Construction and Property
Win better work, and stop losing margin in the paperwork.
Tender intelligence, addenda tracking and project reporting that keep estimators and contract administrators ahead of the documents instead of buried in them.
Operational context
How the sector actually runs.
Bids are won and lost in the estimating window. A head contractor may receive several hundred pages of drawings, specifications and annexures per tender, with a fortnight to price it and a scope that keeps moving until the day before submission.
Addenda arrive late and change things quietly. A revised specification clause or a new drawing revision can invalidate a subcontractor quote that was priced against superseded documents, and nobody finds out until the variation is disputed on site.
Subcontractor quotes arrive in incompatible formats. Twelve trades might return a mix of PDFs, spreadsheets and emailed prices against inconsistent scope splits, and the estimator normalises them by hand under time pressure.
Project data lives in three places that disagree. The project management system holds the programme, the finance system holds the cost, and the site team holds the truth in diaries, photographs and WhatsApp messages that never reach either.
Margin erosion is usually administrative rather than technical. Unclaimed variations, late progress claims, missed defect liability dates and RFIs that sit unanswered cost more on a typical project than any single construction error.
High-value problems
Where the money and the risk actually sit.
We spend the first week of every tender just working out what is actually in scope
Document intelligence extracts the scope, exclusions, key dates, insurance requirements and unusual clauses from the tender set into a structured summary an estimator can review in an hour. The estimator still decides what to price, but they start from a checked extraction rather than a blank page.
An addendum changed the spec and we priced the old one
Automated addenda comparison flags every clause, drawing revision and quantity that changed between document issues, and maps each change to the trade packages already out for quote. The contract administrator gets a change list, not a new set of documents to re-read.
Comparing subcontractor quotes takes days and we still miss gaps
Quotes are parsed into a common scope structure so inclusions, exclusions and rates line up side by side, with gaps between trade packages highlighted. Anything the system cannot confidently map is escalated for manual review rather than silently normalised.
Nobody can tell me the real position on a job until month end
We connect the project management, finance and site systems into a governed reporting layer so committed cost, claimed value, forecast and programme status sit in one place. The number is the same number whoever asks for it.
Our project knowledge walks out the door when a PM leaves
A permission-aware search layer over project documents, RFIs, variations and site records lets the next person find the precedent, the decision and the reason behind it. Access follows existing project permissions, so nothing becomes visible that was not already.
Evidence from the field
Where this has actually worked, and where it has not.
Published results from other organisations, cited so you can check them. None of these are our clients and none of these numbers are ours.
Where the gains are real
The measured gains are in reading, not building. Document-heavy preconstruction work (scope extraction, contract and specification review, progress reporting) is where the sector's evidence sits. Nothing credible shows AI improving on-site productivity at scale.
What the evidence does not support
This is the least-adopted sector in the evidence base. Barriers are not model quality: skills, systems integration and data quality account for the top three, which is a delivery problem rather than a technology one.
RICS member survey, 4,000 respondents worldwide
Global
Surveyed where construction and property professionals actually have AI in production, and what is stopping the rest.
45 per cent reported no AI implementation and 34 per cent were in early pilots, leaving roughly 13 per cent using it regularly in specific processes and under 1 per cent embedded organisation-wide. Contract and document review was among the highest-value applications named, at 30 per cent.
RICS, barriers to adoption
Global
Asked the same respondents what was actually blocking implementation.
Lack of skilled personnel 46 per cent, integration with existing systems 37 per cent, data quality and availability 30 per cent, implementation cost 29 per cent, unclear return on investment 28 per cent.
What it does not prove: Self-reported barriers from a professional-body survey. Useful for direction, not a measurement of any one firm.
International Journal of Construction Management
Peer-reviewed research
Built and evaluated a generative AI model that assembles bid proposal components from a tender set.
An average F1 score of 96.25 on extracting and structuring bid content, with expert evaluation finding reduced manual effort and better consistency between bids.
What it does not prove: A research prototype measured on extraction accuracy, not a commercial deployment measured on won work or margin.
BAM Ireland
Ireland
Autodesk Construction IQ, a machine learning layer over BIM 360 issue and document data, ranking site quality and safety issues by predicted risk so supervisors work the highest-risk items first.
BAM Ireland reported a 20% improvement in on-site quality and safety, and a 25% increase in the share of project staff time spent on high-risk issues, on projects generating 10,000 to 15,000 documents per building.
What it does not prove: Figures are BAM Ireland's own, reported through its software vendor's programme, and are not independently audited. BAM's own account notes the first thing the system surfaced was inconsistent issue closure in BAM's own records rather than genuine site risk, so part of the gain is better data discipline rather than better prediction.
Google DeepMind and Trane Technologies
United States, two commercial facilities (not named in the paper)
A reinforcement learning agent taking over control of live commercial cooling plant (chillers, pumps and towers) from the incumbent rule-based building control system, run as live experiments on two real facilities.
Energy savings of approximately 9% and 13% at the two live experiment sites, measured against each site's existing controller.
What it does not prove: Two sites only, and the paper is authored by the technology providers rather than an independent evaluator. Savings are relative to whatever control strategy each building already ran, so a well-tuned incumbent would leave less headroom. The facilities are not identified, which limits what a reader can check independently.
Zillow Group
United States
Zillow Offers, an instant-buying business in which an automated valuation model forecast future selling prices and set the price Zillow paid for homes it bought, renovated and resold.
Zillow announced on 2 November 2021 that it would wind the business down, after a $304 million inventory write-down in the third quarter, a further $240 million to $265 million expected in the fourth quarter, and a workforce reduction of approximately 25%. Chief executive Rich Barton said the company had determined "the unpredictability in forecasting home prices far exceeds what we anticipated".
What it does not prove: The wind-down was driven by renovation and resale capacity constraints and an extreme housing market as well as by model error, so it is not purely a failure of the valuation model. It remains the clearest published case of a property business retiring a machine learning pricing model because the error was too expensive to carry on the balance sheet.
Australian Bureau of Statistics
Australia
National business survey (Characteristics of Australian Business) measuring whether businesses used artificial intelligence in the workplace, published broken down by industry division.
6% of Australian construction businesses reported using AI in 2024-25, against an all-business rate of 12% and 38% for information media and telecommunications. Construction ranked 14th of the 17 industry divisions published.
What it does not prove: Adoption is not outcome. The survey records whether a business used AI at all, not whether it gained anything, and it captures general-purpose assistants alongside production systems. It is useful as a base rate for how early the sector actually is, not as evidence for or against value.
Australian Bureau of Statistics, Characteristics of Australian Business, 2026
Sector figures are useful for deciding what to try first, and almost useless for forecasting your own result. The organisations above had the data, the systems access and the governance in place before they measured anything. The full global picture covers 13 industries, sets out what separated the organisations that got a result from the ones that got nothing, and carries the randomised trials that found no gain at all.
Use cases
What we build in this sector.
Each is tagged with the disciplines involved, because most useful work crosses more than one.
Tender document intelligence
Extract scope, exclusions, programme dates, liquidated damages, insurance levels and unusual contract clauses from a tender set into a structured, reviewable summary.
- AI
- Automation
Addenda and revision tracking
Compare document issues automatically, surface every substantive change and map it to the affected trade packages and quotes already in the market.
- AI
- Automation
Subcontractor quote comparison
Normalise quotes arriving as PDFs, spreadsheets and email text into a common scope structure, with unmapped items escalated rather than guessed.
- AI
- Data
Project cost and programme reporting
Join project management, finance and site data into one governed model so committed cost, claimed value and forecast reconcile.
- Data
- Cloud
Secure project knowledge search
Permission-aware retrieval across drawings, specifications, RFIs, variations and site diaries, answering with citations back to the source document.
- AI
- Security
Variation and claim readiness
Track instructions, RFIs and site records against the contract so variations are identified and substantiated while the evidence is still fresh.
- Data
- Automation
Legacy estimating platform modernisation
Move ageing on-premises estimating and project systems onto a supported cloud platform without disturbing the estimating team mid-tender.
- Cloud
- Security
Human oversight
Where a person still decides.
Automation proposes. A named person approves. These are the points we design the workflow to stop at.
- The estimator approves the extracted scope summary before any price is built against it. Extraction is a starting point, never a priced position.
- The contract administrator confirms every flagged addendum change before it is issued to subcontractors as a scope revision.
- A quantity surveyor signs off any automated quantity or rate comparison before it informs a submitted tender price.
- The project manager approves each identified variation before it is submitted to the principal or superintendent.
- A director approves the final tender submission. No automated step may submit a bid.
Delivery options
On Azure, on AWS, and afterwards.
The platform is chosen for the workload, not for us. Both paths end in the same place: someone still owns it after go-live.
- 01
Delivered on Microsoft Azure
Suits builders and developers already standardised on Microsoft 365, where tender documents live in SharePoint and the estimating team works in Excel and Teams every day.
- Azure AI Document Intelligence extracts structure from tender PDFs, drawings registers and subcontractor quotes.
- Azure AI Search indexes project documents with security trimming that inherits existing SharePoint permissions.
- Microsoft Foundry hosts the extraction and comparison workflows, with evaluation runs against a held-back set of past tenders.
- Microsoft Fabric consolidates project management, finance and site data into a governed reporting model surfaced through Power BI.
- Power Automate routes flagged addenda changes and variation candidates to the responsible person for approval.
- Microsoft Purview classifies commercially sensitive tender material and tracks where it travels.
- 02
Delivered on AWS
Suits contractors with existing AWS workloads, custom project platforms, or a preference for keeping estimating tools independent of the Microsoft stack.
- Amazon Textract extracts text, tables and form data from tender documents and scanned subcontractor quotes.
- Amazon Bedrock runs scope extraction and addenda comparison, with Bedrock Guardrails constraining output to the source documents.
- Amazon OpenSearch Service provides project knowledge search with document-level access control.
- AWS Glue and Amazon Redshift build the cost and programme reporting model, presented through Amazon Quick.
- AWS Step Functions orchestrates the multi-stage tender pipeline so each document set is processed reproducibly.
- Amazon S3 with object lock retains tender submissions and issued documents as an evidentiary record.
- 03
Operated after handover
Tender volume is seasonal and document formats change without warning. The system needs someone watching it between projects, not just during the build.
- Extraction accuracy monitored against a reviewed sample each month, with drift reported before it affects a live tender.
- Document parsing failures triaged and corrected, including new formats introduced by a consultant or principal.
- Search index freshness and permission synchronisation verified as project teams and access change.
- Cloud cost tracked per project so the platform's running cost can be attributed to jobs.
- Reporting model reconciled against finance at each period close.
- Quarterly review of which extractions are being overridden by estimators, feeding the next round of improvement.
Related services
The capabilities behind this work.
Artificial Intelligence
AI that is chosen for a reason, costed before it is built and governed once it is live.
Data and Analytics
Reporting your executives trust, produced once, with a modelled layer underneath it that the next AI or forecasting project can stand on.
Cloud Modernisation
Ageing systems become a cloud platform your team can change safely, recover predictably and account for line by line.
Managed Services
Your platform keeps earning its business case after go-live, with cost, security posture, reliability and adoption reviewed on an agreed cycle rather than left to drift.
Free discovery workshop
Start with one construction challenge.
Bring a process that costs more than it should. We will map the opportunity, the readiness gaps and a recommended next step.