AI-powered pre-construction engineering automation for industrial projects
Candid Intelligence applies large language models (Gemma, Qwen, DeepSeek) and agentic AI systems to compress months of pre-construction engineering into hours. The tech stack reveals a focus on document understanding (LangGraph, PydanticAI for structured extraction) paired with industrial engineering simulation (Aspen HYSYS, Aspen Plus), and the project backlog shows active work on safety validation, piping workflows, and scope-gap detection—suggesting the company is building toward fully automated design review and consistency checking for capital projects.
Candid Intelligence automates pre-construction engineering workflows for industrial construction and heavy-asset projects. The platform ingests engineering diagrams and specification documents, applies AI-driven analysis to detect rework causes and scope inconsistencies, and surfaces insights that typically require weeks of manual review by senior engineers. The team is senior-heavy (20 of 21 open roles at senior level) and engineering-focused (17 engineering, 2 product, 2 sales), based in San Francisco with active U.S. hiring. Current work spans document understanding, agentic task automation, human-in-the-loop expert feedback, and instrumentation validation.
Python, PyTorch, Gemma, Qwen, DeepSeek, LangGraph, PydanticAI, Neo4j, Aspen HYSYS/Plus (engineering sims), Next.js/React, FastAPI, PostgreSQL, Supabase, Redis, AWS, Docker.
Agentic AI systems for pre-construction tasks, automating document understanding and piping workflows, safety review automation, scope-gap detection, and human-in-the-loop feedback loops for expert validation.
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