Summary

A leading capital investments organization partnered with Marlabs to improve how their internal teams functioned, including business development, HR, customer support, project management, and others. Enterprise lived across more than 20 platforms, forcing employees to spend significant time researching before they could move forward.

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 agentic solution unified data from systems like Salesforce, Marketo, Confluence, Jira, GitLab, Workday, Zendesk, and ServiceNow to provide insights and enable employees to query real-time data and contexts. The AI platform also enabled intelligent document search and report generation with specialized, built-in agents to ensure quality. The result was faster prioritization and discovery, richer insight, and more consistently data-driven engagement across the organization.

Challenge

The client’s teams relied on large enterprise systems for demand generation, sales execution, HR processes, project management, customer support, and more, but the data remained fragmented and difficult to interpret holistically. Lead scores lacked business context, pipeline quality was opaque, and employees regularly spent nearly half their time manually searching for and contextualizing the data they needed.

As competitors began adopting AI-driven 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 enterprise platform anchored by conversational AI that could dynamically pull, synthesize, and explain enterprise knowledge from multiple cross-functional systems.

Unified data foundation

The solution integrated engagement data, intent signals, sentiment analysis, and account activity, among other factors, into a single intelligence layer. This eliminated data silos and created comprehensive, continuously updated reports.

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 agentic enterprise knowledge AI not only summarizes data but also produced reports and intelligently searches documents, removing the need for employees to manually search across multiple notes, documents, reports, and dashboards.

AI-driven prioritization and scoring

An AI scoring model generated a priority score (0–100) for certain teams' needs, like sales. To score each lead or account, the agentic AI solution balanced intent, engagement, and sales interactions. The model highlighted top positive and negative signals so teams could quickly understand why it gave a certain ranking, 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 employees 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 employees accessed and acted on contextualized intelligence. Instead of spending time researching across systems, teams could ask direct questions and receive immediate, contextual answers that informed their day-to-day efforts.

The unified view of data improved transparency, reduced manual efforts and wasted rework, and enabled faster and more confident engagement across the company.

Impact

• Improved productivity by reducing manual research time
• Increased sales conversion of qualified opportunities through better prioritization
• Greater visibility across enterprise systems and teams
• 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