SignalForge GTM

The Bottleneck

Revenue operations teams are drowning in "fragmented signal noise." CRM data, call intelligence, and email threads never reconcile in real-time. Most "AI" sales tools operate as black boxes, providing scores without evidence—creating a massive trust gap between the agent and the human seller.

The Architectural Solution

I built an Explainable Agentic Workflow that prioritizes auditability over raw power. The engine uses a modular pipeline to join disparate datasets, extract deterministic risk signals, and score pipeline health with full source-attribution.

• Explainable Decisions: Every classification is backed by specific, plain-language reasoning and per-signal evidence—eliminating the "black box" risk of standard AI.
• Governance & Evaluation Layer: Included a built-in evaluation suite that compares agent performance against human-curated ground truth, enabling iterative model refinement against KPIs.
• Configurable Business Logic: Logic is decoupled from code into triage_rules.json, allowing operators to adjust risk thresholds without engineering intervention.
• Human-in-the-Loop Orchestration: The system identifies low-confidence predictions, explicitly flagging them for human oversight before automated actions commit.

Command Stack

Python | Streamlit | Pandas (Data Ingestion) | Agentic Orchestration Logic