95% less manual
research per lead.
How Bactolife replaced hours of website reading with a multi-agent pipeline that segments and enriches every lead automatically.
The problem.
The team was opening company websites one at a time, reading them, and deciding by hand whether each lead was even relevant.
A company's self-assigned industry category is not its business model.
LinkedIn Sales Navigator gives broad access to potential leads and no way to segment them for a niche. The variables that matter (business model, product focus, customer type) are missing or unreliable, and industry categories are self-assigned and often inaccurate. Two companies can look identical on LinkedIn and occupy completely different positions in the value chain.
Lead qualification was slow, manual and resource-intensive, which meant the size of the addressable market was set by how many hours the team could spend reading.
The prototype.
The question was whether the signals that matter could be extracted reliably from official company sources rather than from self-assigned categories, and whether the resulting segmentation would be good enough to act on. That the work could be automated was never in doubt.
It was. The prototype proved lead enrichment could replace hours of manual website research with a repeatable workflow.
In production.
An automated lead enrichment pipeline runs as a series of specialist agents, each with one clearly defined task, working in sequence: classify the company, find the official website, extract the content, analyse it.
All website data is loaded into one shared knowledge base the agents can query, a setup known as retrieval augmented generation. From there the system segments leads and enriches each one with ten highly specific niche attributes.
What arrives now is a qualified lead, where it used to be a name and a guess.
Change how
you operate.
Tell us where you’re stuck. If operational AI can change the outcome we’ll show you how, and if it can’t, we’ll say so.
Talk to us