AI for Construction Companies

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.