Specialized agent graph
Research, enrichment, scoring, critique and outreach responsibilities are separated into bounded modules rather than collapsed into one general agent.
Agentic sales intelligence
Eighteen specialized agents turning an ICP into evidence-backed action.
DeepLead researches companies and people, triangulates public information, scores fit and coordinates safe outreach through specialized agents. The system includes model policy, evaluation gates and cost governance so intelligence quality can improve without making production behavior unpredictable.
SEMANTIC CRITIC
claim confidence 0.94
Private product repository. Architecture facts are derived from its 42 route handlers, specialized agent modules and executable quality-gate pipeline.
18
specialized agents
42
API routes
40+
automated tests
4
quality-gate stages
System anatomy
Research, enrichment, scoring, critique and outreach responsibilities are separated into bounded modules rather than collapsed into one general agent.
Claims are assembled from multiple sources and passed through semantic critique before they become operational recommendations.
Claude Agent SDK and OpenAI capabilities are selected through task policies that account for quality, latency and cost.
Intelligence gates, chat A/B evaluation, automated tests and production-readiness checks protect releases from silent quality regressions.
Engineering judgment
Seniority is not the number of technologies in a project. It is the ability to identify failure modes early and choose boundaries that keep the system understandable after launch.
Each agent owns a narrow contract, making routing explainable and failures easier to isolate and evaluate.
A cost governor and model policies keep higher-capability inference focused on the stages where it changes the result.
Prompt quality is backed by automated evaluation and rollout checks instead of relying on manual spot checks alone.
Production stack