From the Citizen Engagement & Services edition of November 7, 2026
Burlington, Ont. building department reports ~15% examiner time savings on relevant code sections with Archistar AI PreCheck for single detached homes, plus readiness scores rising toward 85% (vendor case study)
City of Burlington, Ontario Building Department / Archistar · Municipal building permitting and plan review · Burlington, Ontario, Canada
- Publisher
- Archistar blog (vendor case study)
- Original publication
- 2026-11-06
- Source retrieved
- 2026-11-08
What happened
Archistar's Nov. 6, 2026 case study says Burlington applied AI PreCheck to Ontario Building Code Part 9 low-density housing (single detached dwellings). City data visualized in Power BI showed the department returned applications in 9–10 days while applicants took 15–17 days to resubmit, so submission quality was the bottleneck. Chief Building Official Nick Anastasopoulos estimates about 15% savings on a two-hour process for the relevant OBC sections; the vendor's before/after table claims readiness scores climbing toward 85%+ from roughly 70% completeness. Timeline: three-month pilot completed June 2024; production November 2024; 15–20 applications re-run to validate against examiner judgment; ~20 new applications in 2025–2026 AI PreCheck testing. Examiners moved from checking every result to periodic spot checks; structural, mechanical and exit reviews stay with staff. Expansion toward ADUs, septic, garages and Part 3 is planned.
Why it matters
Permitting is in CE scope. This is one of few AI plan-review deployments with a named official's time-savings estimate and a staged trust-building method, useful as a comparator for U.S. AI intake and completeness tools.
Evidence and measured results
Vendor-published case study quoting the city's chief building official; small validation sets (15–20 and ~20 applications); no independent evaluation, error rates or cycle-count data.
Limitations and uncertainty
Vendor-authored; 15% is an estimate for specific code sections, not end-to-end permit time; the 85% readiness figure is a vendor before/after claim; Ontario Building Code context limits direct transfer to U.S. codes.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-11-07; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Incomplete permit submissions drive repeated review cycles; Burlington found applicants took 15–17 days to resubmit versus 9–10 days for city returns.
- Stakeholders
- Chief Building Official
- Plan examiners
- Design firms and applicants
- Council
- Discovery questions
- Where does permit cycle time accumulate today?
- Which code sections are prescriptive enough to automate?
- How will examiners validate AI flags?
- Value hypothesis
- Pre-checks may cut examiner time on prescriptive sections (about 15% claimed) and raise submission quality; unverified independently.
- Potential engagement
- A permit cycle-time and resubmission baseline plus a limited pre-check validation on one building type.
- Unsupported claims
- End-to-end permit time reduction
- Transfer to U.S. codes without re-validation
- The 85% readiness claim as independent fact
Pre-sales engineering
Role takeaway
- Fit
- Fits building departments with high volumes of simple residential permits under prescriptive code sections.
- Architecture and integration
- SaaS pre-check before staff review, feeding examiners flags for completeness and Part 9 compliance.
- Prerequisites
- Permit database for baseline
- Examiner-labeled validation files
- Digital drawing submissions
- Constraints
- Ontario Building Code-specific rules
- Small validation samples
- Interpretive reviews stay manual
- Security
- Confirm data residency, retention and access controls for applicant drawings on the vendor platform.
- Proof of value
- AI-versus-examiner agreement on sample files
- Resubmission cycles per permit
- Time saved on named code sections
Delivery
Role takeaway
- Work
- Stage AI pre-check adoption by code section with examiner validation and spot-check sampling.
- Dependencies
- Archistar platform
- Permit system data
- Examiner capacity for validation
- Ownership
- Building Department led by the Chief Building Official.
- Skills and adoption
- Hands-on examiner validation to build trust and address job-loss concerns.
- Governance checkpoints
- Validation gate per code area
- Spot-check sampling rate documented
- Council reporting on cycle times
- Acceptance criteria
- Examiner agreement threshold met before reducing checks
- Resubmission cycles fall versus baseline
- Spot-check findings logged
- Risks
- Over-trust in AI flags
- Vendor claims outpace data
- Code changes break rules
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
AI PreCheck runs completeness and Part 9 compliance checks before staff review; examiners spot-check outputs; city used Power BI on its permit database for the business case.
Governance
Who approves, reviews and stays accountable for outcomes?
Staged trust model (check every result, then every fifth, then periodic spot checks) keeps examiners accountable; life-safety reviews explicitly retained by staff.
Security and privacy
What data, permissions and controls need testing?
Applicant drawings are processed by a third-party SaaS platform; the case study does not describe data residency or retention.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Positioned as capacity redirection with no job losses; change management addressed examiner fear of job loss through hands-on validation.
Procurement
What should contracts, pricing and exit terms secure?
Pilot-to-adoption path (June 2024) and expansion plans; contract terms and cost not disclosed.
Operating model
Which teams own the service once it runs?
Shifts effort upstream to applicants and designers by raising submission quality; examiners reallocate saved time to interpretive reviews.
Publication history
Read preserved resource versions (JSON)Stable resource ID: burlington-on-archistar-ai-precheck-part9-15pct-20261106