A corporate intelligence firm runs a network of 1,200+ field correspondents across 85 jurisdictions. The bottleneck was always between collection and analysis. We designed 16 agents to eliminate it.
The problem
Field correspondents send intelligence via WhatsApp voice notes in seven or more languages. The principal had to listen to every recording, translate, score for reliability, check for compliance risks, cross-validate against other sources, and synthesize it into client-ready reports. Manually.
The ceiling was about five concurrent mandates. The network could collect far more than one person could process. Revenue was capped by the founder's time, not by the quality of the network.
What we designed
The system is built in layers. Collection agents handle WhatsApp ingestion and voice transcription. Engagement agents score correspondents and coach them on reporting quality. Analysis agents cross-validate sources and flag contradictions. Intelligence agents maintain institutional memory and run OSINT monitoring. Delivery agents produce client-ready reports. A calibration agent tracks prediction accuracy over time.
The whole pipeline runs over WhatsApp because that is what correspondents use in the field. The voice pipeline supports English, French, Portuguese, Arabic, Swahili, Turkish, and Russian. It works on 2G connections.
WhatsApp voice notes in 7+ languages, auto-transcribed and translated
Scoring, anti-bribery checks, institutional knowledge retention
Cross-validation, source mapping, open-source monitoring
The math
A comparable analyst team costs £150,000 to £300,000 per year. This system replaces that headcount and operates indefinitely.
With the agents handling collection-to-analysis, mandate capacity goes from five to fifteen or more. That is $600K+ in annual revenue capacity with the same headcount.
First agent goes live in four weeks. All sixteen are deployed within twelve months.
30 minutes. We'll walk through your workflow and figure out where a multi-agent system can multiply your capacity.