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Construction

Stop managing by spreadsheet. Build on-time and on-budget.

We build project intelligence platforms, safety monitoring systems, resource planning engines, and BIM integration tools for construction firms. The software that catches delays before they compound and keeps crews safe on every site.

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35%

Fewer schedule overruns

55%

Fewer safety incidents

Overview

80% cost overruns aren't inevitable - they're a data problem

Construction software development at 1Raft focuses on the three areas that most directly impact project outcomes: schedule intelligence, site safety, and resource optimization. We bring patterns from 100+ products across adjacent industries to build project delay prediction, computer vision safety systems, material planning engines, and digital twin platforms - each engineered to keep builds on time, on budget, and incident-free.

Construction projects fail at the data layer. Project managers track progress through weekly site walks and spreadsheets. Safety teams rely on periodic inspections instead of continuous monitoring. Material orders are placed on gut feel, not demand signals. The result: 98% of megaprojects go over budget, averaging 80% cost overruns.

Our project intelligence platforms detect schedule drift 2-3 weeks before it shows up in reports. Computer vision systems flag safety violations in real time - hardhats, fall zones, exclusion areas - without adding headcount. Resource planning engines forecast material needs based on project phase, weather, and labor availability.

Every product integrates with existing project management, BIM, and ERP systems - Procore, Autodesk, Oracle Primavera, SAP, and custom platforms. We connect to IoT sensors, drones, and site cameras. No rip-and-replace required.

Experience Signal

1Raft builds project intelligence platforms, computer vision safety systems, material planning engines, and BIM integration tools for general contractors, developers, and heavy civil firms. Our engineering draws on patterns validated across 100+ products in adjacent industries.

35%

Fewer schedule overruns

55%

Fewer safety incidents

Industry Pain Points

What's broken in construction

01

98% of megaprojects exceed their original budget by an average of 80% because schedule drift goes undetected until it compounds into months of delay

02

Safety incidents cost the construction industry $171B annually because hazard identification relies on periodic walk-throughs instead of continuous monitoring

03

Material waste runs 20-30% on typical projects because procurement is based on static estimates that don't adjust as scope and schedules change

04

Subcontractor coordination fails when 15-40 trades share a site with conflicting schedules, and conflicts surface only at the daily standup - or worse, on the slab

05

Project closeout takes 3-6 months because punch lists, as-built documentation, and compliance records live in disconnected systems and filing cabinets

Solutions

Problems we solve in construction

Each solution is built from patterns we've validated across 100+ products. No experiments on your budget.

01

Project Schedule Intelligence

Ingests schedule data from Primavera, MS Project, or Procore and overlays weather forecasts, labor availability, permit timelines, and subcontractor performance history. Predicts delays 2-3 weeks before they hit the critical path and recommends re-sequencing options.

02

Computer Vision Safety Monitoring

Processes feeds from site cameras and drones to detect PPE violations, unauthorized zone entry, fall hazards, and equipment proximity risks in real time. Alerts site safety managers instantly and logs incidents for compliance reporting.

03

Resource and Material Planning

Forecasts material requirements by phase using BIM quantities, schedule data, and historical usage patterns. Auto-generates purchase orders timed to delivery windows. Tracks waste by trade and identifies patterns that inflate costs.

04

Digital Twin and BIM Integration

Creates a live digital replica of the project that syncs with BIM models, IoT sensor data, and progress photos. Enables clash detection, progress tracking against the model, and as-built documentation that updates automatically as work completes.

05

Progress Tracking and Reporting

Drone and camera-based progress capture compared against BIM and schedule baselines. Auto-generates weekly progress reports with variance analysis. Gives owners, GCs, and subs a single source of truth instead of conflicting spreadsheets.

Use Cases

Real-world use cases

Schedule Intelligence for a Commercial GC

Problem

A general contractor managing $400M in active projects used Primavera for scheduling but relied on weekly manual updates. Schedule overruns averaged 14 weeks across projects, with delays discovered 3-5 weeks after they started.

What we built

We built a schedule intelligence layer that ingested Primavera data, weather feeds, subcontractor performance scores, and permit status. The system flagged critical-path risks daily and recommended re-sequencing options with cost-impact analysis.

Result

Average schedule overrun dropped from 14 weeks to 5.8 weeks. Early-warning detection improved from 3-5 weeks late to 2 weeks ahead. Liquidated damages exposure decreased by $2.1M across the portfolio in the first year.

Computer Vision Safety for a Heavy Civil Contractor

Problem

A highway construction firm with 1,200 field workers averaged 8.4 recordable incidents per 100 workers annually. Safety officers conducted twice-daily walk-throughs but couldn't cover 12 active zones simultaneously.

What we built

We deployed computer vision models processing 86 site cameras and 4 daily drone flights. The system detected PPE non-compliance, exclusion zone breaches, and struck-by hazards in real time. Alerts routed to safety officers and foremen via mobile app.

Result

Recordable incident rate dropped from 8.4 to 3.1 per 100 workers. PPE compliance rose from 74% to 96%. Near-miss reporting increased 3x as the system caught hazards before they became incidents. OSHA citation costs fell 68%.

Material Planning for a Multi-Site Developer

Problem

A residential developer building 340 units across 4 sites wasted 24% of concrete and 18% of lumber due to over-ordering driven by static takeoff estimates. Material spend ran $2.8M over budget annually.

What we built

We built a material planning engine that synced BIM quantities with phase-level schedules, tracked actual consumption against forecasts, and auto-generated purchase orders timed to delivery windows. Added waste tracking by trade with root-cause classification.

Result

Material waste dropped from 24% to 11% for concrete and from 18% to 8% for lumber. Procurement costs fell 16% through better timing and reduced emergency orders. The system paid for itself in 4 months.

Our Approach

How we approach construction projects

1
Phase 1· Weeks 1-2

Project Operations Audit

We analyze your scheduling process, safety incident data, material waste patterns, and technology stack. We quantify where you're losing money to schedule drift, rework, and procurement inefficiency.

Deliverables

  • Schedule performance analysis with root-cause breakdown of recent overruns
  • Safety incident audit with pattern identification across sites and trades
  • Prioritized opportunity list ranked by cost savings and implementation speed
2
Phase 2· Weeks 3-4

System Design and Integration Planning

We design the product architecture with your project, safety, and IT teams. Every integration point - Procore, Primavera, BIM, cameras, drones, ERP - is mapped before build begins.

Deliverables

  • Technical architecture with integration specifications for all systems
  • Camera and sensor placement plan for safety monitoring coverage
  • Data architecture for schedule, safety, and material analytics
3
Phase 3· Weeks 5-10

Build and Single-Site Pilot

We build in sprints and deploy to one active project site first. Real construction data - schedules, camera feeds, material consumption - validates the product before multi-site rollout.

Deliverables

  • Working product deployed at pilot project site
  • Performance metrics from real project and safety data
  • Iteration backlog based on field team feedback and pilot learnings
4
Phase 4· Weeks 11-14

Multi-Site Rollout and Optimization

We roll out across active projects with site-specific configuration - camera layouts, schedule structures, trade mixes. AI models improve continuously as more project data feeds into the system.

Deliverables

  • Multi-site deployment with per-project configuration
  • Executive dashboard tracking schedule, safety, and cost KPIs across portfolio
  • Quarterly optimization plan tied to safety targets and cost reduction goals

Outcomes

Measurable outcomes

20-35% reduction in schedule overruns through early-warning detection and automated re-sequencing recommendations
40-60% decrease in recordable safety incidents through real-time computer vision monitoring and proactive hazard alerts
15-25% reduction in material waste through demand-driven procurement and phase-level consumption tracking
50-70% faster project closeout through automated punch list management and as-built documentation

Pattern Transfer

1Raft built predictive maintenance systems for manufacturing clients using IoT sensor data to detect equipment anomalies before failures. That same pattern - streaming sensor data, anomaly detection, automated alerts - is exactly what powers our construction safety monitoring. The sensors moved from factory floors to job sites; the detection architecture stayed the same.

Services

Services for construction

Frequently asked questions

Projects range from $60K-$200K. A schedule intelligence platform starts around $60K. Computer vision safety monitoring with camera integration runs $80K-$150K. A full project intelligence suite - scheduling, safety, and material planning - runs $150K-$200K. We provide a fixed estimate after a strategy session.

Next Step

Every week of undetected schedule drift costs you 3-5x more to recover than to prevent.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

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