AI Estimation Software for a Fabrication Company
AI estimation software that turns historical drawings and costings into automated quote generation. Three phases: estimation engine, enquiry automation, and presentation layer.
Structural and general fabrication company, estimating and commercial teams
A mid-sized fabrication company in WA with 15 years of completed jobs, technical drawings, and related costings. The business handled structural steel, architectural metalwork, and general fabrication across commercial, industrial, and government projects.
The estimating team of four people prepared quotes manually. They pulled from experience, checked old jobs where they could remember them, and built estimates in spreadsheets. Historical drawings and costings sat in disconnected folders, email threads, and the heads of senior estimators.
What needed to change
Years of valuable estimating knowledge was locked away. The company had 15 years of drawings, quote documents, pricing sheets, and job-specific costing notes, but none of it was structured or searchable. Estimators relied on memory to recall similar past jobs.
Estimates took too long and varied too much. A first-draft estimate for a mid-complexity job took 2 to 3 days. Two estimators quoting similar work would produce noticeably different numbers because each had different historical reference points and assumptions.
The enquiry-to-estimate pipeline was entirely manual. Staff opened every email, sorted attachments, extracted drawing information, and transferred data into estimate templates by hand. As enquiry volume grew, the bottleneck was not fabrication capacity but the estimating queue.
Presentation was an afterthought. Estimates went out in inconsistent formats. Council submissions needed different detail than customer proposals. Tender responses required specific breakdowns. The team spent hours reformatting the same estimate for different audiences.
What we built
A three-phase <a href='/ai-solutions/ai-development-and-integration'>AI estimation software solution</a> that (1) used historical drawings and costings to generate informed estimates, (2) automated the flow from enquiry to draft estimate, and (3) produced presentation-ready output for customers, councils, and tender panels.
Historical Knowledge Layer
Past drawings, completed quote documents, pricing sheets, costing notes, and job-specific assumptions ingested, structured, and indexed. The system learns from previous jobs and uses relevant past work as context for new estimates. Built on <a href="/knowledge/rag-systems-explained">RAG architecture</a> for accurate retrieval.
AI Estimation Engine
Identifies similar past jobs, compares specifications and drawings, suggests likely cost components, and generates a recommended estimate structure. The estimator reviews, adjusts assumptions, and finalises. The AI supports the decision, it does not replace the estimator.
Enquiry Automation Layer
Captures enquiries from email, web forms, and uploaded documents. <a href="/ai-solutions/ai-automation-and-workflows">AI extracts key information</a> from customer drawings, specifications, and notes. Classifies job types, routes data into the estimation engine, and generates a draft estimate for review.
Presentation Layer
Takes the estimate output and shapes it into the right format for the audience. Customer-friendly proposals, detailed line-item breakdowns, council-ready submission formats, tender-ready estimate packs, and branded templates. One estimate, multiple outputs.
How it works
Enquiry arrives
Customer sends drawings, specs, or a brief via email, web form, or file upload. The system accepts PDF drawings, Word documents, spreadsheets, and scanned files.
AI extracts and classifies
The automation layer reads the enquiry, extracts key details from drawings and attachments, classifies the job type (structural, architectural, general), and identifies relevant specifications.
Historical jobs matched
The estimation engine searches indexed historical data for similar past jobs based on drawing characteristics, material types, complexity, and scope. Relevant costings and assumptions are retrieved.
Draft estimate generated
The AI generates a recommended estimate structure with suggested cost components based on historical matches and business rules. <a href="/knowledge/preventing-ai-hallucinations">Constrained to source data</a> so recommendations are grounded in real past work.
Estimator reviews and finalises
The estimator reviews the AI draft, adjusts assumptions, adds or removes line items, and applies commercial judgement. The tool accelerates the process but the estimator owns the final number.
Presentation output selected
The finalised estimate is formatted for the target audience. Customer proposal, council submission, tender pack, or internal approval document generated from the same underlying data.
Measurable outcomes
We had 15 years of good data sitting in folders nobody could search. Now the system finds similar past jobs in seconds and gives the estimators a solid starting point. We are quoting faster and more consistently, and the presentation packs look professional every time.
How we delivered it
Phase 1: AI Estimation Engine
6 weeksCollected and structured 15 years of historical drawings, estimate sheets, and costing data. Built the core estimation engine that matches new enquiries against historical jobs and generates draft estimates. Tested with the estimating team against real jobs to validate accuracy.
Phase 2: Enquiry Automation
5 weeksBuilt the automation layer that captures enquiries, extracts information from drawings and documents, classifies job types, and routes data into the estimation engine. Reduced manual handling between first enquiry and draft estimate. <a href="/guides/ai-agent-vs-workflow-automation">Workflow automation approach</a> chosen over a full agent model for reliability.
Phase 3: Presentation Layer
5 weeksCreated the output formatting system. Built templates for customer proposals, council submissions, tender packs, and internal approvals. Connected to the estimate data so one estimate produces multiple presentation formats without manual reformatting.
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