AECForward
AECforward.ai
Since
Solution Overview
for
Construction companies generate large volumes of valuable technical data across PDF drawings, BIM models, spreadsheets, ERP systems and engineering documents. Yet much of this information remains fragmented and requires significant expert time to interpret and process.
Generic AI tools struggle with construction-specific drawings, engineering rules, geometry and structured technical data. As a result, many critical workflows тАФ estimating, product configuration, engineering design, technical document analysis and reporting тАФ remain highly manual.
AECforward addresses this gap by combining AI with construction engineering and computational design to automate these specialized workflows.
Our Solution:
AECforward designs and deploys bespoke AI systems for engineering firms, contractors and manufacturers.
Our solutions combine machine learning, computer vision, AI agents, engineering rules and parametric 2D/3D modelling.
Depending on the workflow, a system can read client PDF drawings and technical documents, extract geometry and structured data, apply engineering or product-selection rules, interact with company databases and generate calculations, 3D models, drawings, reports or quotations.
Rather than forcing companies into a generic SaaS workflow, we integrate AI with their existing expertise, data and software environment. Solutions can be deployed in the cloud or within the client’s own infrastructure.
How it works:
AECforward bridges the gap between generic AI and the highly specialized requirements of construction and engineering.
Our differentiation is the combination of AI expertise with structural engineering, computational design and deep knowledge of AEC data. We do not limit AI to text: our systems can reason across technical documents, drawings, structured databases, engineering rules and 2D/3D geometry.
We also combine deterministic engineering and parametric methods with machine learning and generative AI, selecting the most appropriate technology for each part of a workflow.
Classification & Use Cases
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Impact & SDGs
Contribution to any of the 17 Sustainable Development Goals:
SDG 11 тАУ Sustainable Cities and Communities: Better use of building data and engineering automation enables more efficient design and delivery of the built environment.
SDG 12 тАУ Responsible Consumption and Production: Automated design, configuration and data-driven decision-making can improve material selection, reduce errors and support more efficient use of construction resources.
SDG 13 тАУ Climate Action: We develop machine-learning applications that analyse building environmental data, including prediction of embodied and operational carbon from early design parameters.
SDG 8 тАУ Decent Work and Economic Growth: Automation reduces repetitive technical work and allows engineers and construction professionals to focus on higher-value decisions and expertise.
