A client needed to find, qualify and reach customers without hiring a team. The platform does the heavy lifting, but nothing leaves the building without a person reading it first.
A growing B2B services firm had a pipeline problem. Their existing client base came entirely from referrals and networking; reliable, but not scalable. The founder knew the profile of their ideal customer intimately: industry, company size, recent funding events, hiring patterns. But turning that knowledge into a steady stream of qualified conversations meant hiring a prospecting team they could not yet afford, or spending hours each day on manual research that pulled them away from client work.
The ask was nuanced: automate the research and drafting, but keep the final judgement human. The client did not want a bot sending cold emails; they wanted a tool that surfaced the right people at the right time, wrote a thoughtful first draft, and waited for a person to press send.
The platform pulls from public company data sources; funding databases, job boards, news APIs; and scores each lead against the client's own criteria: sector match, growth signals, decision-maker seniority, and timing indicators. A language model drafts a short, personalised outreach note for each qualified lead, but the output is held in a review queue. Nothing is sent until a human reads the draft, edits it if needed, and approves it.
The interface is deliberately simple: a single-page dashboard showing the week's top prospects and a review panel for pending drafts. No dashboards full of vanity metrics, no AI agent pretending to be a person. The tool earns its keep by shrinking the research-and-draft phase from hours to minutes, while the human stays firmly in control of every conversation.
Everything runs serverless on AWS; Lambda for the scoring pipeline and draft generation, DynamoDB for lead storage, S3 for static assets, and CloudFront for delivery. Each client gets strict tenant isolation at the database level. Authentication goes through Cognito with no shared user pools. The architecture is deliberately boring: no Kubernetes cluster to nurse, no long-running services to patch, no infrastructure that needs a full-time operator.
Pricing is deterministic: the client pays a fixed amount per qualified lead surfaced, worked out from the underlying compute cost rather than guessed at from what the market might bear. No monthly minimums, no enterprise-tier bells and whistles nobody asked for. The whole thing shipped in under two weeks.
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