I just built my own multi-agent GTM research assistant!

(it finds the reason to reach out before it writes a single message)

Cold outreach usually fails on timing, not on wording. By the time you find out that a target company raised a round or hired a new data leader, the window has closed and your message reads like every other cold email in the inbox.

So the research has to happen before the writing, and it has to run across the whole target list at once.

Today, we’re building a system where you drop in target company names and three agents handle the rest.

Here’s how it works:

↳ Agent 1 searches recent news and pulls trigger events for each company

↳ Agent 2 finds people at those companies and enriches their career background

↳ Agent 3 joins the two and writes a ready-to-send message per contact

↳ Results come back ranked by how strong the trigger is

↳ The whole pipeline runs inside a Streamlit UI

The order matters more than the agent count. The writer agent runs last and receives the trigger event and the contact’s background as its input, so it never starts from a blank company name. If the news agent finds nothing recent for a company, there is nothing to write from, and that company drops down the ranking instead of producing a generic message.

Tech stack:

↳ Seltz as the data layer, running the news scope and the people scope over the same target list

↳ CrewAI to orchestrate the three agents in sequence

↳ Streamlit to host the interface

Here’s why I picked this stack:

Seltz maintains its own web index instead of wrapping a search engine, so the news scope and the people scope return structured results you can join on the company name. That join is the whole system. A trigger event with no contact attached is not actionable, and a contact with no trigger gives you nothing to say.

Get started here: https://seltz.ai

CrewAI keeps the handoffs explicit. Each agent receives the previous agent’s output as context instead of starting its own search from scratch, so the message writer already knows both what happened at the company and who it is writing to.

Streamlit keeps the target list, the pipeline run, and the final ranked output in one place, which makes the intermediate agent output easy to inspect when a message comes out wrong.

Find all the code and everything you need to run this app in the studio: https://lightning.ai/dailydoseofdatascience/templates/build-a-multi-agent-gtm-workflow…

I also wrote a comprehensive article that covers this entire idea and how to replicate this for your own use case in more detail.

The article is quoted below.

Seltz - Web Knowledge for AI Agents

Source: https://seltz.ai/ Seltz is used in applications where models need access to precise, factual, and up-to-date web information that cannot be reliably memorized during training. Large language models often struggle with exact figures, niche facts, and continuously changing data, relying instead on patterns learned from past exposure. Typical applications include systems that must reference current statistics, specifications, pricing, regulations, technical documentation, or time-sensitive information from the web. This includes AI assistants and agents that reason over live data, analytical tools that depend on accurate external facts, and knowledge systems where correctness and freshness matter more than general language understanding.