Case Study · AI Implementations

Agentic SDR Research Platform

Sales intelligence · Agentic AI · Prospect research · 45+ data points · Enterprise workflow

The challenge

Sales research was too slow to scale.

Hours lost per prospect

SDRs spent 3-4 hours compiling company intelligence from scattered public and private sources before meaningful outreach could begin.

Low confidence in the data

Manual research produced incomplete, inconsistent prospect profiles with roughly 70% reliability.

No operating leverage

Research throughput was capped by human effort, limiting the number of companies a rep could pursue at once.

Revenue leakage

Delayed and shallow research meant weaker outreach, slower qualification, and missed opportunities.

What we built

An agentic research assistant for SDRs.

Multi-source research agents

Agents gather, normalize, and summarize company intelligence across public and private sources.

45+ data-point profiles

Each company profile includes structured firmographic, market, signal, and outreach-ready context.

Workflow-ready output

Results are shaped for SDR workflows, not as raw search dumps, so reps can act immediately.

Scalable batch research

The system can run research across 50+ companies in one flow while preserving governance and security boundaries.

Results

Research moved from bottleneck to accelerator.

3-4 hrs -> min

Prospect research turnaround.

45+

Structured data points per company.

50+

Companies researched in one run.

70% -> 99%

Data reliability improvement.

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