# Floorbase

*/Startups/Floorbase*

## Startup Overview

The software converts raw LiDAR scans directly into annotated 3D floor models. It ingests unorganized point cloud data and outputs clean, usable architectural geometry without human intervention.

Architects and reality capture teams lose days translating massive point clouds into actionable spatial data. Traditional methods force teams to rely on intensive manual point cloud processing or closed hardware-software ecosystems that lock down data.

Unlike rigid subscriptions from Matterport or highly manual workflows in Faro Scene, this platform operates with full automation. Users upload raw scan data and pay only for the architectural features successfully extracted, ensuring costs map exactly to the structural complexity of the delivered model.

## Startup Founding Hypothesis

**Approach**: that converts raw LiDAR scans into annotated 3D floor models
**Competitors**:
- [Manual Point Cloud Processing](/Competitors/Manual_Point_Cloud_Processing)
- [Matterport](/Competitors/Matterport)
- [Faro Scene](/Competitors/Faro_Scene)
**Differentiator2x2**: fully automated and priced per extracted architectural feature

## Startup Solution Coordinate

**Solution**: [Floorbase Spatial Extractor](/Services/Floorbase_Spatial_Extractor)

## Startup Position2x2

```mermaid
quadrantChart
    title LiDAR Processing vs Pricing
    x-axis Manual Processing --> Fully Automated
    y-axis Standard Pricing --> Pay Per Extracted Feature
    quadrant-1 Automated Pay-Per-Feature
    quadrant-2 Manual Pay-Per-Feature
    quadrant-3 Traditional Services
    quadrant-4 Automated Subscriptions
    Manual Point Cloud Processing: [0.15, 0.15]
    Matterport: [0.85, 0.10]
    Faro Scene: [0.45, 0.20]
    Floorbase: [0.90, 0.85]
```

## Startup Offer

**Proof**:
- Targeting a 10-minute turnaround time for fully annotated 10,000 sq ft commercial spaces.
- Aiming to eliminate 90% of manual point-cloud tagging labor for architectural surveying teams.
- Designed to export extracted features directly into native Revit and AutoCAD formats.
**Tiers**:
- Name: Base Geometry · Price: ~$0.05–$0.10 per extracted feature · Inclusions: Automated identification and 3D annotation of core architectural elements (walls, doors, windows, stairs) from raw point clouds.
- Name: Comprehensive Systems · Price: ~$0.15–$0.30 per extracted feature · Inclusions: Full structural and MEP extraction, including structural beams, exposed piping, HVAC ducts, and electrical fixtures.
- Name: Enterprise Volume · Price: ~$0.02–$0.08 per extracted feature · Inclusions: Discounted rate for surveying firms processing over 50 large-scale scans per month, featuring a hard price-cap per building.
**Guarantee**: Floorbase guarantees 95% feature recognition accuracy on standard commercial LiDAR scans; any missed or mislabeled elements reported within 7 days will be manually corrected and returned at no charge.
**Business Function**: ProvideService
**Objection Handlers**:
- Objection: High-density scans with complex clutter will generate false features and drive up the per-feature cost. Rebuttal: The pricing model includes a per-scan maximum cap, ensuring complex environments never exceed the cost of traditional manual processing.
- Objection: Point cloud noise and reflective surfaces will ruin the extraction accuracy. Rebuttal: Intended to utilize multi-pass filtering algorithms that clean ghosting artifacts before applying the feature recognition models.
- Objection: We cannot trust an AI to define load-bearing structural beams correctly. Rebuttal: Floorbase is designed to tag features with a confidence score, isolating low-confidence elements into a separate layer for human verification.
**Pricing Architecture**: UsageMeter
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Technical and exact, rooted in strict dimensional accuracy and structural truth.
**Tagline**: Annotated 3D floor models generated directly from raw LiDAR scans.
**Icon Concept**: tripod
**Palette Intent**: industrial-safety
**Visual Identity**: The visual identity combines high-visibility safety yellow with deep concrete grey, employing stark, grid-aligned typography that mirrors physical architectural drafting.
**Archetype Reference**: the-creator

## Startup Buyer Chain

**Chain**: Floorbase → Reality Capture Service Provider → Architect / General Contractor
**Gtm Motion**: Acquires surveying and reality capture firms through targeted search for point-cloud processing alternatives. Expands account value organically through a usage-based pricing model tied directly to the volume of architectural features extracted from each uploaded LiDAR scan.
**Agent Channel**: Designed to list in the LangChain tool registry and the OpenAI API schema directory, enabling autonomous BIM agents to discover and trigger automated 3D feature extraction from raw scan files.
**Primary Channel**: Targeted search marketing capturing high-intent technical queries for 'automated LiDAR to BIM conversion' alongside an intended presence in the Autodesk App Store.

## Startup Customer Journey

```mermaid
flowchart LR; A[LiDAR Conversion Query] --> B[Autodesk App Store Listing]; B --> C[Raw Point Cloud]; C --> D[Annotated Architectural Feature]; D --> E[Revit Native File]; E --> F[Comprehensive Systems Tier]; F --> G[Enterprise Volume Agreement];
```

## Startup Proof Points

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**Pilot Goals**:
- 30-day parallel processing pilot with a regional surveying firm: Run 10 raw commercial LiDAR scans through Floorbase alongside traditional manual Revit drafting to prove the 10-minute turnaround and verify the 95% accuracy guarantee.
- 60-day volume testing pilot with a national engineering consultancy: Process 50+ large-scale scans using the Comprehensive Systems tier to validate that the automated extraction of HVAC and structural beams reduces junior drafting overhead by at least 80%.
**Target Metrics**:
- Target: 90% reduction in manual point-cloud tagging labor hours for architectural surveying teams.
- Aim: 10-minute automated turnaround time for fully annotated 10,000 sq ft commercial spaces.
- Target: 95% feature recognition accuracy on standard commercial LiDAR scans prior to human review.
- Aim: 100% successful direct export rate of recognized elements into native Revit and AutoCAD layers.
**Target Case Studies**:
- Mid-sized architectural surveying firm: Replaces manual point-cloud tagging with automated extraction, cutting the time to deliver native Revit models for 10,000 sq ft commercial spaces from days to under one hour.
- Commercial general contractor MEP division: Processes complex high-density LiDAR scans to isolate exposed piping and HVAC ducts, enabling rapid clash detection without dedicating junior draftspersons to manual tracing.
- High-volume commercial real estate brokerage: Deploys the Base Geometry tier to instantly generate floor plans and core architectural elements from raw point clouds across their property portfolio at a capped per-building cost.
**Testimonial Targets**:
- BIM Manager at a surveying firm: Validates that the confidence-score layering system successfully isolates complex load-bearing ambiguities for human review while flawlessly automating standard wall and door geometry.
- Principal Architect: Expresses relief that the per-building cost cap keeps surveying expenses strictly predictable, even on high-clutter or highly complex renovation projects.
- Virtual Design & Construction (VDC) Coordinator: Highlights how the multi-pass filtering accurately ignores ghosting and reflective noise, delivering clean MEP system extraction without manual cleanup.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: The automated extraction algorithm fails to reliably distinguish structural elements from furniture or noise in messy LiDAR scans, requiring expensive manual correction that breaks the unit economics. · Mitigation Status: in-progress
- Severity: high · Description: Major LiDAR hardware manufacturers lock down their raw point cloud formats or restrict API access, preventing the system from ingesting source data. · Mitigation Status: unmitigated
- Severity: moderate · Description: Enterprise clients accustomed to flat-rate or per-square-foot billing reject the unpredictable per-feature pricing model, stalling sales cycles. · Mitigation Status: unmitigated
- Severity: low · Description: Processing dense point clouds incurs high cloud compute costs that compress margins before the processing engine is fully optimized. · Mitigation Status: in-progress

## Startup Competitors

- [Manual Point Cloud Processing](/Competitors/Manual_Point_Cloud_Processing) — Status Quo
- [Matterport](/Competitors/Matterport) — Incumbent Platform
- [Faro Scene](/Competitors/Faro_Scene) — OEM Software
- [Leica Cyclone](/Competitors/Leica_Cyclone) — Legacy Enterprise
- [NavVis IVION](/Competitors/NavVis_IVION) — Enterprise Solution

## Startup Story Brand

**Hero**:
- **Need**: to be the firm delivering high-precision architectural truth, not a bottleneck for project timelines
- **Want**: to turn raw LiDAR point clouds into ready-to-use Revit models without weeks of manual tracing
- **Identity**: the lead surveyor at an architectural scanning and BIM firm
**Plan**:
- Step: Upload · Detail: Drop your raw LiDAR scans directly into the secure Floorbase processing portal.
- Step: Validate · Detail: Review the automated 3D annotations and high-confidence feature layers on your dashboard.
- Step: Export · Detail: Download the fully labeled geometry directly into native Revit or AutoCAD formats.
**Guide**:
- **Empathy**: Does your scanning workflow still stall while technicians spend days labeling basic wall geometry?
**Problem**:
- **Villain**: manual feature tagging
- **External**: Surveying teams lose hundreds of billable hours inside Faro Scene or Matterport manually clicking and labeling every door, window, and pipe
- **Internal**: You feel like your expensive LiDAR equipment is wasted when you are still tracing lines like a 1990s CAD drafter
- **Philosophical**: Why should a surveyor accept tedious point-cloud tracing when architectural intent is already visible in the scan?
**Success**: You deliver fully annotated 10,000 sq ft models in under ten minutes, slashing manual labor by 90% while maintaining structural truth.
**One Liner**: Manual point-cloud processing costs surveying firms weeks of billable time. Floorbase automates architectural feature extraction directly from LiDAR scans so models are ready for Revit in minutes.
**Positioning**:
- **So That**: convert raw scans to annotated models 10x faster
- **Unlike**: manual point cloud processing
- **For Whom**: architectural scanning and BIM firms
- **Category**: Automated 3D feature extraction service
**Call To Action**:
- **Direct**: Upload a scan
- **Transitional**: View sample annotated model
**Failure Stakes**:
- Missing project delivery deadlines
- Inflated labor costs per scan
- Inaccurate manual measurements
**Transformation**:
- **To**: free to lead high-scale architectural surveys, no longer stuck doing the drudgery
- **From**: a technician buried in manual point-cloud tagging
**Controlling Idea**: LiDAR scans should be instantly readable architectural models, not manual tracing exercises.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: Manual point-cloud processing costs surveying firms weeks of billable time. Floorbase automates architectural feature extraction directly from LiDAR scans so models are ready for Revit in minutes.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 1b885cd3a260e716

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Automated 3D feature extraction service for architectural scanning and BIM firms. Unlike manual point cloud processing — convert raw scans to annotated models 10x faster.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: 178d1bbec294f59b

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Surveying teams lose hundreds of billable hours inside Faro Scene or Matterport manually clicking and labeling every door, window, and pipe
Solution: Manual point-cloud processing costs surveying firms weeks of billable time. Floorbase automates architectural feature extraction directly from LiDAR scans so models are ready for Revit in minutes.
Customer: architectural scanning and BIM firms
Unlike: manual point cloud processing
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: 3c99a78975955a8b

## Startup Token M E D D P I C C

**Pain**: Surveying teams lose hundreds of billable hours inside Faro Scene or Matterport manually clicking and labeling every door, window, and pipe
**Metrics**: Target: You deliver fully annotated 10,000 sq ft models in under ten minutes, slashing manual labor by 90% while maintaining structural truth.
**Rendered**: Pain: Surveying teams lose hundreds of billable hours inside Faro Scene or Matterport manually clicking and labeling every door, window, and pipe
Economic buyer: Reality Capture Service Provider
Metrics: Target: You deliver fully annotated 10,000 sq ft models in under ten minutes, slashing manual labor by 90% while maintaining structural truth.
Competition: manual point cloud processing
**Mechanism**: spine-derived-v1
**Competition**: manual point cloud processing
**Economic Buyer**: Reality Capture Service Provider
**Vocab Fingerprint**: ee59203a473eca4f

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Automated 3D feature extraction service for architectural scanning and BIM firms

architectural scanning and BIM firms — Surveying teams lose hundreds of billable hours inside Faro Scene or Matterport manually clicking and labeling every door, window, and pipe Manual point-cloud processing costs surveying firms weeks of billable time. Floorbase automates architectural feature extraction directly from LiDAR scans so models are ready for Revit in minutes.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: ab8ef15a8d0621ed

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Automated 3D feature extraction service. Manual point-cloud processing costs surveying firms weeks of billable time. Floorbase automates architectural feature extraction directly from LiDAR scans so models are ready for Revit in minutes. Serves architectural scanning and BIM firms.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: a07d6249b3d4a322

## Neighborhood

### Candidate solutions

- [Specialized Floor Staff Recruitment](/Problems/Specialized_Floor_Staff_Recruitment) — candidate solution for · Problems
- [Bindery Equipment Injury Claims](/Problems/Bindery_Equipment_Injury_Claims) — candidate solution for · Problems

### Competitors

- [Manual Point Cloud Processing](/Competitors/Manual_Point_Cloud_Processing) — competes with · Competitors
- [NavVis IVION](/Competitors/NavVis_IVION) — competes with · Competitors
- [Leica Cyclone](/Competitors/Leica_Cyclone) — competes with · Competitors
- [Matterport](/Competitors/Matterport) — competes with · Competitors
- [Faro Scene](/Competitors/Faro_Scene) — competes with · Competitors
- [Facebook Groups](/Competitors/Facebook_Groups) — competes with · Competitors
- [impromptu mechanical interviews](/Competitors/impromptu_mechanical_interviews) — competes with · Competitors
- [ZipRecruiter](/Competitors/ZipRecruiter) — competes with · Competitors
- [Indeed](/Competitors/Indeed) — competes with · Competitors
- [Indeed and ZipRecruiter](/Competitors/Indeed_and_ZipRecruiter) — competes with · Competitors
- [local Facebook Groups](/Competitors/local_Facebook_Groups) — competes with · Competitors
- [manual mechanical interviews](/Competitors/manual_mechanical_interviews) — competes with · Competitors
- [impromptu mechanical tests](/Competitors/impromptu_mechanical_tests) — competes with · Competitors
- [Craigslist](/Competitors/Craigslist) — competes with · Competitors
- [Indeed Retail Postings](/Competitors/Indeed_Retail_Postings) — competes with · Competitors
- [Workday Recruiting](/Competitors/Workday_Recruiting) — competes with · Competitors
- [Keyword Scraping](/Competitors/Keyword_Scraping) — competes with · Competitors
- [Indeed Retail Boards](/Competitors/Indeed_Retail_Boards) — competes with · Competitors
- [Hobbyist Facebook Groups](/Competitors/Hobbyist_Facebook_Groups) — competes with · Competitors
- [ZipRecruiter Talent Networks](/Competitors/ZipRecruiter_Talent_Networks) — competes with · Competitors
- [Impromptu In-Person Tests](/Competitors/Impromptu_In-Person_Tests) — competes with · Competitors
- [Craigslist Job Boards](/Competitors/Craigslist_Job_Boards) — competes with · Competitors
- [impromptu interview tests](/Competitors/impromptu_interview_tests) — competes with · Competitors
- [Generic Job Boards](/Competitors/Generic_Job_Boards) — competes with · Competitors
- [Manual Keyword Scraping](/Competitors/Manual_Keyword_Scraping) — competes with · Competitors
- [manual interview tests](/Competitors/manual_interview_tests) — competes with · Competitors
- [manual vetting](/Competitors/manual_vetting) — competes with · Competitors
- [Manual Mechanical Tests](/Competitors/Manual_Mechanical_Tests) — competes with · Competitors

### Embodies

- [Service-as-Software](/Theses/Service-as-Software) — embodies · Theses

### What it offers

- [Floorbase Spatial Extractor](/Services/Floorbase_Spatial_Extractor) — offers · Services
- [Aptitude Bench](/Services/Aptitude_Bench) — offers · Services

### Composed of

- [Certification Verification API](/Software/Certification_Verification_API) — composes · Software
- [Hobbyist Dexterity Engine](/Software/Hobbyist_Dexterity_Engine) — composes · Software
- [Mechanical Fluency Worker](/Agents/Mechanical_Fluency_Worker) — composes · Agents
- [Gear Tuning Agent](/Agents/Gear_Tuning_Agent) — composes · Agents
- [Aptitude Bench Service](/Services/Aptitude_Bench_Service) — composes · Services
- [Niche Sourcing Worker](/Agents/Niche_Sourcing_Worker) — composes · Agents
- [Gear Fluency Agent](/Agents/Gear_Fluency_Agent) — composes · Agents
- [Floor Aptitude Service](/Services/Floor_Aptitude_Service) — composes · Services
- [Mechanical Scenario Engine](/Software/Mechanical_Scenario_Engine) — composes · Software
- [Equipment Ontology API](/Software/Equipment_Ontology_API) — composes · Software

### Who it serves

- [Sporting Goods Retailers](/CompanyTypes/Sporting_Goods_Retailers) — serves · CompanyTypes

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