SpecOS
getspecos.comRole: Founder
Scope: Product Design & Branding, Full-Stack Development, Data Architecture & AI Integration
Timeline: January 2025 - December 2025
Tech Used
AI-powered product discovery for construction. SpecOS is a conversational and image-based product discovery platform built for construction. It takes product data from 328K+ products, 1.25M variants, and 9.2M product features and makes them searchable in seconds.
Overview
Product data in construction is locked in catalogs and PDFs. Contractors waste hours chasing specs, while distributors lose sales when products can't be found.
SpecOS transforms that workflow: ask in plain language or upload an image, and get structured results instantly.
Admin UI
Data intelligence dashboard for managing product enrichment, gap detection, and completeness scoring.
Automatically detects when customers ask questions that can't be answered due to missing product data, flagging gaps for enrichment.
Extracts missing values from product documentation using AI, automatically filling in spec sheets, PDFs, and technical data.
Category-specific scoring ensures products aren't penalized for irrelevant attributes—only what matters for their category.
Custom AI Tools
The AI agent has access to 13 specialized tools that handle everything from natural language search to document retrieval.
Natural language product search
Reverse image search using CLIP embeddings
Semantic search across product catalog
Retrieve full product specifications
Direct SKU lookup
Find similar or substitute products
Fetch spec sheets and documentation
List all available product documents
Search within product PDFs
Multi-step product recommendation flow
Retrieve product ratings and reviews
Industry terminology definitions
General construction knowledge
Agentic RAG Model
Our system uses an Agentic Retrieval Augmented Generation (RAG) model. This is a multi-step process that uses specialized AI "agents" to deliver more accurate and context-aware results than a standard chatbot.
Agentic RAG Model Diagram
Data Model
The data model is designed for flexibility to accommodate complex and varied product catalogs without requiring schema changes.
Core Schema
A brand has many products. A product has many product_variations.
Flexible Attributes
EAV Model — to handle a wide range of product specifications, we use an Entity-Attribute-Value (EAV) model. Product-specific attributes (e.g., "Height", "Voltage", "Material") are stored in the product_features table as key-value pairs.
Hardcoded Sequences
Critical multi-step interactions are not left to the discretion of the AI. They are explicitly coded as non-negotiable sequences to ensure reliability and safety.
Example
The getProductAdvice sequence — when a user asks for product advice, the system executes a mandatory 3-message sequence. This is a hardcoded rule, not an AI decision.
getProductAdvice Sequence
This diagram is best viewed on desktop
Reverse Image Search Architecture
Reverse Image Search Architecture
This diagram is best viewed on desktop
Model Agnostic
SpecOS was designed to be model agnostic, allowing customers to choose their preferred model without having to change the code.
Project Details
Role: Founder
- Product Design & Branding
- Full-Stack Development
- Data Architecture & AI Integration
Tech Stack
© 2026 Ethan James Fox