israNetworks
Built at Built with Opus 4.7: a Claude Code hackathon · Apr 21, 2026 · Remote

Operator is an AI system that turns marketing data into real business decisions - and executes them. The system integrates four critical layers into a single operational loop: Paid advertising platforms (Meta, Google) Lead sources (forms, CRM systems) Sales outcomes (calls, deals, revenue) External market context (seasonality, demand shifts) How the System Works Operator builds a deterministic feedback loop between campaigns and real revenue. It continuously: Ingests data from ad platforms and CRM systems Matches leads to actual sales outcomes using deterministic logic (IDs, timestamps, deduplication) Evaluates performance based on real business impact - not platform-reported metrics Generates structured actions Executes or escalates those actions based on risk policies This process runs in recurring evaluation cycles (every 4 days), ensuring continuous adaptation to real performance. AI Layer (Opus 4.7 Integration) Operator uses Opus 4.7 as a reasoning layer on top of deterministic data. While core data processing, matching, and evaluation are fully deterministic, Opus 4.7 is used to: Interpret complex performance patterns Generate structured action proposals Provide explanations for decisions Assist in creating new campaign variations This hybrid architecture ensures that: Critical decisions remain grounded in deterministic logic AI is applied where reasoning and flexibility are required Execution Layer (Core Differentiator) Operator is not an analytics tool - it is an execution system. Based on its evaluation, it can: Pause or reduce spend on unprofitable campaigns Reallocate budget toward high-performing segments Prioritize campaigns based on lead quality and actual revenue conversion Generate and suggest new campaign variations All actions are tied to real business outcomes - not proxy metrics like CPL. Controlled Autonomy Operator operates under strict guardrails: Budget changes limited to ±20% per cycle Minimum data thresholds required for action Risk-based execution model: Low → auto-executed Medium → logged with rollback High → requires approval Additional controls: Human-in-the-loop approvals (WhatsApp / dashboard) Full audit logs and explainability Built-in rollback for every action Why This Matters Advertising platforms optimize for signals they can see. Operator optimizes for what actually matters: revenue. Due to tracking limitations, reported conversions often diverge significantly from actual business outcomes. Operator closes that gap by feeding real business data into the decision layer - and acting on it. Scalability The system is designed to operate beyond a single account. It can: Adapt to different industries and business models Learn from cross-account performance patterns Operate across regions with localized context This positions Operator as a scalable execution layer for marketing systems — not just a tool. Core Principle Most systems: → Analyze performance Operator: → Makes decisions → And acts on them