SLED Opportunity · COLORADO · COLORADO DEPARTMENT OF LOCAL AFFAIRS
AI Summary
The Colorado Department of Local Affairs seeks information from firms experienced in AI solutions for building construction code compliance, focusing on NLP, ML, and user-friendly interfaces. This RFI aims to gather insights for a generative AI construction plan review system.
Information Requested Who We Are Looking For We are particularly interested in hearing from firms with experience in implementing similar AI solutions within state-level systems. Additionally, any experience with the following areas would be valuable: Expertise in developing AI systems for code/regulatory compliance, preferably in building construction. Experience with natural language processing (NLP) and machine learning (ML) to analyze and interpret building plans. Experience with developing user-friendly interfaces for complex systems. Experience with working with government agencies and stakeholders to gather requirements and ensure compliance. Experience with agile development methodologies. Experience with piloting and scaling AI systems. **Solicitation Type**: RFI - Request for Information (Formal) **Source ID**: PU.AG.USA.1652595.C11245501 **Piggyback Contract**: No **Question Acceptance Deadline**: 07/01/2026 07:00 PM EDT **Questions are submitted online**: No **Bid Submission Type**: Electronic Bid Submission **Additional Bidding Instructions**: Email one copy of the information document to brenda.lujan@state.co.us Upload one copy to BidNet in .pdf format **Owner Organization**: Department of Local Affairs **Solicitation Number**: RFI-NHAA-2026000047 **Reference Number**: 0000428938 **Pricing**: No Pricing **Bid Documents List**: | Item Name | Description | Mandatory | Limited to 1 file | |---|---|---|---| | Bid Documents | Documents defining the proposal | Yes | No |
SLED stands for State, Local, and Education. These are solicitations issued by state governments, counties, cities, school districts, utilities, and higher education institutions — as opposed to federal agencies.
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