH DEALS GET DONE. Developers pay for decisions made in hours because slow answers lose sites.
RULE 3 — GOVERNMENT PARTNERSHIPS CONVERT A COMMERCIAL TOOL INTO INFRASTRUCTURE. Automating planning assessment makes your rules engine the reference for both sides of the process.
RULE 4 — EXPORTING A DATA-DEPENDENT PRODUCT MEANS REBUILDING THE DATA. Each new country restarts the asset, not just the sales motion.
How to get the first strong position
MARKET PATTERNS & PLAYBOOK
3
MONEY
money rev pri
REVENUE MODEL
Subscription
PRICING MODEL
Tiered Pricing, Freemium, Trial Pricing
WHY THEY WON
Tiered subscription access to the zoning/planning database and AI design-generation tools, with government contracts structured as separate, larger engagements (e.g., automated permit assessment platforms) priced differently from the core property-professional subscription product.
A free public-facing tier lets homeowners and the general public explore basic site feasibility, while paid professional tiers for architects, developers, and agents unlock deeper design-generation and compliance-assessment tools - directly mirroring the different depth of need between casual and professional users.
TARGET AUDIENCE
CUSTOMER BUYING BEHAVIOUR
tg cb
Property developers and architects assessing site feasibility; real estate agents identifying underdeveloped sites for clients; government planning departments automating permit and compliance assessment; students and educators learning generative design.
Property professionals: self-serve or lightly sales-assisted, trial-first. Government contracts: long, RFP-driven procurement cycles typical of public-sector technology purchases, requiring extensive validation of data accuracy before adoption.
| PRICING INTELLIGENCE
What makes this model effective & make customers pay
When your output changes a capital allocation decision, price against the deal, not the analysis.
RULE 1 — AUTOMATED PLANNING AND FEASIBILITY ASSESSMENT COMPRESSES WEEKS OF CONSULTANT WORK INTO MINUTES.
The anchor is the planning consultant's fee and the land acquisition itself.
RULE 2 — SPEED CHANGES BEHAVIOUR, WHICH IS WHERE THE REAL VALUE SITS.
Developers evaluate far more sites when assessment is instant. More shots on goal beats cheaper shots.
RULE 3 — THE PROPRIETARY REGULATORY DATASET IS THE MOAT.
Zoning, overlays and planning rules compiled across jurisdictions are expensive and slow for anyone to replicate.
RULE 4 — DEVELOPERS PAY MORE THAN ARCHITECTS FOR IDENTICAL OUTPUT.
Sell to whoever carries the capital risk, not whoever produces the drawing.
A developer is buying the ability to reject a bad site in an afternoon. Decision-support tools price against the transaction they inform, which is why property software sustains fees that design software cannot.
PRICE & REVENUE
| Revenue Risk - The biggest threat to revenue stability
Selling both a professional subscription and large government engagements creates two revenue lines with opposite cycles and opposite sales motions.
Government contracts are lumpy, tendered and politically exposed; they flatter a year and do not recur automatically.
Property-professional subscriptions track development activity, which stops in a rate cycle.
Products built on public zoning and planning data depend on that data staying available and machine-readable — a dependency with no commercial remedy.
No revenue, ARR or contract values published.
Where the model can break
4
MOTION
LinkedIn: https://www.linkedin.com/company/archistar/, Twitter: https://twitter.com/Archistar, Facebook: https://www.facebook.com/archistar.ai/, Instagram: https://www.instagram.com/archistar.ai/
GROWTH EXPANSION MODEL
COMPETITIVE STRATEGY
motion ge cs
Geographic Expansion, Market Development (New Customer Segments)
HOW THEY EXPAND
Archistar expanded from private-sector property professionals in Australia into government planning-automation contracts domestically, then into international expansion (US, starting in Dallas, followed by additional states; also UK) funded by its 2022 capital raise, sequencing new-country entry only after mapping that country's specific zoning data.
Differentiation, First-Mover Advantage
HOW THEY COMPETE
As the first mover in AI-driven zoning and feasibility analysis in Australia, Archistar differentiates on the depth and accuracy of its proprietary planning-rule database, competing less against traditional design software (Autodesk, Graphisoft) and more by offering a category of capability those tools don't provide.
GROWTH ENGINE
GTM
ge n gtm
Freemium User Acquisition, Partnership Growth, Data Platform
A free public tier lets a broad audience (including future professional customers) experience the product's value with no commitment, while data partnerships with CoreLogic and Domain and government relationships extend distribution and credibility beyond what direct sales alone could achieve; this weakens in any market where Archistar hasn't yet compiled the local zoning dataset, since the core value proposition depends entirely on data completeness.
- Content marketing (articles, case studies, videos) demonstrating real use cases for developers and architects.
- Digital advertising targeting architecture and property sectors specifically.
- Participation in industry conferences and trade shows to build relationships with developers and government planning bodies.
SUSTAINING MOATS
Switching Costs, High Customer Lock-In, Brand Power, Technology Advantage (complex enterprise scenarios)
moat
The comprehensive, continuously updated zoning and planning-rule database, validated through direct government partnerships, is both technically difficult and time-consuming for a new entrant to replicate market by market, and government trust in the platform (evidenced by direct planning-department adoption) compounds as more jurisdictions rely on it for real regulatory decisions.
| MOAT INTELLIGENCE
THE STANDARD: Encoding planning regulation jurisdiction by jurisdiction produces a moat that is slow to build, impossible to shortcut and bounded by borders.
RULE 1 — REGULATORY DATA IS THE BARRIER AND THE PERMANENT COST. Zoning, overlays, height limits and setback rules differ by municipality and change constantly. Maintaining them is an operation that never finishes.
RULE 2 — DEVELOPERS BUY SPEED TO A BID DECISION, NOT ANALYSIS. Assessing many more sites in the same time changes deal flow, which is a revenue argument that survives any budget review.
RULE 3 — SELLING TO GOVERNMENT AND TO DEVELOPERS SIMULTANEOUSLY IS UNUSUAL AND STRONG, because planning authorities using the same rules engine legitimises the outputs for everyone else.
THE SIGNAL: each new country is a fresh regulatory build with no leverage from the last. That makes the moat genuinely defensible and the expansion plan expensive — capitalise for national wins rather than product-led growth.
Why this company remains defensible
ARR & TAKEAWAY
ARR Journey - what to do at each stage
PRE-$1M ARR — TURN PLANNING RULES INTO SOFTWARE
Zoning, overlays and development controls are public, complex and manually interpreted. Encoding them for a jurisdiction is slow, unglamorous work that competitors avoid.
Sell to developers and councils, both of whom lose money to slow assessment.
$1–5M ARR — SELL SITES ASSESSED PER WEEK
The buyer's metric is pipeline throughput, not drawings produced.
WATCH: feasibility studies run per seat.
$5–10M ARR — GOVERNMENT CONTRACTS VALIDATE AND CONSTRAIN
Council and agency deals give credibility and long cycles. Capitalise for the procurement timeline.
$10–50M ARR — EACH JURISDICTION IS A NEW PRODUCT
Rules do not transfer across cities or countries. Expansion cost is engineering, not sales.
NOTE: revenue not disclosed; reported funding varies by source.
$50–100M ARR — DEVELOPMENT ACTIVITY IS RATE-DEPENDENT
Revenue falls with interest rates and construction starts, with no churn event. Underwrite to the trough.
$100M+ ARR — NOT IN EVIDENCE
Rule: encoding a jurisdiction's rules is a moat per jurisdiction and a tax on every new market. Model expansion as R&D, not as sales territory.
COPY PLAYBOOK : What Worked → What Failed → What to Replicate → What to Avoid
THE STANDARD: Building a genuinely proprietary regulatory dataset beats a thin software layer over public information. It also means every new geography is a multi-year data project.
SEQUENCE:
1. Assemble the regulatory data nobody has structured, in one jurisdiction, properly.
2. Sell decisions that data enables — feasibility, site selection — not the data itself.
3. Budget years, not months, per new country.
WORKED: The most comprehensive national planning-rule dataset in its home market, hard for a funded competitor to replicate quickly.
CAUTION:
1. INTERNATIONAL EXPANSION MEANS RE-MAPPING AN UNFAMILIAR REGULATORY SYSTEM MARKET BY MARKET. Budget jurisdiction-specific data work per country, not a software localisation.
2. PROPERTY-DEVELOPMENT DEMAND IS RATE-DRIVEN AND CYCLICAL, regardless of data quality.
bottom of page