SponsorMatch AI

SponsorMatch AI transforms live event culture into high-ROI brand sponsorships using Qloo's Taste Graph—featuring non-obvious brand matches, custom activations, and 100% cited, auditable CMO pitches.

Comment

💡 Inspiration

Every year, over $70 billion is spent globally on live event sponsorships, yet a staggering percentage of those deals underperform. The root cause? Demographic guesswork.

Event organizers pitch brands using blunt, surface-level demographics: "Our festival reaches males aged 18–34." In response, CMOs receive dozens of nearly identical pitches proposing standard logo slap-ons and generic banner placements. The result is high sponsorship clutter, attendee indifference, and zero defensible ROI.

We asked: What if live sponsorships weren't based on demographic assumptions, but on mathematically verified cultural affinity?

Live events—whether it's an electronic music festival in Barcelona, an endurance road race in Chicago, or an indie desert gathering—possess rich, authentic subcultures. Qloo's Taste Graph is the only cultural intelligence engine capable of traversing the connections between artists, geography, venues, and brand consumption. We built SponsorMatch AI to turn raw event culture into high-converting, defensible B2B partnership proposals backed by 100% verified evidence.

⚡ What It Does

SponsorMatch AI is an autonomous sponsorship intelligence platform designed for festival organizers, sports properties, and brand partnership teams.

  1. Autonomous Cultural Discovery: Takes any event's name, category, lineup, and location, and uses Qloo to traverse the taste graph across music, fashion, beverage, wellness, and technology.
  2. "Non-Obvious Gems" vs. "Category Giants": Surfaces both high-confidence legacy partners and non-obvious challenger brands (e.g., pairing Sónar Barcelona with Teenage Engineering at a 1.3x cultural lift, or the Chicago Marathon with Maurten Hydrogel at a 1.67x cultural lift).
  3. Evidence-Grounded CMO Pitches: Generates complete B2B proposals featuring verbatim Qloo affinity citations ([1], [2]), tailored on-site experiential activations (e.g., modular artist pavilions, recovery lounges), and calculated tier pricing based on reach and lift multipliers.
  4. "With vs. Without Qloo" Comparative Benchmark (Feature F8): A built-in judging differentiator demonstrating side-by-side why a plain LLM hallucinates ungrounded demographic tropes ("68% drink energy drinks"), while SponsorMatch achieves 100% empirical grounding through Qloo.
  5. Auditable Evidence Trace: Every metric, lift calculation, and pitch claim links to a permanent query record displaying redacted parameters, response snippets, and API provenance.
  6. Closed-Loop Feedback & Exclusivity (Feature F10): Organizers can exclude brands with category conflicts or exclusivity locks. The platform instantly re-ranks candidates on cached taste data with zero additional API quota consumption.
  7. Honest Abstention Engine: If an event has insufficient verified cultural signals, the agent visibly and honestly abstains rather than inventing fake recommendations.

🛠️ How We Built It

SponsorMatch AI was engineered from the ground up as a production-grade, enterprise B2B platform:

  • Backend & Pipeline (Node.js, TypeScript, Express):
    • Built a 7-stage streaming pipeline using Server-Sent Events (SSE) for real-time stage tracking (Planning ➔ Resolving ➔ Profiling ➔ Discovering ➔ Ranking ➔ Validating ➔ Writing ➔ Auditing).
    • Implemented an intelligent Qloo API Proxy with strict credential isolation (QLOO_API_KEY never leaks to the client), request deduplication, and an in-memory cache to respect rate limits.
    • Formulated a normalized Cultural Lift Index Formula: $$\text{Lift Index} = \frac{\text{Raw Qloo Affinity}}{0.35 + 0.65 \times \text{Qloo Popularity}}$$ This isolates true subcultural affinity from mere global brand ubiquity.
  • Citation Auditor Agent:
    • Runs an automated validation pass over every generated sentence, verifying that every cited statistic maps back to a logged Qloo query ID.
  • Frontend UI/UX (React 18, TypeScript, Tailwind CSS, Lucide):
    • Designed an ultra-fast, responsive dashboard featuring pre-warmed instant events, interactive affinity bar charts, deal valuation simulators, slide-out Evidence Trace drawers, and Markdown/PDF pitch deck export.

🧗 Challenges We Ran Into

  1. Normalizing Affinity vs. Ubiquity: A massive global brand (like Nike or Coca-Cola) naturally has high baseline recognition everywhere. Our initial tests favored obvious category giants. We solved this by developing our Cultural Lift Index, which compares event affinity against general population baseline, allowing high-affinity challenger brands (like On Running, WHOOP, and Maurten) to shine.
  2. Zero-Hallucination Guardrails: Generative models naturally love to invent believable-sounding statistics (e.g., "3.4x average conversion rate"). We designed a strict citation regex and verification auditor that scans every pitch sentence. If a number cannot be traced to a Qloo response, it is flagged and discarded.
  3. Handling Rate Limits & Quota Conservation: We pre-planned entity resolution batches and designed a zero-cost closed-loop feedback engine, allowing users to reject candidates and re-rank on cached taste graphs without re-querying the API.
  4. Production TypeScript & Bundling: Overcoming strict production compiler checks on Render (TS7026, JSX Intrinsic Elements, devDependency handling) required fine-tuning our monorepo build scripts and dependency configurations.

🏆 Accomplishments That We're Proud Of

  • 100% Citation Grounding Rate: Across all sample events and live analyses, 100% of numerical affinity claims cite auditable Qloo data points.
  • The "With vs. Without Qloo" Differentiator: An interactive, transparent demonstration proving that SponsorMatch cannot function without Qloo—turning a hackathon judging requirement into a hero product feature.
  • Sub-Second Pre-Warmed Caching: Delivering instant (<300ms) multi-domain sponsor analysis across music, sports, and festival environments.
  • Honest Abstention: Building an AI agent disciplined enough to declare "Insufficient Signal" when presented with nonsensical inputs, rather than guessing.

📚 What We Learned

  • Taste is more predictive than age or gender: Two 30-year-olds can have completely diametrical lifestyles, but their shared affinity for specific musicians, venues, and culinary spots reveals precise commercial resonance.
  • Qloo as the Missing Grounding Layer: LLMs are exceptional at drafting persuasive B2B narrative copy, but completely untrustworthy with facts and metrics. Coupling Qloo's Taste Graph with an LLM writer produces the ideal balance: mathematically grounded data presented in compelling executive language.

🚀 What's Next for SponsorMatch AI

  1. Direct Ticketing Platform Integrations: Connect directly with Eventbrite, Ticketmaster, and DICE APIs to pull anonymized attendee music/taste graphs in real time.
  2. Automated CRM Push: One-click sync to HubSpot and Salesforce to generate ready-to-send outbound CMO sequences and sponsorship decks.
  3. Post-Event Proof of Performance: Measure post-event social sentiment and attendee brand recall to validate and calibrate Qloo's pre-event lift predictions.
  4. Self-Service Brand Portal: Allow brand managers to submit their target taste profiles and be automatically alerted when an event with matching cultural alignment opens sponsorship inventory.

Built With

Share this project:

Updates

Submission history