Are project delays and cost overruns only visible to you after they have already happened?
Are site safety audits catching non-compliance issues, or are they mostly surfacing problems after an incident?
AI for Construction Companies
Construction projects run over budget and behind schedule not because teams are careless, but because the warning signs are buried in schedule data, resource logs, and contract documents that no one has time to analyse systematically. AI changes what is visible before it becomes a problem.
We build AI systems for construction companies: project delay prediction from schedule and resource data, cost overrun prediction, computer vision safety compliance monitoring, contract and specification document extraction, material quantity estimation, equipment maintenance prediction, subcontractor performance scoring, and BIM data analysis. Each system is scoped against your project data and a specific cost, schedule, or safety target.
Delay risks surfaced weeks before they show up in a progress report
Cost overrun probability scored from your schedule, resource, and change order data
Safety non-compliance detected on site by computer vision before an incident occurs
Contract obligations and specification requirements extracted automatically from dense project documents
RaftLabs builds AI systems for construction companies including project delay prediction models trained on schedule and resource data, cost overrun probability scoring using change order history and resource utilisation, computer vision safety compliance monitoring on construction sites, contract and specification document extraction using NLP, material quantity estimation from drawings and BIM data, predictive maintenance for construction equipment, subcontractor performance scoring, and BIM data analysis tools. Engagements are scoped at a fixed price after a discovery phase that maps your project management data, schedule history, and document library to the specific AI capability being built.
Construction projects fail predictably. AI makes the prediction systematic.
Most delay and cost overrun events follow patterns. The project that runs out of float in month two rarely recovers. The subcontractor who missed the first three milestones rarely delivers the fourth on time. The safety incident that happens on a Friday afternoon on a site without adequate supervision was not unforeseeable. AI trained on your historical project data surfaces these patterns on active projects before the damage is done.
What we build
Project delay prediction
Delay risk models trained on your historical project schedule data, resource utilisation logs, and milestone completion records. The model scores each active project weekly with a delay probability and the contributing signals: float consumption rate, resource overrun, subcontractor milestone hit rate, and change order volume. Project managers see which projects are at risk and why while there is still schedule contingency to protect. Requires at least 20-30 completed projects with schedule actuals versus plan and resource utilisation history.
Cost overrun prediction
Cost risk models trained on your project cost history, change order logs, and resource consumption data. The model produces a cost-at-completion estimate that accounts for the current trajectory of resource utilisation, the volume and pattern of change orders, and the stage of the project relative to cost-to-complete patterns on historical comparable projects. Flags projects where the current cost trajectory puts the contingency at risk before the cost report shows it. Gives commercial managers an early signal to review scope, resource, and subcontract exposure.
Computer vision safety compliance monitoring
Camera-based safety compliance detection covering PPE compliance (hard hats, high-visibility vests, harness at height), exclusion zone breaches during plant and machinery operation, and access route safety. Alerts delivered to site safety managers in real time with a timestamped image and location. Works with your existing CCTV infrastructure or purpose-positioned cameras for specific zones. Continuous monitoring of high-risk areas versus periodic safety walks. Full event log for audit trail and incident investigation purposes.
Contract and specification document extraction
NLP pipeline that reads contracts, subcontract agreements, and technical specifications and extracts structured data: obligation dates, milestone payment triggers, penalty clause thresholds, scope inclusions and exclusions, material specifications, testing requirements, and hold and witness points. Output is a structured summary for contracts managers and commercial teams, cross-referenceable with programme and procurement schedules. Reduces per-contract review time and reduces the risk of missing an obligation buried in a dense appendix.
Equipment maintenance prediction
Predictive maintenance models for construction plant and equipment: tower cranes, excavators, concrete pumps, generators, and site machinery. Trained on your maintenance history, equipment usage logs, and fault records to predict failure before it causes downtime. Surfaces high-risk equipment with the contributing signals: operating hours since last service, usage pattern deviation, and fault code history. Allows maintenance to be scheduled during planned downtime rather than responding to an unplanned breakdown that stops a programme-critical activity.
Subcontractor performance scoring and BIM analysis
Subcontractor performance scoring models that use your project records to build a milestone hit rate, defect rate, variation frequency, and safety event score for each subcontractor. Surfaces performance trajectory on active packages and provides a data-driven input to procurement decisions on new packages. BIM data analysis tools that extract quantity and specification data from your BIM models, cross-reference against procurement and programme records, and flag discrepancies between model data and site installation records or purchase orders.
Which project risk problem are you trying to see earlier?
Delay, cost overrun, safety, or subcontractor performance: tell us the specific problem and we will assess which AI system addresses it and what your project data supports.
Related services
AI Development -- end-to-end custom AI system builds
Predictive Analytics -- risk scoring and forecasting models across industries
Computer Vision Development -- vision AI for detection and compliance monitoring
AI Document Intelligence -- document extraction for contracts and specifications
Business Process Automation -- construction operations automation
AI for Construction by area
Construction Software -- project management, estimating, site operations
Construction Automation -- RFI tracking, subcontractor payments, reporting automation
Field Service Automation -- mobile workforce, job dispatch, invoice automation
Frequently asked questions
Project delay prediction models are trained on historical project data where you know the outcome: projects that were delivered on schedule, projects that ran late, and the degree of delay. The model learns which combinations of early-project signals predict schedule slippage. Common high-signal features include: schedule float consumption rate in the first quarter of a project (burning through contingency early is a strong predictor of later delays), resource utilisation versus plan (teams running at over 100% utilisation for extended periods are a delay precursor), subcontractor milestone hit rates in early phases, change order volume and timing, and weather or site access disruptions relative to plan. The model scores each active project on a weekly basis and produces a delay risk score with the contributing factors. Project managers see which projects are at risk and why -- not a generic red-amber-green status, but a specific signal: float consumption is running at 2.3x the planned rate, and this is the activity driving it. This shifts delay management from reviewing what has already happened in a progress report to seeing the trajectory and intervening while there is still float to protect. To build effectively, we need historical project schedule data with actual versus planned milestones, resource utilisation records, and change order logs from at least 20-30 completed projects. We assess your project management system data in discovery.
Computer vision safety monitoring uses cameras positioned at high-risk zones of the construction site -- working at height areas, plant and machinery exclusion zones, access routes, and ground-level activity areas -- to continuously analyse the video feed for safety non-compliance. The model is trained to detect: workers without personal protective equipment (hard hats, high-visibility vests, safety boots, and harness where required), workers in exclusion zones during plant and machinery operation, workers without harness at height, and crowding in areas where social or safety distancing is required. When the model detects a non-compliance event, it generates an alert: a notification to the site safety manager with a timestamped image and the location. The site safety manager can review the image and take action immediately rather than waiting for the next safety walk. The system keeps a log of all detected events and their resolution, which supports your safety audit trail and incident investigation process. Camera positions and alert thresholds are configured to your site layout and the specific PPE requirements of each zone. The system works with standard CCTV cameras, which most construction sites already have, or with purpose-positioned cameras for zones where existing camera coverage is insufficient. We assess your existing camera infrastructure and the specific compliance requirements of your site types during discovery.
Contract and specification document extraction uses natural language processing to read construction documents -- contracts, subcontract agreements, technical specifications, drawings registers, and scope of work documents -- and extract the structured data your teams need to act on. What this means in practice: the system reads a 300-page subcontract and extracts the key obligation dates, milestone payment triggers, penalty clause thresholds, scope inclusions and exclusions, and insurance and compliance requirements into a structured summary that a contracts manager can review in 10 minutes rather than 2 hours. For technical specifications, the system extracts material specifications, testing requirements, quality standards references, and hold and witness point requirements. The extracted data is presented in a structured format that can be cross-referenced with your programme and procurement schedule. For organisations processing a high volume of contracts (frameworks, multiple active projects, or procurement of many subcontract packages), this reduces the manual review time per contract significantly and reduces the risk of missing an obligation buried in clause 47 of an appendix. We assess your typical document types and the specific data fields your contracts and commercial teams need to extract during scoping. Document quality -- scanned PDFs versus native digital documents -- affects extraction accuracy and we will advise on that tradeoff before building.
Subcontractor performance scoring models use your historical project data to build a performance record for each subcontractor you have worked with: milestone hit rate on programme, defect rates at practical completion, variation and change order frequency, safety event history, payment claim accuracy, and responsiveness to instruction. The model scores each subcontractor across these dimensions using your own project records rather than relying on subjective assessment or reference calls. For current active projects, the model flags subcontractors whose current performance trajectory on an active package is deviating from their historical pattern -- a subcontractor with a strong track record who is running behind on a current package gets flagged earlier than a standard progress review would surface the issue. For procurement decisions on new packages, the model surfaces the historical performance record of shortlisted subcontractors against the specific package type being procured -- their score on concrete works is a separate record from their score on M&E installation. This gives your commercial team a structured, data-driven input to subcontractor selection and early warning of performance deterioration on active packages. The quality of the output depends on the consistency and completeness of your historical project records. We assess data availability and help structure data collection for projects where records are incomplete.