Applied AI research in the built environment.

Landform Labs shares its work in the open, so firms across architecture, engineering and construction can follow how AI is developing and engage with it directly.

01Research

The research focuses on practical decisions in architecture, engineering and construction.

A hut with a raised storm shutter on the ridge nose above Rosalie Bay, Aotea Great Barrier Island.
Method studyAotea Great Barrier Island

An Aotea land listing became a reviewed early design study.

A hut on 24 hectares of bush, developed from a land listing into an early design study. Open site data helped compare possible locations, two concepts were built on the real terrain, and each stage was reviewed and refined by a person. The result includes a drawing package, a cost range and a clear record of five checks still open.

LiDAR siting Drawings as code Five checks open
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Publishing studyField Notes · Vol. 01

The Aotea study was also prepared as a 28-page book.

The same Aotea work was arranged as a 28-page volume, a short walkthrough and a detailed page. The note considers how each format changes the way someone encounters and responds to the design.

28 pages Turned by hand One shared model
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The first day of the work is also available as a field note: see how the study developed over one working day →

02Case studies

Products and prototypes tested in real workflows.

Vistafy project dashboard showing multiple architecture rendering projects in a dark workspace interface.
Case studyArchitecture visualization

Vistafy: rendering and motion inside the practice.

Architects turn sketches, references, and finished renders into client-ready visuals and walkthroughs without leaving the project workspace. The lesson is that AI is more useful when it holds context across iteration, review, and presentation.

Sketch to render Element-level edits Motion from renders
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BookDone cover image showing mobile app screens and voice-first portfolio positioning.
Personal buildField evidence

BookDone: voice and photos into apprentice bookwork.

Site work gets captured in the way tradespeople already work: photos, voice notes, and short follow-up questions. The system turns that field evidence into formatted BCITO submissions that still need review before they count.

Voice capture Unit standard mapping Reviewable output
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Buildbit source-backed answer interface with cited NZS 3604 references.
Retired pathStandards retrieval

Buildbit: NZS 3604 answers with the clause attached.

NZS 3604 became askable in plain language, under a formal Standards NZ license, with answers tied back to clauses. The useful lesson was also the boundary: in compliance work, almost-right is still a liability.

Licensed source Citation first Refuse over guess
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NZ site intelligence interface showing parcel details, buildability score, constraints, and terrain slope overlay.
Site-data workSite feasibility

NZ site intelligence: a source-backed layer for early feasibility.

Parcel, terrain, constraints, and missing-data states get pulled into an early feasibility layer. The aim is a practical source pack that helps a team decide what needs review before concept work goes too far.

Parcel context Terrain checks Missing-data status
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> set objective
> agent planning...
> agent 47 / 100
> APPROVE → continue
> next task
OrchestrationEarly

Command Center: agent work behind approval gates.

A web dashboard for running AI agents like a team. Set an objective, the agents plan and execute, and every plan and result waits on human approval before it counts.

Team orchestration Approval gates Multi-model
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Parallel desktop app showing local coding agents running with a pending approval.
Local agentsEarly

Parallel: reviewer corrections carry into later agent runs.

A native desktop app that drives your local coding agents, Claude Code, Codex, and Cursor, from one place. Actions wait on approval before they run, and every correction is carried into the next run.

Local coding agents Approval gates Feedback as memory
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