Overview

Sentilops is my financial-literacy and intelligence automation platform. The system is designed to convert an overwhelming stream of newsletters, market updates, business content, investing education, and real-estate information into structured research that can be searched, routed, and acted on. The goal is financial intelligence and education, not automated investment advice or guaranteed-return trading signals.

Architecture

A representative Sentilops automation uses a pipeline such as:

• Incoming webhook / source content
• Variable normalization
• Historical context lookup
• AI agent with a finance knowledge layer
• Web content retrieval and text normalization
• Second-stage AI analysis
• Structured routing
• Sheets / Drive / Slack outputs

Decision Routes

Depending on the analysis, the system can route information into:

• priority alerts
• action queues
• tool / platform trackers
• market lessons
• cash-flow diagram inputs
• research archives
• structured finance records

Knowledge Layer

Instead of treating each article as an isolated summary, Sentilops is designed to preserve useful material as a growing financial knowledge system. This creates reusable context for:

• financial literacy
• assets vs. liabilities
• cash-flow thinking
• real-estate research
• business trends
• opportunity tracking
• finance content creation

Reliability Patterns

The architecture uses structured routing and dedicated outputs rather than one giant AI response. That makes it easier to:

• inspect each decision path
• keep research organized
• send only meaningful alerts
• separate action items from educational notes
• preserve source material
• troubleshoot individual branches

Technologies

• Make.com
• Make AI Agents
• Gmail / webhook intake
• Google Sheets
• Google Drive
• Slack
• HTTP
• HTML-to-text parsing
• JSON
• knowledge bases
• routing logic
• structured financial research