Finch, formerly commonly referred to as Finch3D, is an AI-native building-design platform for architects and AEC teams. It sits between early massing and detailed BIM production, helping teams explore building forms, generate unit mixes and floor plans, check project metrics, and move approved geometry back into tools such as Revit and Rhino. Rather than generating attractive architectural images, Finch focuses on the geometry, data, rules, and repeated decisions behind a buildable design.

A typical Finch workflow begins with a building mass created in an existing design tool.

Rhino users can send a building mass and context through the Finch plug-in, while Revit users can upload conceptual mass families. Autodesk Forma also has a Finch extension for sending building volumes and surrounding context directly into the platform. Once imported, Finch separates the project into stories and gives designers tools for assigning programs, editing floor plates, and developing layouts.

This is one of Finch's better qualities. It does not ask architecture firms to throw away the software they already use.

Finch is not built around generic AI producing a different answer every time. Its Adaptive Plan Library lets firms save their own layouts and reuse them across future projects.

Plans can contain constraints that define which walls should stay fixed, which dimensions can expand, and which minimum conditions need to remain intact. Finch says a saved plan can be adapted across many new unit shapes while preserving these rules.

That approach is more relevant to architecture firms than unrestricted generation. A practice may already have apartment layouts that reflect accessibility standards, local regulations, preferred room proportions, and lessons from completed projects. Finch turns that institutional knowledge into reusable design logic instead of forcing the team to start from zero.

Finch can generate residential unit distributions around corridors and circulation cores. Designers can control variables such as stairwell count, units per stairwell, corridor width, wall width, and core dimensions before generating alternatives.

The platform then supports more detailed unit planning. Firms can set graph rules such as minimum bedroom and bathroom dimensions, filter plans by characteristics such as bedroom count or region, and reuse matching layouts from their libraries.

Full AI-generated unit plans have an access limitation worth noting: Finch's current documentation places this capability within its higher-level organizational workflow rather than making it universally available. Users without that access can still search and adapt plans from their own libraries.

Finch's newer product direction includes Archie, an AI agent intended to handle repetitive precision work.

The company describes Archie as assisting with tasks such as exact door placement, compliance checks, and maintaining consistent changes across linked units.

This is a more practical use of AI than asking a chatbot to design an entire building from a sentence. Architectural projects contain hundreds of small coordinated decisions. Automating some of those repetitive updates can leave designers with more time for circulation, spatial quality, façade logic, and other decisions that benefit from human judgment.

Finch is also built around rapid comparison.

The platform updates design data while architects change a proposal, which means alternatives can be judged on more than appearance. Its current website highlights real-time analysis of density, area, feasibility, and design trade-offs across residential, high-rise, office, and master-planning work.

For early-stage projects, this may be more valuable than producing one supposedly “optimal” answer. Architects can explore many variations and understand what changes when floor plates, programs, unit distributions, or building forms move.

Finch's usefulness depends heavily on getting data back out.

Finished variants can be exported as BIM into Revit with object families. Rhino users can import both 2D plans and 3D geometry, while Grasshopper can stream Finch project data for additional parametric work. PNG and CSV exports are also available for visual and numerical project information.

Current documentation also covers workflows with Autodesk Forma and Archicad, although support depth differs between applications.

Finch is strongest for residential feasibility studies, apartment planning, master planning, high-rise layout development, office test fits, and firms that repeat similar building typologies.

It becomes especially useful when a practice already has a substantial library of successful plans and internal standards. Those teams can encode more of their existing knowledge into Finch instead of treating AI as an external designer.

Finch is not a general architectural design replacement. Detailed façade design, structural engineering, bespoke geometry, visualization, documentation, and many later-stage decisions still belong in specialized AEC tools.

Its strongest automation also depends on preparation. Plan libraries, graph rules, program definitions, and constraints need to reflect the firm's actual design standards. Poorly defined rules will not become good architecture just because an AI system applies them faster.

Software compatibility should also be checked against the exact version and workflow a firm uses, since Finch's integrations and feature availability are not identical across Rhino, Revit, Grasshopper, Forma, and Archicad.