AI / ML ToolsRecent

ML Keyword Intelligence Tool

A Streamlit-based internal SEO tool that crawls content, analyzes semantic relationships, and surfaces keyword opportunities using ML models.

Start a Project View Services

Project Snapshot

Service: AI / ML Tools
Project type: Internal SEO Tool / Streamlit App
Industry: SEO / Content Strategy
Client type: SEO and content operations team
Timeline: Internal tooling build
Year: Recent

Overview

Project Overview

A compact view of the business context, implementation direction, and the system-level goal behind the build.

Internal SEO research tooling built around website data, clustering, and semantic analysis.

Narrative

Challenge and Solution

The problem space and the direction the build took to turn it into a workable system.

The Challenge

Manual keyword research was slow and disconnected from the real content already published on the website, making it difficult to identify practical SEO opportunities from actual site data.

The Solution

I built a Streamlit-based workflow that crawls website pages, stores content in a database, extracts keywords, generates semantic similarity and relativity scores, and clusters opportunities using transformer-based models.

Approach

Approach and Implementation

Execution choices, architecture direction, and implementation details that shaped the final system.

Combined crawling, storage, keyword extraction, and model-based scoring in one internal workflow

Used transformer models to compare semantic relationships instead of relying on simple term frequency

Packaged the system in Streamlit so the analysis was usable beyond engineering-only workflows

Stack

Technology Stack

Grouped by role so the implementation is easy to understand at a glance.

Data / Intelligence

PythonStreamlitDatabase storageNLP

Platform

TransformersHugging Face modelsWeb crawling

Outcome

Outcome and Impact

Delivery impact, workflow gains, or strategic value signals supported by the available case study data.

The tool converted existing site content into a working keyword intelligence system that made research faster and more grounded in real content inventory.

Automated keyword discovery

ML-based content analysis

Keyword clusters from website data

Faster SEO research workflow

Reduced time spent on manual keyword exploration

Created a repeatable internal process for content opportunity analysis

Improved the ability to spot gaps and search-intent groupings from live site data

The tool converted existing site content into a working keyword intelligence system that made research faster and more grounded in real content inventory.

Deep Dive

System Modules and Build Notes

Richer project notes, functional modules, and implementation details pulled from the long-form case study content.

Strategic highlights

Research workflow tied directly to site content
Semantic grouping instead of only manual spreadsheet analysis
Admin-friendly interface for repeated use

Business impact

Reduced time spent on manual keyword exploration
Created a repeatable internal process for content opportunity analysis
Improved the ability to spot gaps and search-intent groupings from live site data

Takeaway

Final Takeaway

The tool converted existing site content into a working keyword intelligence system that made research faster and more grounded in real content inventory.