# Perplexity API Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/perplexity-api
> Markdown URL: https://aitinkerers.org/technologies/perplexity-api.md
> Technology record last updated: 2026-03-12T03:35:57Z
> Generated: 2026-09-22T02:44:01Z

Integrate a real-time, web-grounded LLM (Large Language Model) into your application, delivering current, cited answers and search results.

The Perplexity API grants programmatic access to its search infrastructure and Grounded LLM models, including `sonar-pro` and `sonar-small-online`. Unlike static LLMs, this system executes real-time online searches, ensuring all generated responses are current, factually grounded, and include verifiable citations. Developers utilize the OpenAI-compatible chat completions endpoint or the dedicated Search API to embed unparalleled, web-wide research and Q&A capabilities directly into their products.

- Official technology site: https://www.perplexity.ai/api
- Public AI Tinkerers demos and talks: 3
- Result page: 1 of 1

## Recent Public Talks and Demos

### [From Cold Email to Trusted Conversation: How AI Agents Turn Outreach into Revenue](https://seattle.aitinkerers.org/talks/rsvp_Z5K4dyJQ7rE)

This talk covers the architecture of an AI-driven outbound system designed to create highly personalized B2B outreach at scale. I will walk through the design of two agent systems — IRIS, a hyper-personalized email generation engine, and Hermes, a social-channel warming system that leverages the psychological mere exposure effect to increase response rates. IRIS uses Firecrawl to extract structured information from target company websites, Perplexity for real-time research enrichment, and an LLM pipeline that synthesizes this context into personalized outreach messages rendered through React Email templates. The result is emails that read as if they were written by a human founder after researching the company. Hermes operates as a GUI-driven agent that manages warm-up sequences on social platforms, creating lightweight touchpoints before direct outreach. Together, these agents form a two-channel system designed to increase outbound effectiveness in trust-sensitive industries like healthcare. I will walk through the system architecture, data flow, and agent orchestration, including how contextual data is collected, synthesized, and translated into human-like messaging. I will also share early performance results showing how research-driven outreach can significantly outperform traditional cold email strategies.

- Event context: AI Tinkerers Seattle: GTM Track — March — 2026-03-26 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_Z5K4dyJQ7rE

### [AI JAMIE](https://seattle.aitinkerers.org/talks/rsvp_JTrn8OX6vvs)

A Collaborative Whiteboard transcription tool that runs ai agent tasks, offering realtime visual aids and conversation enhancements.

- Event context: AI Tinkerers Seattle - April Meetup — 2025-04-25 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_JTrn8OX6vvs

### [Building a 68,000+ Element Graph Database: Generative AI Meets Ground-Truth Vetting: A Technical Deep Dive](https://boston.aitinkerers.org/talks/rsvp_QnatrFsN4jk)

Abstract: Can we create a valid dataset with over 68,000 elements, built, cleansed, and vetted using generative AI and standard CPU logic? In this talk, I will share how this can be achieved by integrating local and API-based Large Language Models (LLMs) with a Neo4j graph database, anchored to ground truths from Wikidata. Additionally, this talk will briefly show how the IDE Cursor AI was used in a 'one shot' fashion to create the node scripts used for this project. What You’ll Learn: • Node.js Scripts for LLM Interactions: I’ll walk you through the Node.js scripts written in JavaScript that automate the solicitation of responses from LLMs. Specifically, we’ll see how the Llama 3.1 model, running locally with Ollama, was used to generate a set of idealized features for each job task. • Leveraging Perplexity API for Solution Matching: Discover how I utilized the Perplexity API to find products or solutions that match the idealized features generated. We’ll delve into the code that handles API requests and processes the responses. • Data Anchoring with Wikidata API: Learn how I ensured data quality by anchoring information to ground truths using the Wikidata API. I’ll show you how I matched solutions from Perplexity responses to Wikidata Entity IDs as a first pass for populating the Neo4j database. • Neo4j Graph Database Schema: I’ll provide an in-depth look at the Neo4j graph database schema, explaining how Product and Company nodes are connected. We’ll explore how content from Perplexity enriches the relationships in the graph. • Building the App with SvelteKit: See how the front-end application was built using SvelteKit. I’ll guide you through the codebase, highlighting how the app interacts with the Neo4j database to present data to users. Technical Challenges and Solutions: • Cost Reduction with Local LLMs: Understand how running the Llama 3.1 model locally with Ollama helped reduce costs associated with large-scale data generation. • Data Quality Assurance: Learn about the strategies employed to cleanse and vet the data, including anchoring to Wikidata and handling discrepancies between sources. • Integration of Multiple APIs: See how I managed the complexities of integrating responses from different APIs (Perplexity and Wikidata) into a cohesive dataset. Live Demonstration: • Code Walk-Through: I’ll live-demo sections of the code, explaining how each part contributes to the overall goal. This includes Node.js scripts for LLM interactions, API integrations, and SvelteKit components. • Interactive Exploration: Attendees will have the opportunity to see the app in action, exploring the dataset of 850 jobs and their associated tasks and solutions.

- Event context: AI Tinkerers Boston November Meetup — 2024-11-25 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_QnatrFsN4jk

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