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Enhancing Efficiency with Artificial Intelligence
Breaking the Workflow Barrier with APS-Connected
Data
Christopher Stock
Head of Digital Delivery, Dewan Architects & Engineers
Gareth Ponmoon
Operations Manager, Dewan Architects & Engineers
Learning Objectives
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Transforming AEC Problems into Opportunities through Strategic Research &
Development (R&D)
Leverage AI-augmented decision-making to drive smarter coordination & unlock
actionable, query-based insights from BIM data
Discover how APS unlocks transformative workflow potential through real-world AI
integration in an AEC practice
Learn how scalable AI in BIM boosts efficiency by cutting manual tasks and
enabling smarter, more streamlined workflows
Understand how to empower teams with custom AI tools to adopt, implement, and
evolve with intelligent digital workflows
Description
This session demonstrates how AI-augmented systems streamline coordination, automate clash
detection, and deliver natural language–driven responses using Autodesk Platform Services
(APS) connected data. Explore future-forward workflows that leverage AI integration with
Autodesk tools like Revit, Navisworks, and Autodesk Construction Cloud (ACC). Discover how
machine learning, deep learning, Optical Character Recognition (OCR), and Natural Language
Processing (NLP) amplify project insights, enable query-based model exploration, and drive
intelligent automation. Learn how Dewan achieved up to 70% efficiency gains, cut coordination
prep time by 40%, and achieved processes up to 6,000x faster, all while enabling their
workforce to confidently adopt and evolve with AI-driven custom tools. Walk away with
actionable strategies to embed scalable AI, empower teams, and accelerate project delivery
across every stage of the lifecycle.
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Speaker(s)
Christopher Stock is a seasoned professional with over a decade of
experience in BIM, currently a key figure at Dewan Architects and
Engineers in Dubai. His extensive, multidisciplinary background has
enabled him to develop an analytical yet innovative approach to design.
Christopher has successfully delivered multiple BIM construction
projects, showcasing his expertise in advancing digital delivery. At
Dewan Architects and Engineers, Christopher drives innovation and
excellence in every project. His commitment to high standards and
fostering a collaborative environment has earned him recognition as a
leader in the field. His work significantly enhances the efficiency and
accuracy of construction processes, contributing to the architectural and
engineering industry.
Gareth Ponmoon is a leading expert in BIM processes and workflows
within the architectural design and construction industry. With years of
hands-on experience, Gareth plays a pivotal role in shaping and
managing BIM strategies at Dewan. He is responsible for developing and
maintaining BIM standards, while guiding project teams and collaborating
closely with architects, engineers, and stakeholders to ensure the
accuracy and reliability of BIM models. His expertise drives efficiency and
innovation, ensuring that all projects meet their design and execution
goals.
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AI-Augmented Building Information Modelling
The integration of Artificial Intelligence (AI) with Building Information Modelling (BIM) has
transformed the way designers interact with project data. By enabling real-time engagement
between users and their models, AI enhances decision-making and unlocks new levels of
efficiency.
Quible: Dewan’s In-House AI Platform
Quible is Dewan’s custom-built AI platform designed to connect people, data, and workflows in
innovative ways. Built on a continuously evolving knowledge base, Quible empowers teams to
make project-aware, data-driven decisions. Its unified AI architecture combines multiple
foundation models to interpret not just documents but also 3D models, drawings, and project
data.
How It Works
AI Agents: At its core, Quible operates using autonomous AI agents, each designed to
perform a specific task with precision, such as code generation, compliance validation,
and model health checks.
Multi-Agent Framework: These agents work together within an integrated platform,
enabling seamless collaboration and distributing responsibilities across specialized
tasks.
Autonomous Task Execution: Quible carries out end-to-end workflows with minimal
human input, delivering insights and automations in real time.
By transforming static BIM data into a dynamic, interactive dataset, Quible allows teams to
converse with their project information, unlocking smarter, faster, and more connected decisionmaking.
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Autodesk Platform Services (APS)
Autodesk Platform Services (APS) is a powerful suite of cloud-based APIs that enable seamless
integration between Autodesk tools and other platforms. It connects design, construction, and
project data across workflows, unlocking new opportunities for automation and collaboration.
Key Capabilities
Data Access & Management – Retrieve, manage, and update project information, including
designs, documents, and models, directly from Autodesk Construction Cloud (ACC).
Automation – Streamline repetitive processes such as model health checks, data extraction,
and file conversions. APS empowers teams to deploy cloud-based automation scripts to
achieve significant time savings.
Interoperability – Connect Autodesk data with external platforms like ERP, CRM, GIS, or AIpowered systems, creating a unified project ecosystem.
Visualization – Embed interactive 2D and 3D model viewers directly into custom-built
applications for better design and coordination insights.
Impact on Project Delivery
By enabling real-time access to project data, APS enhances collaboration, improves
decision-making, and reduces risk. Teams can integrate automation directly into their
workflows, driving measurable efficiency gains and unlocking new levels of productivity.
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APIs Driving AI-Enabled Workflows
Autodesk Platform Services (APS) provides a powerful suite of APIs that enable Quible to
connect design tools, extract live project information, and integrate data directly into AI-driven
workflows. These APIs form the backbone of real-time, intelligent task execution.
Core APIs Powering AI-Augmented BIM
Model Derivative API – Extracts and visualizes 3D geometry and metadata for dynamic model
interactions.
Data Management API – Manages and organizes cloud-based design assets, enabling
structured data flows.
Authentication API – Ensures secure access to project data and connected systems.
AEC Data Model API – Structures BIM data for intelligent queries, reporting, and takeoffs.
ACC APIs – Links model versions, issues, and live project data for real-time AI insights.
Revit API – Enables programmatic access to the Revit design environment, supporting
creation, modification, and data extraction through custom add-ins and automations.
Navisworks API – Powers customization and automation in Navisworks, including clash
detection, data extraction, model navigation, and integration with external tools.
Connecting Data to AI
Through these APIs, Quible enables real-time interaction between designers and data.
Revit and Navisworks models hosted on Autodesk Construction Cloud (ACC) can be
launched directly from model threads, think of it as “ChatGPT for BIM models”.
Designers can select any model element, query its data, and receive instant, intelligent
insights, fundamentally changing the way teams engage with project information.
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Function Calling
Function Calling enables AI to move beyond simply providing information by allowing it to
directly interact with APIs and perform actions within a project environment. By mapping
natural language prompts to predefined API functions, AI can securely trigger operations such
as:
• Retrieving model data
• Generating automated reports
• Updating project records
• Executing complex workflows
How It Works
Through Function Calling:
1. The AI interprets the natural language request.
2. It selects the appropriate API function to handle the operation.
3. The function executes directly within the APS API environment.
4. Results are returned to the AI and displayed to the user.
Impact on Project Delivery
This transforms AI from a passive assistant into an active project participant,
bridging the gap between conversation and execution. The result is improved
efficiency, precision, and security while reducing the need for manual interventions.
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BIM to AI Workflow
This workflow illustrates how Quible connects Autodesk Platform Services (APS) with AI
models to deliver fast, accurate, and context-aware insights from project data. It seamlessly
integrates user queries, foundation models, Autodesk APIs, and retrieval-augmented generation
(RAG) to enable intelligent responses.
How It Works
1. User Query – The process begins when a designer submits a natural-language request.
2. Orchestrator – The orchestrator determines the most efficient way to handle the query.
3. Foundation Model – The system calls a foundation model to interpret the request and
understand intent.
4. API Endpoints – Autodesk APIs are triggered via FastAPI to fetch, manipulate, or
update relevant model and project data.
5. Retrieval-Augmented Generation (RAG) – When needed, the AI retrieves information
from an internal knowledge base to provide project-specific answers.
6. Response – Results from all sources are combined into a clear, context-driven
response delivered back to the user.
Impact on Project Delivery
This integrated pipeline transforms a simple user question into an intelligent,
connected workflow. By securely managing requests through FastAPI and combining
data from models, documents, and knowledge bases, Quible delivers answers that are
both accurate and actionable in real time.
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Retrieval-Augmented Generation (RAG)
In the early stages of AI integration, responses were sometimes convincing but not always
correct, a common challenge in custom AI implementations. The issue wasn’t that the AI was
“lying,” but rather that it lacked access to sufficient verified knowledge sources.
How RAG Solves This
RAG enhances AI accuracy by combining real-time document retrieval with language
generation. Instead of relying solely on pre-trained data, the AI dynamically queries trusted
knowledge bases, such as building codes, specifications, and project repositories — before
producing an answer.
Why It Matters
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Accuracy & Trust – In the AEC industry, precision is critical. RAG ensures
responses are grounded in cited, project-specific sources.
Compliance – AI references verified documents, making results reliable for
audits and approvals.
Transparency – Every response can be traced back to its original data, ensuring
confidence in decision-making.
How It Works
1. User Query – A designer asks a question, e.g., “How does the SBC define gross floor
area?”
2. Context Retrieval – The system searches connected knowledge bases for the most
relevant data.
3. Verified Sources – Relevant documents are retrieved in real time.
4. Cited Response – The AI uses both the user’s prompt and retrieved context to generate
an accurate, verifiable answer.
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Impact on Project Delivery
By grounding AI insights in real, project-specific knowledge, RAG transforms Quible
into a powerful research and decision-support tool. Teams gain faster, reliable
access to up-to-date information, ensuring accuracy, compliance, and trust across
every stage of project delivery.
Historic Data Querying
DeepQuery is a custom specialist AI agent, designed to address two critical challenges in the
construction industry:
1. Litigation — Providing rapid, accurate answers based on historic project records.
2. Staff Turnover — Preserving institutional knowledge when senior team members leave.
Trained on five years of company emails and documents, DeepQuery leverages vectorized
data storage and Retrieval-Augmented Generation (RAG) to deliver instant, context-aware
insights with full citations.
How It Works
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User Query – A question is submitted to the system.
Agent Decisioning – The DeepQuery agent determines the best way to retrieve the
relevant data.
Vector Store Search – RAG is used to scan a highly organized, vectorized knowledge
base containing historic documents, specifications, and communications.
Foundation Model Processing – The AI structures the results and generates a clear,
project-specific response.
Cited Answer – Responses include references, chain histories, and source context for
full transparency.
Real-World Impact
In a test case, answering a series of queries manually took 29 hours. DeepQuery
completed the same task in 16 seconds — making it 6,000x faster.
When a senior team member leaves, their project knowledge can be vectorized and
queried directly, effectively keeping their expertise alive.
Strict access controls are in place to ensure data security and privacy compliance.
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Workflow
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User Query → Submit a question.
Agent → Acts as an intelligent intermediary.
RAG + Vector Store → Retrieves verified, project-specific data.
Foundation Model → Processes and structures the information.
Response → Returns an accurate, cited answer in seconds.
Optical Character Recognition (OCR) & Machine Learning
Document control has long been one of the biggest workflow bottlenecks in construction
projects. To solve this, Dewan developed ConformML, an AI-powered agent combining Optical
Character Recognition (OCR), Machine Learning (ML), and Natural Language Processing
(NLP) to automate document validation at scale.
ConformML processes submission documents, compares them against drawing lists, and
highlights discrepancies in seconds, eliminating repetitive manual checks. It acts like a
document controller that never sleeps, delivering speed, accuracy, and consistency.
How OCR Powers Automation
OCR enables AI to read and interpret text from scanned documents, drawings, images, and
handwritten notes, converting them into machine-readable formats. This transforms static files
into searchable, structured data, making it possible to integrate legacy records directly into
modern digital workflows.
How ConformML Works
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User Task or Upload – A user uploads submission documents or prompts the AI
agent.
Agent Processing – ConformML routes documents intelligently and triggers
OCR + ML pipelines.
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Dual OCR Parsing – Two independent OCR engines process the documents:
Primary OCR extracts structured data.
Post-Parser OCR validates and cross-checks extracted fields.
Comparison Logic – The outputs are compared against drawing lists and
submission requirements.
RAG-Enhanced Validation – Where needed, the agent retrieves related project
data from knowledge bases for improved accuracy.
Foundation Model Integration – The system ties everything together,
contextualizing discrepancies and preparing a clear response.
Human-in-the-Loop Review – A controlled approval step ensures results meet
QA/QC requirements.
Impact on Project Delivery
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Speed: Manual document review previously took 13 hours — ConformML completes
the same task in 10 minutes, making it 77x faster.
Consistency: Automates QA/QC at scale, eliminating human error.
Efficiency: Generates structured Excel reports instantly, reducing repetitive
administrative work.
Scalability: Replaces time-intensive manual processes with continuous, automated
document control.
Clash Approval Agent
During Dewan’s needs analysis across Digital Delivery teams, they surveyed 20+ BIM
Managers and Coordinators and discovered a significant opportunity for automation:
• On average, 40% of their time was spent reviewing and approving clashes.
• A benchmark test showed that manually reviewing 500 clashes takes 6 hours.
• With AI, the same review is completed in 40 seconds — making it 600x faster.
How It Works
1. Clash Data + User Query – The process begins with clash reports and relevant user
prompts.
2. Agent Processing – The agent collects and interprets the input data, orchestrating the
workflow.
3. Programmatic Rules – Simple yes/no checks are applied using predefined Clash
Approval Rules.
4. RAG Integration – For more complex cases, Retrieval-Augmented Generation searches
project-specific knowledge bases for contextual information.
5. Foundation Model – Handles reasoning and evaluates clash context to ensure
consistency across projects.
6. Computer Vision + RAG – For visual scenarios, 3D models are analyzed alongside
textual rules to improve accuracy.
7. Approve / Review / Reject – The AI proposes an action based on rule-driven logic and
contextual analysis.
8. Human-in-the-Loop Oversight – Complex or high-risk clashes are escalated for
manual review, ensuring quality control.
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Impact on Project Delivery
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Speed: Reduces clash approval workflows from hours to seconds.
Consistency: Ensures uniform application of rules across all projects.
Scalability: Frees up BIM Managers and Coordinators to focus on high-value design
decisions.
Hybrid Efficiency: Combines AI-driven automation with human oversight for optimal
accuracy.
Task Execution
Agentic Task Execution takes AI beyond providing insights — it allows AI agents to act
autonomously within design tools like Revit. Unlike traditional scripts, add-ins, or Dynamo
routines, these agents interpret natural language prompts, translate them into executable
actions, and complete complex modeling tasks end-to-end with minimal human intervention.
This shift moves us from automation to delegation — where designers describe outcomes in
plain language, and AI makes it happen.
Why It’s a Game-Changer
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Tasks that once took hours of manual modeling can now be completed in minutes.
AI understands context, follows project-specific rules, and executes tasks directly in live
models.
The agent provides a step-by-step explanation of actions, improving transparency and
trust.
Model Context Protocol (MCP) Workflow
The Model Context Protocol (MCP) powers task execution by bridging AI reasoning
and Revit’s API. The workflow is as follows:
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1. User Prompt – The designer gives a plain-language instruction, e.g., “Place round
columns at every grid intersection.”
2. Orchestrator – Routes the prompt to the foundation model for intent understanding.
3. Foundation Model – Interprets the request and determines the optimal execution plan.
4. MCP Server – Translates the AI’s intent into structured tool calls for Revit’s API.
5. Code Agent – Writes C# or Python code in real time to execute the task.
6. Retry & Debugging – If the first attempt fails, the agent diagnoses the issue, rewrites
the code, and retries automatically until successful.
7. Persistent Memory – Successful interactions are stored, allowing the AI to reuse code
instantly for future tasks — effectively creating custom Revit capabilities on demand.
Impact on Project Delivery
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Accelerated Modeling – Reduces manual effort and speeds up repetitive workflows.
Intelligent Debugging – Agents reason through errors and self-correct without human
intervention.
Adaptive Learning – Stored memory enables the agent to improve over time.
Seamless Integration – AI agents operate natively within existing tools like Revit,
meaning zero disruptions to design processes.
Make Your Own AI Tools
The real power of AI lies in accessibility. At Dewan, they focused on putting Large Language
Models (LLMs) directly into the hands of our teams, inside the tools they already use. By
integrating AI seamlessly into everyday workflows, they made adoption intuitive and effective.
With the right approach, any organization can transform manual processes into AI-driven
solutions that scale with the business.
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Approach at Dewan
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Accessible AI for Teams – Dewan a created split-pane AI interfaces that allow users
to compare Quible’s responses with other LLMs like Grok 4, others enabling informed
decisions and boosting adoption.
Rapid Prototyping with Cursor – Using Cursor, an AI-powered Integrated
Development Environment (IDE), quickly prototype proof-of-concept applications
before handing them to the development team for full-scale implementation.
Leveraging Cloud Platforms – build on managed services from Amazon, Google, and
Microsoft, which provide out-of-the-box infrastructure and scalability for AI-driven
workflows.
The Role of Data Sources
AI is only as effective as the data it can access. Connect internal and external
systems — including email, SharePoint, calendars, Revit, and more — to give Quible
context-rich insights. This turns generic prompts into business-specific intelligence
that drives real value.
Security & Privacy First
From the start, security and data privacy have been integral to our AI strategy:
• Strict Access Controls – Role-based permissions protect sensitive data.
• Code & System Hardening – All deployments undergo rigorous code
reviews, system audits, and security testing.
• Independent Security Validation – External validation ensures compliance
with global standards.
• Data Protection – None of the information sent to Quible is used to train
external models, and all sensitive data is encrypted end-to-end.
While tools like Cursor enable “vibe coding,” professional developers and IT
teams remain deeply involved to ensure that every deployment is secure,
compliant, and production-ready.
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Anyone can build AI tools — but secure, scalable implementation requires a thoughtful
strategy, access to the right infrastructure, and integration with your organization’s knowledge
base.
AI + Human Collaboration
AI isn’t here to replace us — it’s here to work with us. By automating repetitive, data-heavy
tasks, AI frees teams to focus on creativity, strategy, and solving complex challenges.
The shift isn’t about whether AI will change work — it already is — but about how people
choose to use it: to displace people or to empower them. By embracing reskilling,
upskilling, and human-AI collaboration, unlock innovation, expanded potential, and new
opportunities people can’t yet imagine.
Time-Saving Speed Factor Analysis
At Dewan, they have benchmark every implementation to quantify the impact of AI. By
strategically layering AI agents across workflows, Dewan has not only accelerated delivery but
also transformed how teams work.
AI unlocks time savings, frees up capacity, and empowers teams to focus on high-value
design, strategy, and collaboration.
Key Insights
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Efficiency Gains: Reductions in manual effort of up to 6,400x in some workflows.
Capacity Unlocking: AI gives teams back time to focus on design quality and
strategy.
Scalable Impact: Improvements compound across disciplines, creating firm-wide
efficiency gains.
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The Bigger Picture
This analysis highlights an important shift: AI at Dewan isn’t about replacing people — it’s
about embedding intelligence into processes to:
• Enable time savings and improved efficiency.
• Unlock team capacity for creativity and problem-solving.
• Deliver better architecture and engineering outcomes.
Bottom Line
AI empowers people, not replaces them. By strategically integrating AI into workflows, you can
redefine how design and delivery happen, creating a smarter, faster, and more collaborative
practice.
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