Diagnosis
We map ICP, data sources, CRM, cadences and bottlenecks. We leave with a prioritized backlog.
We turn repetitive sales tasks into governable workflows: research, enrichment, writing, follow-up, scoring and CRM updates.
We turn repetitive sales tasks into governable workflows: research, enrichment, writing, follow-up, scoring and CRM updates.
We map ICP, data sources, CRM, cadences and bottlenecks. We leave with a prioritized backlog.
We connect tools, data and prompts into auditable workflows. First in sandbox, then with real accounts.
We train the team, measure replies, adjust criteria and leave documentation to operate independently.
We work with tools B2B teams can maintain: CRM, enrichment, outbound, automation and reporting with clear ownership for each system.
See details →Prospecting agent to identify accounts, write first touches and detect sales-ready leads.
Data, signals and personalization at scale without losing control of sources.
Account and contact research using ICP, territory, sector and role criteria.
Workflows across forms, CRM, enrichment, email and reporting without black boxes.
Pipeline, properties, scoring and reporting to measure what happens after the first touch.
Campaigns, domains, warm-up, deliverability and controlled follow-ups.
The promise circulating about AI in sales is always the same: automate prospecting, generate personalised messages at scale, close more. The problem is that this promise confuses productivity with effectiveness. You can automate a hundred mediocre messages as easily as ten. And at scale, the damage scales too: more noise, more contact base burnout, more rejection towards the brand.
What AI does well in sales is specific: it accelerates tasks you had already figured out, processes intent signals a human couldn't review in the available time, and reduces operational friction in repeatable, high-volume activities. What it doesn't do well is replace judgment about whether the ICP is right, whether the message fits the buyer's timing, or whether the channel makes sense for that specific segment. That judgment remains human.
When we work on applied AI for sales, the starting point isn't which tool can we use. It's where the real bottleneck is in the commercial process. Sometimes it's in lead qualification. Sometimes in following up open opportunities. Sometimes in meeting preparation or proposal generation. Each insertion point has a different logic, a different complexity level, and a different ROI.
The result of a well-designed process is a commercial team that uses AI to do more of what it already did well, with less time spent on low-value tasks — not a team that delegates to AI decisions it still hasn't made well. The difference between those two scenarios is enormous in terms of real results.
We work with teams that want to adopt AI in a practical and sustainable way: we configure flows, integrate tools into the existing stack, train the team on real use — not product demos —, and track whether adoption generates more useful conversations or just more measurable activity.
Tell us about your team, market and current tools. We will show where AI can create pipeline and where it would only add noise.
We will reply in less than 24 hours.
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