Peec AI helps companies track and improve how they appear in AI search engines. The stack reveals a dual focus: a full-stack web application (TypeScript, React, Tailwind) paired with real-time data infrastructure (RabbitMQ, Elasticsearch, PostgreSQL) to monitor AI model outputs (OpenAI, Claude, Perplexity, Gemini, Llama). Active hiring across engineering, marketing, and sales—with senior and leadership roles concentrated in those functions—suggests they're scaling GTM and product simultaneously, though hiring velocity is decelerating.
Tech Stack 71 technologies
Core StackTypeScript React Tailwind CSS RabbitMQ PostgreSQL Elasticsearch OpenAI n8n Zapier HubSpot Firebase Figma Terraform Pulumi CloudFormation Datadog Sentry Prometheus Grafana PagerDuty shadcn/ui Firestore Claude Perplexity Gemini Llama Apollo Clearbit LinkedIn CSS+37 more
What Peec AI Is Building
◆Challenges
- Bottlenecks
- Budgeting and forecasting
- Operational inefficiencies
- Scaling outbound campaigns
- Scaling team across functions
- Single points of failure
- Automating lead enrichment
- Scaling sales infrastructure
- Data hygiene issues
- Acquiring new customers
▲Active Projects
- Build and scale sales infrastructure
- Growth experiments across paid, organic, lifecycle channels
- Optimize onboarding and activation flows
- Ai search recommendation models
- Frontend architecture, component library, and design system
- Build gtm flywheel
- Aligning gtm goals
- Automated lead enrichment and scoring
- Tooling automation and monitoring
- Incident response practices
Hiring Activity
40 roles · 8 in 30d
Department
Seniority
Company intelligence
