Built for small and mid-size AEC teams who face enterprise-grade sustainability pressures without enterprise budgets, staff, or time.
Embodied carbon calculations that should take minutes consume days of manual spreadsheet work, often done too late to influence design decisions.
Site defect triage relies on handwritten notes, photographs filed in email threads, and engineer memory — inconsistent and hard to audit.
The tools that do exist cost six figures and require multi-month onboarding. A 12-person structural consultancy can't absorb that overhead.
BIM models, site photos, climate datasets, and sensor feeds live in disconnected silos. Nobody has time to join them up before the client meeting.
Moonshop gives SME AEC teams access to three specialist AI agents — each designed to answer a specific class of engineering question, quickly, reproducibly, and without a PhD in data science.
Ingests IFC geometry and material data, benchmarks embodied carbon against databases, and suggests lower-carbon structural alternatives — with quantified savings per swap.
Processes site photographs with computer vision to detect cracks, spalling, and moisture ingress. Triages defects by severity and links findings to model elements with full context.
Overlays climate projection scenarios onto your building model. Scores flood, heat, and wind exposure. Recommends design adaptations with engineering-grade rationale.
Consultancies of 5–50 engineers who need sustainability analysis to be a workflow step, not a project milestone.
Design-led practices who need carbon and resilience data early enough to inform material and form decisions.
Main contractors managing site quality assurance who need consistent, documented defect records without adding admin overhead.
Asset owners who need a credible view of their portfolio's climate exposure and embodied carbon baseline for reporting and investment decisions.
Upload an IFC, get a full carbon analysis with material breakdown and reduction opportunities — before your next client call, not your next project phase.
Identify high-impact swap opportunities early, when design is still fluid. Quantified per-material savings so recommendations are actionable, not aspirational.
Computer vision triages site photographs by severity, links to model elements, and generates structured defect reports — eliminating the manual review backlog.
Climate scenario analysis mapped to your specific building model, with design adaptation recommendations grounded in climate projection data.
Every run produces the same structured outputs — dashboards, issue lists, PDF exports, share links — so reporting becomes a process, not a bespoke task.
Multi-tenant architecture with clear data isolation. Your models and analyses stay yours — no training on client data, no cross-account visibility.
AI in engineering carries genuine responsibility. Moonshop is designed for professionals who need to stand behind their outputs — so we've built the trust architecture in from the start, not bolted it on after.
Every output includes explicit confidence scores and the assumptions driving them. No unexplained numbers.
Every agent run records the inputs, parameters, and data sources used — reproducible and auditable months later.
Strict data isolation between companies. Your BIM models and analyses are never visible to other users or used for training.
Agent schemas are decoupled from any single AI provider. As better models emerge, outputs stay structured and consistent.
Moonshop was founded by someone who spent years on the tools — delivering structural projects, writing carbon reports by hand, and triaging site defects from email photo threads. The frustration is real. The solution is designed from the inside.
We're inviting a small cohort of SME AEC teams to shape the product from the ground up. Early partners get direct access to the founding team, priority feature input, and founding-tier pricing locked in permanently.