Project Case Studies

Problem

Competitive updates were shared manually, creating inconsistent visibility across product, sales, and marketing.

Technical approach

• Built monitoring workflow to track competitor content, pricing, and launch signals across priority sites.
• Used AI analysis to convert raw changes into structured briefs and summaries for commercial teams.
• Automated alert distribution so sales, product, and marketing could access updates without manual coordination.
• Created self-serve reporting layer for ongoing competitive review and prioritization.

Problem

List building and prospect qualification were manual, inconsistent, and difficult to scale across territories.

Technical approach

• Pulled public state license data into a repeatable sourcing workflow and cross-referenced it against internal customer data.
• Applied NPPES and ICP-based signals to qualify prospects and prioritize accounts for outreach.
• Used AI-assisted research and scoring to produce more targeted prospect lists and sales-ready context.
• Structured routing by state and territory so qualified opportunities could move directly into sales workflows.

Problem

Manual translation took around five days: documents were sent to a platform, quoted, accepted, and then queued for delivery.

Technical approach

• Designed upload flow requiring minimal training so teams could submit files and receive translated outputs automatically.
• Triggered translation jobs directly from file uploads in a SharePoint upload folder.
• Automated translation processing with DeepL through Power Automate while preserving formatting and styling.
• Applied glossary and consistency controls so translated outputs stayed aligned with language standards.
• Routed translated drafts for QA and approval before delivery back to teams.

Problem

Product discovery was low on the website experience.

Technical approach

• Defined recommendation logic using purchase behavior, product relationships, and shopping signals.
• Implemented recommendation modules directly in the website experience to increase product discovery.
• Built attribution model tying on-site search and recommendations to downstream purchases.
• Used measurement outputs to refine the recommendation experience and quantify revenue influence.

Problem

Manual job searching across dozens of company career pages, email alerts, and job boards was time-consuming and inconsistent.

Technical approach

• Built TypeScript and Node.js scrapers for Greenhouse, Lever, and Ashby ATS platforms to capture structured job data.
• Integrated Gmail parsing to process LinkedIn and Indeed alerts into normalized listings.
• Designed AI scoring against resume keywords, target roles, skills, and location preferences.
• Built Notion integration to deduplicate and store matches, gaps, and red flags.
• Implemented digest workflow to surface the strongest opportunities in a single pass.