Executive Summary

Eventric partnered with CloudGeometry to rapidly develop and validate an AI-powered Venue Comparison Engine designed to eliminate time-intensive manual workflows for tour managers. CloudGeometry delivered both a technical proof of concept and a functional demonstration platform—enabling Eventric to validate product viability, accelerate go-to-market readiness, and build a defensible AI feature set that differentiates them in the live event logistics space.

The Business Challenge

Tour Managers and touring professionals must continuously evaluate whether a venue can accommodate an artist’s technical rider—covering production requirements, power specs, stage dimensions, rigging constraints, backline availability, and more.

Historically, this process requires:

  • Manual review of unstructured rider documents and venue spec sheets

  • Spreadsheet comparisons and subjective interpretation

  • High risk of missed requirements and costly on-site surprises

Eventric identified an opportunity to transform this workflow into a scalable product feature—one that could improve planning speed, reduce operational risk, and support a new premium product offering. However, building an AI solution capable of parsing messy rider data and generating reliable comparisons posed significant complexity.

The Solution

CloudGeometry structured the engagement to help Eventric move from concept to market-ready demo in two fast-moving phases—focusing on business validation first, then user-ready delivery.

Phase 1: AI Proof of Concept (PoC)

Duration: 5–7 weeks
Goal: Prove AI-driven ingestion, attribute extraction, and scoring reliability—fast.

To accelerate time-to-value, CloudGeometry leveraged Langbuilder, its internal AI development framework designed for rapid prototype delivery. Langbuilder enabled the team to integrate LLM-based parsing and structured extraction quickly without building custom orchestration from scratch.

Technology Used (CG Stack Highlights)

  • Langbuilder for prompt chaining, structured extraction, fallback logic

  • Selective LLM parsing to extract operational attributes from unstructured rider notes

  • Python ingestion + transformation pipeline with configurable CSV import rules

  • Normalization + scoring engine to produce RYG (Red/Yellow/Green) match ratings

  • Structured output generation for review and validation (CSV + JSON)

PoC Deliverables

  • Rider & venue ingestion pipeline (manual upload based)

  • AI-assisted parsing of venue specs and rider requirements

  • Rules + normalization engine to create consistent comparisons

  • RYG scoring and mismatch detection across 10+ critical attributes

  • Internal Git-based codebase and documentation for handoff

Outcome: Eventric validated that AI could automate a historically manual workflow and reliably surface mismatches that impact touring operations and cost.

Phase 2: Functional Demonstration Platform

Duration: 6–8 weeks
Goal: Deliver a demo-ready platform that replicates real tour workflows and supports internal pilots.

CloudGeometry built an interactive web-based experience enabling Tour Managers to upload data, compare venues, and generate exportable reports with actionable insights.

Technology Used (CG Stack Highlights)

  • API integration layer to connect with Eventric’s backend systems

  • Web application dashboard for visualization and workflow usability

  • AWS deployment environment for demo stability and stakeholder access

  • Role-based authentication for controlled access

  • Export engine supporting CSV + PDF output formats

Key Platform Features

  • Venue comparison dashboards with RYG summaries

  • Task tracking + mismatch resolution workflows

  • Exportable reports for tour planning and venue coordination

  • API scaffolding for production extensions and scaling

Deliverables

  • Deployed demo environment on AWS

  • Full UI + backend integrated application

  • API scaffolding for production buildout

  • Documentation and onboarding materials

Outcome: Eventric received a demo platform suitable for internal pilot testing, stakeholder reviews, and investor showcases—with architecture aligned for future production scale.

CloudGeometry Delivery Approach

CloudGeometry deployed an RMST model that aligned expert capability to each phase—without requiring long-term staffing commitments.

The result was fast iteration, milestone transparency, and predictable delivery within fixed investment constraints.

Results (Business-Oriented Outcomes)

Speed to Market

  • Delivered a working PoC in ~6 weeks

  • Delivered a production-quality demo application in ~8 weeks

Cost Predictability

  • Maintained fixed-budget delivery with clear scope and milestone controls

AI-Driven Operational Gains

  • Reduced manual effort by automating extraction and comparison of rider/venue data

  • Improved reliability of mismatch detection through scoring + normalized comparisons

Strategic Product Differentiation

  • Enabled Eventric to build a defensible AI capability for touring logistics

  • Positioned VCE as a premium feature and a growth lever for monetization

Business Impact

With CloudGeometry’s AI transformation delivery model and Langbuilder-based acceleration, Eventric reduced product development risk while moving rapidly from concept to a validated demonstration platform. The Venue Comparison Engine now positions Eventric to introduce a high-value AI feature into the touring ecosystem—improving touring operations, increasing product stickiness, and opening new commercial paths in live event logistics.

‍