AI-powered takeoff software automating construction and field-services estimation
Attentive.ai builds Beam AI, an automated takeoff platform for construction estimating and field-services bidding. The tech stack is heavy on computer vision (PyTorch, OpenCV, TensorFlow, Hugging Face) and geospatial processing (PostGIS), paired with cloud infrastructure (GCP, AWS, Azure) and async job processing (Kafka, RabbitMQ, Celery) — reflecting the pipeline complexity required to process blueprints and aerial imagery at scale. Hiring is accelerating across engineering and support, while active projects signal a shift from purely technical automation toward customer retention: onboarding optimization, adoption tracking, and upsell execution now sit alongside core ML/AI work.
Attentive.ai operates Beam AI, a takeoff and estimation platform serving general contractors, subcontractors, suppliers, and field-services businesses across landscaping, paving, facilities maintenance, and snow removal. The product automates measurement extraction from blueprints and aerial imagery, enabling preconstruction and sales teams to submit bids 2X faster without hiring additional staff. The company serves 1,100+ businesses across the U.S. and Canada and is backed by institutional investors including Insight Partners, Peak XV Partners, Vertex Ventures SEA, InfoEdge Ventures, and Tenacity Ventures. With 201–500 employees and accelerating hiring in engineering and support, the organization is scaling to address both product adoption and customer retention challenges identified as key focus areas.
Python, PyTorch, OpenCV, TensorFlow, Hugging Face, PostgreSQL, GCP/AWS/Azure, Docker, Kubernetes, React, Next.js, Kafka, RabbitMQ, PostGIS for geospatial processing, and CI/CD automation.
Beam AI is trusted by 1,100+ businesses across the U.S. and Canada, including general contractors, subcontractors, suppliers, and field-services companies.
Attentive.ai's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.