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Case Study·ChatbaseA signal led outbound motion that listens to who is already engaging, then reaches them across every channel.
Client snapshot
Headline result
LinkedIn engagement turns into pipeline
Their team turns around feature requests in days, replies instantly on Slack, and constantly suggests new things we should be doing. They even get upset when we don't update our campaigns often enough. That's the level of service you get when you work with hungry founders building for you as their exact ICP. Why would I ever consider any other GTM tool when there's an entire team obsessed with getting us more leads?
The client
Chatbase is the AI customer support platform used by more than 10,000 businesses to build and deploy agents that resolve complex support queries, automate service workflows, and lift resolution rates across web chat, WhatsApp, Slack, and email.
Its strongest fit sits with SaaS and fintech support teams carrying high, repetitive ticket volume. Chatbase's growth team runs outbound seriously and wanted a single system to do it, rather than stitching together a separate tool for email, LinkedIn, social listening, and enrichment.
The challenge
Cold lists were never the problem. The problem was relevance. Chatbase needed to reach support and CX decision makers at the exact moment they were thinking about AI support, not weeks later from a static list.
The buyers worth talking to were already active on LinkedIn, commenting on posts about support automation, AI agents, ticket deflection, and the tools in the category. That intent was visible in public, but it was scattered across the feed and impossible to action by hand. The brief was to capture that engagement and convert it into conversations before it went cold.
Our solution
We built Chatbase a signal led outbound engine on BrandJet, where the listening layer feeds the outreach layer directly. Instead of starting from a purchased list, the system starts from who is already engaging.
We tracked relevant LinkedIn posts and watchlisted topics across the category, then automatically captured the people commenting and reacting. These are buyers actively signalling interest in AI support and automation.
Each captured commenter was enriched, qualified against the Chatbase ICP, and added straight into a multichannel sequence. No manual list building or copying between tools.
Engaged prospects were reached on the same channel where they showed intent, with context from the post they had just commented on, giving every touch a warm, relevant opening.
Email ran in parallel to reinforce reach and follow up at scale, written in direct, honest copy anchored on concrete support outcomes: ticket deflection, faster resolution, headcount neutral scaling.
Listening, sequencing, and inbox all lived in a single system, so the team could tighten the ICP, swap topics on the watchlist, and adjust messaging in days rather than weeks.
Why it worked
Cold list tools optimise for volume: bigger databases, better deliverability, more sends. They do not get smarter about which person to reach.
By triggering outreach on real engagement rather than on a schedule, every conversation Chatbase opened was with someone who had just demonstrated interest in the category in public. That changed the texture of the replies. Warmer openings, more relevant conversations, and a motion that compounds as more of the right posts are tracked.
Client outcomes
Chatbase consolidated a fragmented outbound stack into one signal led system and shifted the centre of its motion from cold lists to live buyer intent.
The strongest results came from running a tight ICP, a focused watchlist, and multichannel sequences triggered by engagement rather than schedule, reaching support and CX leaders at the moment they were already thinking about AI support.
Across the engagement, the signal led motion became the standout channel for Chatbase:
Outcome highlights
The engagement is active and ongoing, with the social signal layer carrying the warmest, highest intent conversations for Chatbase's sales team.
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