In pipeline generated through accounts surfaced by the AI lead engine
Lead conversion rate on prioritized, high-propensity accounts
Across third-party signals like job postings, tech stack changes, funding events, press releases, and earnings calls
A global leader in consumer electronics needed a faster, more precise way to identify buying signals for its sales team. The signals existed across the market, in hiring surges, funding events, tech stack changes, press releases, and earnings calls, but they were scattered across disconnected third-party sources and unstructured content the company could not integrate or act on at scale.
Lead generation depended on resource-intensive manual identification of triggers and static, rules-based campaigns. The result was uneven precision, slow response to in-market accounts, and missed opportunity in segments the company was positioned to win.
OneSix designed and built an AI-powered lead generation engine tailored to the client's data landscape and sales motion. The system ingests third-party signals, including job postings, tech stack changes, and funding announcements, alongside unstructured sources such as press releases and earnings calls, then applies large language models to extract buying cues from text that previously required manual review.
A generative AI layer and unsupervised clustering algorithms group accounts displaying buyer-like behaviors, while continuous sales feedback refines the model's understanding of what a high-propensity account looks like. The output is a prioritized, ranked view of in-market accounts that the sales team can act on immediately, supported by account-level IQ Cards that surface the specific signals driving each recommendation.
In early production, the engine has generated $1.3M in pipeline at a 33% lead conversion rate, with strong adoption across the sales organization driven by model trust and measurable results.
More to explore
OneSix developed a machine learning platform for a food distributor, enabling restaurants to optimize ingredient ordering, reduce waste, and enhance profitability through data-driven inventory management.
.png)
OneSix built an edge-based AI computer vision system that detects shrink-wrap packaging defects in real time and scales across production lines from a single multi-task model, removing per-line calibration and operator subjectivity from the inspection process.
OneSix developed a deep learning-powered forecasting engine that improved demand forecast accuracy by 24% and automated long-range, hourly projections across 1,000+ buildings to support energy program participation.