Projects by Shashank Shekhar - BuildOS, AI, Search & Product Systems

BuildOS Agent

AI-native development workspace for project planning, knowledge management, architecture generation, AI-assisted coding, documentation, and execution.

Problem: Modern AI coding tools lose project context, duplicate decisions, and struggle with long-running multi-repository software projects.

Tech stack: Next.js, React, TypeScript, Tailwind

Outcome: Designed a persistent engineering workspace where project architecture, decisions, docs, implementation status, prompts, and AI conversations become reusable project memory.

AI-Integrated Automation Services

Automation services for PDF/text extraction into structured JSON with validation, deterministic post-processing, and fallback paths.

Problem: Product teams needed dependable AI workflow automation without brittle prompts or costly retry loops.

Tech stack: Python, FastAPI, Docker, Ollama

Outcome: Implemented FastAPI-based automation services with deterministic validation, reduced extraction errors, and stable Docker deployment patterns.

AI-Powered Workflow Builder Platform

Visual workflow builder with NL-to-graph generation, versioning, and separation between definition and execution layers.

Problem: Operations teams needed AI workflow automation with clear controls, reusable templates, and auditable execution paths.

Tech stack: FastAPI, SQLite, Alembic, React

Outcome: Shipped a production-ready workflow platform with FastAPI backend services, Docker deployment, and versioned workflow governance.

Data Transformation Adapter

In-progress adapter to transform CSV/JSON into structured templates with export to JSON or CSV.

Tech stack: FastAPI, Python

High-Scale Product Discovery Platform

Enterprise-scale product discovery platform for 7.7M+ SKUs with schema-driven UI, faceted search, and bulk editorial workflows.

Problem: Retail teams had to search and curate millions of SKUs with tools that could not keep up — slow search, no governance, and risky bulk updates.

Tech stack: Angular, Typesense, FastAPI, PostgreSQL

Outcome: Delivered a high-scale Typesense search interface and product engineering workflow that improved discovery speed and editorial consistency.

Taxonomy & Classification Management System

Governed taxonomy and ML-assisted classification system to manage categories and attributes across enterprise catalogs.

Problem: Catalog teams were classifying millions of products by hand, with inconsistent category rules and no reliable review process.

Tech stack: FastAPI, PostgreSQL, Typesense, Angular

Outcome: Built an Angular-led taxonomy application with ML-assisted review queues that improved classification accuracy and cut manual analyst effort at enterprise scale.