An AI sales team that never stops researching.
A confidential engagement building a multi-agent AI system that researches prospects, qualifies leads and prepares personalized outreach — so a sales team spends its time on conversations, not busywork.
Confidential client engagement. Client details are withheld.
The challenge
The client's sales team was losing hours every week to manual prospect research, decision-maker lookup and outreach prep — time that should have gone to actual selling conversations.
What we built
A coordinated system of AI agents, each responsible for one stage of the pipeline: researching a prospect and their company, identifying likely decision-makers, scoring the lead against the client's ideal customer profile, drafting a personalized outreach angle, monitoring replies, and recommending the next follow-up. A rolling summary keeps sales leadership oriented on pipeline activity without manually compiling it.
How it's built
AI & automation capabilities
Key features
01 · Research
An agent dedicated to prospect research
Before any outreach is drafted, a research agent builds a real picture of the prospect and their company, then identifies who's actually likely to be the decision-maker.
02 · Qualification
Leads scored against a real ICP
Every prospect is scored against the client's own ideal customer profile, so the sales team's attention goes to the leads most likely to convert, not just the newest ones.
03 · Outreach
Personalized, not templated
Outreach drafts are grounded in the specific prospect and company research the system already gathered — never a generic mail-merge pass.
04 · Follow-up
Replies monitored, follow-ups recommended
The system watches for replies and recommends the next follow-up based on what actually happened in the conversation, with a rolling summary for sales leadership.
Stack
What the client said
“The project involved a lot more than simply connecting an AI model to a workflow. MetaBuf took the time to understand how our sales team actually worked and where automation would genuinely save time. They were thoughtful about the handoffs between different parts of the system and focused on making the technology useful rather than adding AI for the sake of it.”
Tell us what you're building.
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