All work

AI SaaS · Flagship case study

A content engine that learns how users communicate

An AI workflow that analyzes connected social content and generates new posts aligned with each user's style and chosen topics.

Role

Full-stack engineer · End-to-end technical owner

Stack

Next.js · Node.js · MongoDB · GCP · Docker · AI APIs

Timeline

[Add timeline]

[Add anonymized product image or recreated UI]01

Project details, identifiers, visuals, and metrics have been generalized to respect confidentiality.

The challenge

Turning complexity into a product people can trust.

Generic AI writing did not reflect each user's voice. The product needed to turn social content into a usable style profile, then create relevant posts without making the workflow feel technical.

My approach

Designed from the system out.

01

Designed the flow from social data ingestion and normalization to style analysis and content generation.

02

Structured user preferences and inferred writing characteristics so the generation workflow had reusable context.

03

Built the product across backend, database, frontend, cloud delivery, and AI integration; UI/UX refinements were collaborative.

System view

[Add or refine architecture diagram]

InputApplicationQueue / ServicesOutcome

Impact

What changed.

[Add number or range of users supported]

[Add generation time or quality improvement]

[Add product or business outcome]

Next case studyFlexible billing without fragile access control