A digital professional backoffice around studio work.
We help architecture studios connect public data sources, their own project experience and concrete workflows in a source-aware preparation layer. Not as a replacement for architects, but as a better basis for assessment, proposals, project start-up and responsible decisions.
AI is not the product. The workflow is.
Many studios have already seen too many generic AI demos. Our starting point is more practical: which parts of your everyday work are repeated, source-heavy or difficult to reuse across projects?
Here, architecture does not mean software architecture. We mean the built environment: architecture studios, renovation, local plans, BR18, materials, building data and architectural decisions.
FT's model combines architectural judgement, Danish building data, the studio's own knowledge and small controllable agent workflows. The agent retrieves, structures and flags uncertainty. The architect assesses, prioritises and takes responsibility.
First discovery
Start by finding the right problem. It may be proposal work, project start-up, searching previous cases, local plan analysis or a data package before a building permit process.
See agent discoveryA good pilot begins with the studio's own friction.
If an AI pilot cannot be controlled by the studio, it is too broad. FT would rather start with one workflow that can be used in practice than with a large technology vision without clear sources, access boundaries and responsibility.
- 01
Start with a workflow where the studio already feels the friction: project start-up, proposals, local plans, CVs, cases or internal knowledge search.
- 02
Define the source basis before testing: public registers, project archive, proposal material, CVs, cases, BR18, local plans or the studio's own project notes.
- 03
Decide in advance who validates the output, what may only be used internally and which conclusions the agent must not present as certain.
- 04
Do not only measure time. Measure better reuse of experience, fewer overlooked requirements, clearer uncertainty and whether the team actually uses the output.
Discovery, digital backoffice and responsible practice
AI for architecture studios
AI for architecture studios should work as a source-aware preparation layer around data, project knowledge and architectural validation.
Agent discovery for architecture studios
Agent discovery maps friction, data, responsibility and the first realistic AI pilot before a studio builds agent workflows.
The studio digital backoffice
A studio digital backoffice connects public data, internal experience and controlled agent workflows around architectural responsibility.
AI workshop for architecture studios
An AI workshop for architecture studios turns generic AI curiosity into concrete workflows, rules and first pilot choices.
AI in architecture firms
AI in architecture firms becomes useful when it is tied to workflows, sources, governance and explicit professional validation.
The studio knowledge foundation
A studio knowledge foundation makes cases, sources, standards and decisions easier to reuse without losing responsibility.
AI strategy for architecture studios
An AI strategy for architecture studios should prioritise workflows, data boundaries, pilots and architectural responsibility.
AI policy for architecture studios
An AI policy for architecture studios defines allowed use, data boundaries, source requirements and human validation.
Project data foundation
Connect drawings, spreadsheets, product choices and revisions so the studio can review agent drafts from one shared basis.
Drawing, spreadsheet and PDF workflow
Review where drawings, spreadsheets and PDFs create uncertainty, and what an agent must document before the preparation can be used.
Digital backoffice for product data
Structure product data so an agent can prepare a checkable basis for price appendices, manuals and handovers.
Workflows that can be tested on real cases
Proposal agent for architecture studios
A proposal agent for architecture studios can gather relevant cases, CV text, source notes, project questions and first draft structure. It should not promise scope, price or professional conclusions; it prepares material so the studio can write a sharper proposal.
Read page arrow_forward AI local plan analysisAI agent for local plan analysis
An AI agent for local plan analysis can extract relevant provisions, map them to the project intent and flag uncertainty before the studio uses time on design decisions. It is preparation for architectural and authority dialogue, not a binding interpretation.
Read page arrow_forward AI BR18 overviewAI agent for BR18 overview
An AI agent for BR18 overview can identify likely regulation chapters, documentation needs and specialist questions based on building type, use and intervention. It should produce a checklist with sources, not a final compliance answer.
Read page arrow_forward AI building permit preparationAI for building permit preparation
AI for building permit preparation can gather address, BBR, planning, BR18 and project information in a structured overview so the studio can see missing data, risks and next documentation needs before dialogue with municipality or advisers.
Read page arrow_forward AI building profileAI agent for building profile
An AI agent for building profile gathers public information about building, age, use, areas, materials, energy certificate and preservation status in a source-aware profile. It gives the studio a faster first view, but not a technical condition report.
Read page arrow_forward AI renovation screeningAI agent for renovation screening
An AI agent for renovation screening can point to relevant tracks: planning conditions, age, materials, energy certificate, preservation concerns, everyday problems and possible adviser needs. It should not promise solutions, but help the studio ask better first questions.
Read page arrow_forward AI site screeningAI agent for site screening and building potential
An AI agent for site screening can gather cadastre, planning conditions, existing buildings, indicative areas and relevant building-potential tracks. It should especially flag what cannot be concluded without local plan interpretation, survey or municipal dialogue.
Read page arrow_forward AI LCA preparationAI agent for LCA preparation
An AI agent for LCA preparation can gather early material, quantity, building-part and documentation tracks so the studio sees data gaps before formal calculations. It can support decisions, but it must not replace verified LCA calculation or specialist responsibility.
Read page arrow_forward CAD to price appendixFrom CAD to price appendix, ordering and manual
When drawing codes also drive price appendices, ordering and manuals, small ambiguities quickly become manual follow-up work. The first step is to make the connection between code, product, variant, price and documentation visible enough for the project team to review.
Read page arrow_forward project change automationTraceable project changes
A small change in product, location, quantity or price can affect a drawing, price appendix, order list, client PDF and manual at the same time. The value is a traceable overview of what changed, which documents are affected and where a professional must decide.
Read page arrow_forwardStart with discovery before you build anything.
It is not a good idea to move professional responsibility into a model. It is a good idea to test where a source-aware preparation layer can strengthen the studio's own workflows.