Process automation

City of Riviera Beach · Conflation Labs

Vendor-reportedPublic sectorUnited StatesPilotPublic sectorLLM

Vendor-reported. The customer is named and the numbers are quoted from the source page, but the account comes from the vendor. No independent confirmation.

Reported by conflationlabs.com (vendor self-report). Checked against the source page on 2026-09-05. 4 of 4 figures below appear on that page word for word.

The problem

The Riviera Beach Planning Department faced a relentless volume of routine inquiries, consuming 75% of staff's day and creating a bottleneck for high-value tasks.

What was deployed

Vendor
Conflation Labs
Products
AI assistant, Spatial AI Site Plan & Zoning Agent, Conflation Labs platform
Technique
LLM
Build or buy
Bought off the shelf
Deployment
SaaS
Data used
city's authoritative data—including ArcGIS layers, Chapter 31 Zoning Codes, and the Comprehensive Plan

What changed

Each row is quoted from the source. Figures we could not find on the page in those words are marked — they are kept, not deleted, so you can judge them.

routine inquiries

How AI reduced routine inquiries by 75%

Quoted word for word from the source · round percentage, no baseline given

20 hoursplanner time savedper week

saved planners 20 hours per week

Quoted word for word from the source

30 out of 40 answered by AIinquiries automatedfrom 40 daily questionsdaily

Of the 40 daily questions received by the department, the AI successfully answered 30 out of 40, requiring zero staff intervention.

Quoted word for word from the source

5 minutesmanual review timefrom 20 hours

Complex manual reviews that previously took a planner 20 hours to complete were reduced to just 5 minutes of AI processing and human verification.

Quoted word for word from the source

increasedaccuracy

The AI identified "catch-all" requirements in the municipal code that are often overlooked during manual human reviews.

Quoted word for word from the source

The benefit is that GIS and Planning staff get bogged down with low-effort work. This LLM-powered map frees up our resources so we can focus on the important work like the City's Strategic Plan.
Juan Suarez, Senior Planner, City of Riviera Beach

Difficulties and limits

Sources

Similar deployments

Shriram Finance

less than 3–4 hours — Onboarding Turn-Around-Time (TAT)

Independently reportedBanking & finance · Process automation · Classical ML · In production

GE Aerospace

Independently reportedManufacturing · Process automation · Classical ML · Scaled

Apollo Tyres

under 10 minutes — Time for root cause analysis

Independently reportedManufacturing · Process automation · AI agents · In production

Broadcom · Moveworks

88% — autonomous IT resolution rate

Independently reportedTechnology & software · Process automation · NLP · Scaled

Amazon · Amazon

10% more — shopper spend

Independently reportedRetail & e-commerce · Process automation · Computer vision · Scaled · 2024

icare · SAS

increased by 15% — accuracy

Independently reportedInsurance · Process automation · Classical ML · Scaled · 2019