# Outrizz AI: Event Engagement Platform

Fill the booth with the right people, then follow up fast.

An event marketing platform for B2B sales teams. It finds the right people before a trade show and talks them into coming, then captures everyone who stops at the booth and follows them up while the event is still running.

## Tags

Next.js, React Native, AI, tRPC, Turbo Monorepo, Drizzle, Supabase

![Outrizz AI dashboard showing sourced leads and AI conversation history](https://lxbpjvrr41.ufs.sh/f/6KZjuRTQYJxHVfRwEIUsIS2Yw6ONg4BKAnphZLJcMrHvQ3VC)

## At a glance

- **Role:** Lead full-stack engineer
- **Timeline:** Oct 2024 - Apr 2026
- **Team:** Built with the Outrizz team
- **Scope:** Web app, mobile app, AI pipelines

## Outcomes

- **30K+** — leads generated
- **25K+** — monthly interactions
- **+35%** — targetable leads
- **+15%** — conversion
- **-15%** — outreach cost

Measured across 5 campaigns run on the platform.

[Outrizz AI](https://www.outrizz.com) runs one pipeline in two phases. Before the event, agents source, qualify and talk to prospects to drive attendance. During it, the booth team captures visitors on a phone and follow-up goes out within hours. Built together with the Outrizz team.

### Before the event

- Sourcing from LinkedIn, industry databases and company sites
- ICP scoring and intent signals on every prospect
- Agents run the outreach conversation
- Meetings booked into the calendar

### At the booth

- Business cards scanned on a phone, contact data extracted
- Conversations recorded and transcribed into notes
- Booth context merged with the pre-event profile
- Follow-up across email, LinkedIn and WhatsApp

## Sourcing

The pipeline collects from LinkedIn, industry databases, company sites and other public sources, then enriches each contact with company data and verifies it. Records refresh continuously, because half the value of an event list is that it is still correct on the day.

Qualification runs on top: an LLM scores fit against the client's ideal customer profile, reads behaviour for buying signals and attendance probability, and ranks the list. Clients tune the scoring on their own historical conversions.

[Data sourcing dashboard showing user selecting relevant ICPs for their campaign and launching the sourcing process. (video)](https://qzqabhpckmtflhwxetkm.supabase.co/storage/v1/object/public/outrizz-dev/website/source-participants.mp4)

## Outreach agents

Messages are written per prospect from their profile, their company, and why this event is relevant to them. Timing is coordinated across email and LinkedIn so the same person is not hit on both channels at once. Agents handle common objections in the thread and book the meeting when the prospect agrees.

## Capture at the booth

The mobile app takes a photo of a card and pulls out the contact fields, or takes voice notes, or plain manual entry when the light is bad. Each new record is enriched and checked against existing prospects so a lead sourced two weeks ago does not become a second contact.

[Mobile app interface showing business card scanning with AI text extraction and contact form auto-population. (video)](https://qzqabhpckmtflhwxetkm.supabase.co/storage/v1/object/public/outrizz-dev/website/lead-capture.mp4)

Follow-up references the actual booth conversation, so the first message after the event reads like a continuation rather than a blast.

## Training agents on the client's data

Every client gets a sandbox. They train agents on past conversions, winning sales calls and their own messaging, A/B test agent configurations against each other, and watch engagement and conversion per variant. The brand voice and the sales playbook stay consistent across everything the agents send.

[AI training dashboard showing sandbox environments, performance metrics, and customization controls. (video)](https://qzqabhpckmtflhwxetkm.supabase.co/storage/v1/object/public/outrizz-dev/website/ai-speaks-your-language.mp4)

Different jobs get different models. Sourcing, qualification, conversation and follow-up each route to the model that does that job best, with failover to another provider when one is unavailable.

## Architecture

A Turbo monorepo holds the web app, the mobile app and shared packages for AI agents, database, UI and validation, so business logic is written once. tRPC generates the client from the server, Drizzle generates types from the schema, and Zod validates every payload at runtime, including model output.

Data sits in Supabase Postgres. Mobile is React Native on Expo, with over-the-air updates, camera and audio access, and local persistence so the app keeps working when the venue wifi does not. Web is Next.js, installable as a PWA.

Campaigns run across multiple events and venues, every lead carries its event attribution, and teams work in the same account with role-based permissions. Data syncs both ways with the client's CRM, with the full interaction history attached.

![Analytics dashboard showing lead conversion funnel, engagement metrics, and event performance data](https://lxbpjvrr41.ufs.sh/f/6KZjuRTQYJxHO1ZSvWiuzp2aq3lYF1V6dfBC9LAThvnUbGDI)

## Stack

Next.js and React Native on the front, Supabase and Drizzle for data, tRPC between them, TypeScript strict everywhere. On the AI side: several LLM providers behind one interface for conversation and tool calling, a vision model for card OCR, and multi-language speech transcription for booth audio.

## Links

- Project page: https://andrey-markin.com/projects/outrizz-ai
- All projects: https://andrey-markin.com/projects.md
- Live site: https://www.outrizz.com
- Contact: https://andrey-markin.com/#contact