Applied AI research in the built environment.

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

01Case studies

What I have built and what it proved.

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: land context before design hardens.

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: corrections become memory.

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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02Prototype lab

Where research turns into tools you can open.

03Research

Each piece of research hangs on one clear question.