SpeciPlan, Inc. builds AI‑powered software that automates routine workflows for firms in the Architecture, Engineering, and Construction (AEC) sector. Its products, part of the ArchitAI ecosystem, use machine learning to streamline tasks such as design coordination, document management, and project scheduling, reducing manual effort and accelerating project delivery. By integrating directly with existing AEC tools, SpeciPlan helps companies improve efficiency and focus on higher‑value design work.
Funding
Funding not disclosed
Founders
Product
Problem
Architecture, engineering, and construction (AEC) firms often rely on manual drafting, clash detection, and cost estimation processes that are time‑consuming, error‑prone, and difficult to coordinate across project teams.
Solution
SpeciPlan offers the ArchitAI ecosystem, an AI‑driven software suite that automates core AEC workflows. By integrating design, analysis, and project‑management tools, the platform reduces manual effort in drafting, automatically identifies spatial conflicts, and generates cost estimates using predictive models. The AI engine learns from prior project data to improve accuracy and speed over time, enabling faster design iterations and more reliable project planning. Results are delivered through a unified interface that supports collaboration among architects, engineers, and contractors, helping firms accelerate delivery while maintaining quality.
Target Audience
Primary customers are architecture, engineering, and construction firms seeking to streamline design and estimation workflows and improve cross‑disciplinary collaboration.
Features
- AI‑assisted drafting that generates construction documents from high‑level inputs
- Automated clash detection using machine‑learning to flag spatial conflicts early
- Predictive cost estimation models that incorporate material, labor, and schedule data
- Integrated project‑management dashboard for real‑time progress tracking and task assignment
- Seamless data exchange between design, analysis, and budgeting modules within a single ecosystem