Product

Your AI CoWorker for Pre-Con

Transforming construction with AI that learns your role, orchestrates multiple agents, and delivers faster, smarter results without disrupting your existing tools.
Emails PDFs Site Photos Design Files Tender Understanding Bill of Quantities AI Co-worker
Core Technology Pillars
Neuron Factory’s AI CoWorker is designed to integrate seamlessly into existing enterprise workflows, reducing cognitive load and improving efficiency. It learns from skilled workers by continuously mapping tasks through browser tracking, emails, audit logs, and more.

Construction Query Engine (CQE)

Built to ingest and reason over the complex, disparate data of large-scale construction, it unifies drawings, specifications, costs, and schedules into a single queryable hub—empowering agents and workers to easily access, understand, and act on information.

Domain Specific Ontology

Domain specific ontology that is used as a basis for relationships and intent detection.

Multimodal Ingestion

Everything from emails to PDFs. These include text, structural drawings, and version control.

Vector Search + GraphRAG

Data chunks and simple search content kept in Vector Search, full entity extraction and relationships in GraphRAG.

Eval Framework

Understand quality of models, embeddings, and search performance.

Neuron Factory Product

Transforming Raw Data into Actionable Knowledge

We transform unstructured documents into structured knowledge graphs, extracting entities and relationships from raw text and drawings. This semantic layer powers intelligent agent workflows—enabling them to traverse connections, uncover patterns, and make context-aware decisions.  

From document chaos to structured intelligence, we create the construction knowledge fabric that amplifies our AI agents' capabilities.

Reverse Prompting
Traditional prompting requires users to initiate every AI action. With Reverse Prompting, the CoWorker anticipates needs and initiates actions based on workflow changes—like sending an update when a spreadsheet’s delivery timeline changes.
Skilled Worker / Input Prompts
Example: Leo provides an agent a Spreadsheet of delivery times of goods and builds a prompt to have the CoWorker compare with the expected delivery times in a separate sheet. He builds a separate prompt for a different agent based on the output of the first agent to generate an update email.
Skilled Worker + Learning Graph / Input Prompt + Passive Tracking
Example: Leo prompts the CoWorker to generate a timeline update email, the spreadsheet and the timeline would already be tracked as part of Leo’s high engagement surface and their context to the workflow already known.
Learning Graph / Proactive Prompts
Example: Leo is reverse prompted to send a canned timeline update email because of a change made in the spreadsheet impacting the delivery timeline.
All-in-One

The Future of Construction Starts Here

Join forward-thinking teams already using AI to simplify construction workflows.
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