Instead of architects spending days manually writing specs by cross-referencing hundreds of pages of building codes and past projects, Avoice's AI reads the drawings and writes the specs for them.

Avoice's Studio Workflow module ingests architectural drawings (plans, sections, details), building codes, and historical project documentation to automatically generate construction specifications, door/window/finish schedules, and compliance checklists. The system uses multimodal document understanding models to parse CAD/BIM outputs and PDFs, extracting entities such as materials, assemblies, room types, and code requirements. It then applies retrieval-augmented generation (RAG) over the firm's Studio Library—a continuously updated knowledge graph of past projects, preferred products, and office standards—to produce specifications that match the firm's voice and standards. The output is cross-validated against relevant building codes (IBC, ADA, local amendments) using rule-based and ML-driven compliance checks, flagging conflicts before they reach the contractor. This transforms a process that typically consumes 30–40% of an architect's project time into a review-and-approve workflow, enabling small firms to produce enterprise-quality documentation at a fraction of the cost and time.

It's like having a junior architect with photographic memory of every project your firm has ever done, every building code ever written, and the ability to type 10,000 words per minute—except it never calls in sick.

Avoice's AI assistant acts like a brilliant project architect who has memorized every drawing, spec, and email on your project and can instantly answer any question or catch any conflict.

The Studio Assistant is an agentic AI system that maintains a live, structured understanding of an entire architectural project—drawings, specifications, schedules, consultants' documents, meeting notes, and email threads. When an architect asks a natural language question (e.g., "Does the fire rating of the corridor walls on Level 3 comply with the latest code amendment?" or "Which consultant's drawings conflict with our reflected ceiling plan?"), the assistant retrieves relevant context from across all project documents using vector similarity search and structured metadata queries, reasons over the information using chain-of-thought prompting, and delivers a cited, actionable answer. Beyond Q&A, the assistant proactively runs coordination checks—comparing architectural drawings against structural and MEP consultants' models to flag spatial conflicts, specification mismatches, and code violations before they become costly RFIs during construction. The CTO's background at Amazon Alexa suggests the system may also support voice-driven interaction, allowing architects to query their projects hands-free while reviewing drawings on screen or on-site.

It's like Jarvis from Iron Man, but instead of helping you build a suit, it helps you make sure the fire-rated wall doesn't have an unrated door in it before the contractor sends you an angry email.

Avoice turns every past project your firm has ever done into a searchable, intelligent library so architects can instantly find how they solved a similar problem three years ago instead of reinventing the wheel.

Studio Library is a continuously learning knowledge management system that ingests, parses, and semantically indexes every document a firm produces—drawings, specifications, submittals, RFIs, meeting minutes, product data sheets, and internal standards. Using embedding models, the system converts unstructured architectural content into high-dimensional vector representations stored in a vector database, enabling semantic search that understands architectural intent rather than just keyword matching. For example, an architect searching for "high-performance curtain wall details for cold climates" will surface relevant details from past projects even if those documents never used that exact phrase. Beyond search, the system uses collaborative filtering and content-based recommendation to proactively suggest relevant precedents, preferred products, and reusable details when an architect begins a new project of a similar type. Over time, each firm's Studio Library becomes a unique, proprietary knowledge asset—a compounding data moat that makes the platform more valuable with every project completed. This addresses one of the most painful problems in architecture: institutional knowledge walking out the door when experienced staff leave.

It's like if your firm's most senior partner—the one who remembers every project detail from 1997—was cloned, made immortal, and given a search bar.