Summary

A leading capital investments organization partnered with Marlabs to improve how its business development teams identified, prioritized, and engaged high-value prospects. Sales and marketing data lived across more than 12 platforms, forcing business development representatives (BDRs) to spend significant time researching accounts instead of selling.

Marlabs designed and delivered a scalable, AI-powered platform centered on agentic, enterprise knowledge AI with natural language querying capabilities. The platform included an AI scoring model, a GenAI context engine, and predictive analytics capabilities. The agent unified data from marketing automation, intent platforms, and CRM systems to provide insights and enable BDRs to query real-time lead and account context. The result was faster prioritization, richer insight, and more consistently data-driven engagement across the revenue lifecycle.

Challenge

The client’s revenue teams relied on enterprise systems like Marketo and Salesforce to manage demand generation and sales execution, but the data remained fragmented and difficult to interpret holistically. Lead scores lacked business context, pipeline quality was opaque until deals were lost, and BDRs spent nearly half their time manually researching accounts across systems.

As competitors began adopting AI-driven sales intelligence, the organization needed a way to surface actionable insights in real time — without forcing teams to learn new tools or workflows.

Solution

Marlabs developed a custom AI-powered lead prioritization and engagement platform anchored by a conversational enterprise knowledge AI agent that could dynamically pull, synthesize, and explain data from multiple systems.

Unified data foundation

The solution integrated engagement data, intent signals from, and sales activity and sentiment into a single intelligence layer. This eliminated data silos and created a comprehensive, continuously updated view of each account and lead.

Generative AI context engine

At the core of the platform, we built a generative AI engine that translated raw signals into qualitative context. When queried, the enterprise knowledgeAI agent summarized buying stage, engagement history, risks, and opportunities, which removed the need for BDRs to manually search CRM notes or dashboards.

AI-driven prioritization and scoring

An AI scoring model generated a Journey Priority Score (0–100) for each lead or account, balancing intent, engagement, and sales interactions. The model highlighted top positive and negative signals so teams could quickly understand why a prospect was ranked the way it was, not just the score itself.

Phased, de-risked rollout

Marlabs delivered the platform through a phased approach:

• Foundation: Data integration and initial scoring model development
• Pilot: Deployment to a small group of champion BDRs with iterative refinement
• Scale: Broader rollout supported by training and change management
• Optimize: Ongoing model retraining and feature enhancements driven by performance data

Security and governance were built into the architecture to enable secure usability, insight quality, and measurable revenue impact.

Results

The agentic, enterprise knowledge AI platform transformed how BDRs accessed and acted on revenue intelligence. Instead of spending time researching accounts, teams could ask direct questions and receive immediate, contextual answers that informed outreach and messaging.

The unified view of sales and marketing data improved pipeline transparency, reduced wasted outreach to low-quality leads, and enabled faster, more confident engagement with high-intent prospects.

Impact

• Improved BDR productivity by reducing manual research time
• Increased conversion of qualified opportunities through better prioritization
• Greater pipeline visibility across sales and marketing systems
• Faster onboarding and adoption due to natural-language access to insights with agentic AI
• A scalable foundation for future AI-driven recommendations and next-best-action guidance