AI agents

Scout: the whole pipeline

Scout’s agent is a modular pipeline with discrete steps: discover, enrich, qualify, expand and draft. Point it at an ideal customer profile and it works through those steps on its own, running 10+ cycles to hit a lead target.

Discovery searches configurable sources such as Product Hunt, Y Combinator, Crunchbase, GitHub, AngelList and Reddit communities. The agent deprioritizes the sources that perform badly and doubles down on the ones that work. Enrichment then finds the founders, CEOs and other decision-makers at each company, with their LinkedIn profiles and email addresses, using Playwright in stealth mode and the Serper API.

Every lead gets a score from 1 to 5 against the profile, based on industry, company stage, team size, budget, tech stack and founder type, and the agent explains its reasoning. The top-scoring leads seed a second wave of discovery: competitors, companies from the same accelerator cohorts and related players in niche verticals. Last, the agent drafts personalized cold emails from the user’s knowledge base of case studies, pricing documents and company profiles.

The models sit behind the OpenClaw gateway, which abstracts Claude and GPT so the provider can change. The user follows the agent in a Next.js app that updates live during a run, with a Kanban pipeline and an activity log of every agent action.

Bridddge: Claude Code on LinkedIn

In Bridddge, the agent is Claude Code. Bridddge’s built-in MCP server gives it 30+ tools, so it can search for prospects, analyze networks, write personalized outreach and manage campaigns from plain-language requests. Bridddge’s own automation engine runs next to it, with human-like delays of 20 to 45 seconds and daily rate limits.