Fixed-fee work is squeezed. Generative design + AI compliance is how A/E firms protect margin.
A/E firms hold the licence — competitors can't sign drawings. But fixed-fee contracts squeeze margin every year. AI accelerates design iteration 3-5x, automates jurisdictional code-compliance, and surfaces clash detection earlier. The licence-holder still signs. They just get to higher-value reviews faster.
Performance benchmarks
How AI-augmented A/E firms compare on the metrics that determine project profitability.
What leading firms are doing
Industry examples from publicly reported initiatives — not Xamun projects.
- ✓ Test thousands of massing options in minutes
- ✓ Wind, daylight, noise simulation built-in
- ✓ Adopted by Skanska, Sweco, AECOM
- ✓ Auto-check IBC and jurisdictional codes
- ✓ Parametric component generation
- ✓ Cuts plan-check rework dramatically
- ✓ Bespoke generative tools per project type
- ✓ AI-augmented structural and MEP design
- ✓ Differentiator on signature commissions
- ✓ Construction-aware design feedback
- ✓ Asset-lifecycle data integration
- ✓ Standard for civil and infrastructure firms
The transformation opportunity
What changes when a mid-market firm in this segment deploys AI across the engagement lifecycle.
- ✗ 3-5 design iterations per phase
- ✗ Manual code-compliance review
- ✗ Clash detection late, costly
- ✗ Project margin 10-15%, eroded by RFIs
- ✗ Senior PMs buried in admin and QC
- ✗ Repeat-client rate 55-65%
- ✓ 20+ generative iterations explored
- ✓ Auto code-check; PE reviews exceptions
- ✓ Clashes flagged pre-DD
- ✓ Project margin 25-30%, defensible
- ✓ Senior PMs on design strategy and client
- ✓ Repeat-client rate 90%+
The AI system stack
Designed to integrate with Revit, ArchiCAD, AutoCAD, Bentley, Rhino/Grasshopper, and your BIM 360 / Procore environment.
Massing, layout, and façade studies generated in your design language. PE/architect curates the strongest options. Speeds early-phase decision-making.
AI parses drawings against jurisdictional codes (IBC, NFPA, ADA, local). Flags violations and suggests remediations. PE signs after review.
Always-on review of MEP/structural coordination. Surfaces clashes and constructibility issues at SD, not at submittal.
Auto-drafted spec sections, RFI responses, and submittal logs from your firm's standards and project history.
Real-time engagement-economics tracking. Forecasts cost-to-complete. Flags scope creep before it kills margin.
AI-generated visualisations and progress narratives — for owner reviews, jurisdiction approvals, and community engagement.
We build it, run it with your team — and you pay per deliverable issued.
No upfront CAPEX — we invest the build ourselves. Once it’s live, you pay a small toll per drawing, submittal or deliverable issued the system processes: a small fraction of the value each one creates. A slow month means a small invoice; if it doesn’t run, you don’t pay.
We’re not a vendor shipping software into your operation. For the operation we run, we’re part of your company — in your numbers every week, paid only as they move. Where issued deliverables aren’t the honest measure, we price it as a fixed project instead; the accountability doesn’t change, only how you pay.
How Xamun delivers
From intelligence to production in weeks. Built as your IP, not someone else's SaaS lock-in.
Benchmarks your engagement-economics, leverage ratio, and outcome posture against AI-augmented leaders. Maps the highest-impact AI plays first.
AI co-pilots, outcome platforms, and proprietary knowledge engines — integrated with your existing systems. Owned by you. Migratable. Defensible IP.
Intelligence assessment in 2 weeks. First module live in 6-8 weeks. Full stack across one practice within 12 weeks. Outcomes tracked from Day 1.
Reposition before fixed-fee compresses your margin
Book a discovery call to map where generative design and AI compliance can lift project margin, accelerate iteration, and free your licensed staff for the work that requires their judgement. No upfront cost — you pay only when the system runs.
Book a Discovery →