Spatial Artificial IntelligenceFor Architecture, Engineering & Construction

Carmkn is not a chatbot. It is a persistent reasoning system that ingests building models, engineering specifications, and construction documents into a self-learning knowledge graph. It answers questions with deterministic calculations grounded in real project data, not LLM approximation.

Twenty-five autonomous awareness loops run continuously in the background -- learning, monitoring, optimizing, and briefing without being asked. Every file processed, every question answered, every correction made feeds back into the graph. Carmkn remembers, connects, and improves across every project it touches.

Endecta processes BIM models, point clouds, and geospatial datasets into a unified spatial graph. Every element carries its geometry, relationships, and context -- enabling queries that reason about physical space, not just metadata.

From clash detection across linked Revit models to automated equipment placement verification against code requirements, spatial AI turns static building data into an interactive reasoning layer.

Upload architectural drawings, specifications, and BIM models. Endecta extracts entities, relationships, and constraints automatically -- cross-referencing across documents to surface conflicts before they reach the field.

Specifications are parsed into structured data, linked to their referenced standards and product requirements. Design review becomes a conversation, not a checklist.

Engineering documents -- panel schedules, load calculations, single-line diagrams -- are ingested and decomposed into structured knowledge. Each extracted entity is linked to its source document, page, and bounding region.

The knowledge graph captures relationships between systems: electrical feeds mechanical, mechanical serves architectural zones. Queries traverse these connections to answer cross-discipline questions in seconds.

Construction teams deal with thousands of submittals, RFIs, and change orders across a project lifecycle. Endecta resolves entities across these documents -- matching a submittal to its spec section, tracking change order impacts across affected systems.

Field teams get answers grounded in the project record, not hallucinated summaries. Every response traces back to specific documents, pages, and extracted data.

Project data lives in schedules, budgets, contracts, and daily reports. Endecta connects these streams into a unified view -- surfacing cost variances, schedule risks, and resource conflicts that spreadsheets hide.

Ask questions that span data sources: "Which subcontractors are behind schedule on work that affects the critical path?" The answer pulls from schedule data, daily logs, and contract scopes simultaneously.

The Carmkn Revit plugin extracts model data directly from the Revit environment -- elements, parameters, spatial relationships, and linked model references. No export/import cycle, no data loss from format conversion.

Composite ZIP workflows handle linked models that reference external files. The extraction pipeline resolves cross-model relationships, deduplicates shared elements, and preserves the full model hierarchy.

Building codes are complex, jurisdiction-specific, and constantly evolving. Endecta ingests code documents and maps requirements to project elements -- checking Title 24 energy compliance, NEC electrical requirements, and local amendments automatically.

Compliance checking runs against the actual project data, not simplified inputs. When a panel schedule violates NEC 408.36, the system identifies the specific panel, the violation, and the code section -- with citations.

Quantity takeoffs are extracted directly from BIM models and documents -- counts, measurements, and material specifications linked to their source elements. Revision tracking shows what changed between document versions at the entity level.

Cross-project analysis reveals patterns: typical costs per square foot by building type, common spec deviations, recurring clash categories. Historical project data becomes a decision-making asset.

AEC firms compete on expertise, relationships, and execution track record. Endecta structures internal project data into competitive intelligence -- win rates by project type, team utilization patterns, and client relationship depth.

Strategic planning conversations are grounded in your own data. "What is our average margin on healthcare projects over 50M?" is answered from your project history, not industry benchmarks.

Portfolio-level financial projections combine active project data with historical performance patterns. Revenue recognition, cash flow timing, and resource demand forecasts update as project data changes.

Scenario modeling lets you test decisions before committing: "What happens to Q3 cash flow if we accelerate the hospital project by 6 weeks?" The model pulls from real project data, not assumptions.

Research teams ingest standards documents, technical papers, and manufacturer specifications into the knowledge graph. Cross-referencing happens automatically -- a new ASHRAE standard is linked to affected specifications across your project library.

Technology scouting surfaces relevant innovations by matching emerging methods to your project types and pain points. The system learns what matters to your practice from the questions you ask.