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Nomic AI

Domain-specific AI for code compliance and document intelligence in AEC

AEC-focused AI platform that understands 380+ building codes and standards. Automates code compliance checking, QA/QC reviews, RFI management, and submittal reviews by grounding AI in firm documents and industry standards.

Pricing
$50 free credits available
Alternatives
Imerso, Part3, CostWaves
Who it's for
Architecture, Structural Engineering, MEP Engineering
$17M raisedGrowth Stage
Founded 2022 · Enterprise AEC firms including Aurecon (global design firm); 10-50 employees
Website → ← All Tools
WHY IT EXISTS

Why This Tool Exists

The Problem

AEC teams waste 5-15 hours per week manually checking code compliance, reviewing QA issues, and processing RFIs. Without grounding AI in actual project documents and codes, generic AI tools produce unreliable outputs that require extensive human verification.

The Solution

Nomic grounds AI in firm documents and 380+ building codes to automate compliance checking, quality reviews, and RFI processing. Responses are cited to source documents, enabling teams to trust outputs and operationalize AI across workflows.

USAGE

How You Use It

Delivery Method
SaaSAPI
Integrations
Autodesk Construction Cloud (ACC)BentleySharePointEgnyte
Disciplines
Architecture, Structural Engineering, Mep Engineering, Construction, Civil Engineering
Workflows
Coordination & Delivery, Code & Compliance
Project Phases
Construction Documentation, Bidding/Procurement, Construction Administration
Project Types
Commercial, Residential, Healthcare, Education, Infrastructure, Mixed-Use, Industrial
TRANSPARENCY

Data Transparency

Exactly what this tool uses and how

Input
What it needs
Required:Building drawings, Project specifications, Building codes
Optional:RFIs, Submittals, Project schedules, Historical project data, Quality standards
Formats:PDF, DWG, IFC, Native CAD formats, Structured specifications
Output
What you get
Format:Cited compliance reports and automated review results
Fields:Code compliance status and violations, Cited regulations and standards, QA/QC issue flags with locations, RFI responses with source citations, Submittal review recommendations, Referenced project documents and code sections
Algorithm
How it works
Model:Proprietary domain-specific AI models trained on AEC documents and building codes
Accuracy:92% time reduction in repetitive processes (reported outcomes, methodology not published)
Privacy
How your data is protected
Retention:Project-based retention with option for zero data retention per legally binding agreements
Training:Not used to train models (zero data retention policy)
Compliance:SOC 2 Type II certified, Annual penetration testing, GDPR privacy notice for EU/UK residents
API
Integration
Endpoint:https://docs.nomic.ai/reference/api/home
Method:REST API with parsing, extraction, and embedding endpoints
USE CASES

Use Cases

  • ·Automated code compliance checking across 380+ standards
  • ·QA/QC automation to flag quality issues before manual review
  • ·RFI response automation with source citations
  • ·Submittal review against specifications and drawings
  • ·Drawing analysis and specification search across historical projects
PRICING

Pricing

Free
Not available
Pro
Credit-based model (1 credit = 1 page; $50 free credits available)
Enterprise
Custom pricing based on document volume and deployment requirements
Sources & Research NotesResearch date: 2026-02-01
Data Sources (10)
Fields Checked (26)
vendor, website, fundingTotal, yearFounded, maturityStage, problem, solution, deliveryMethod, integrations, disciplines, projectPhases, projectTypes, input-required, input-optional, input-formats, output-format, output-fields, algorithm-approach, algorithm-accuracy, privacy-retention, privacy-location, privacy-compliance, control-features, api-endpoint, api-auth, use-cases
Not Found (5)
customerBase-specific-names, api-rate-limits, pricing-exact-tiers, algorithm-model-details, training-data-sources
About this research: This record summarizes the cited sources and fields checked. A research date does not establish hands-on testing. Consult the original sources for current details and limitations.
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