How Sincera Categorizes 1–2M Records at 85%+ With AI

Sincera ingests millions of messy, inconsistent product and audience records every month. A five-agent AI pipeline now maps each to a clean taxonomy at 85%+ accuracy, in real time.

85%+

Maintained on millions of records

Implementation Time

Chris Taylor

CEO & Co-Founder

Fractional AI

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The Challenge

Sincera receives millions of unstructured records monthly describing products and services from diverse internet sources. The records lack consistency — some represent customer segments like 'Lipton purchaser' while others describe products like 'pork tenderloin' — and needed to be standardized into a usable format.

What They Built

Fractional AI built an AI categorization system that maps records to Shopify's product taxonomy of 10,000 categories with up to seven levels of hierarchy.

A multi-step LLM pipeline runs five sequential agents: CLASSIFY determines whether a record is a brand, segment, or uncategorizable; ENRICH expands it with richer descriptions; DISCERN selects the best candidate category; JUDGE evaluates the decision and assigns a confidence score; and a final step returns categorization with confidence metrics. It uses Claude-3.5-Sonnet, Llama-3.1, and GPT-4o-mini, with Braintrust for evaluations, OpenAI Structured Output, and a modified-RAG vector search workflow.

AI Role

The pipeline runs five sequential agents. CLASSIFY decides whether a record is a brand, a segment, or uncategorizable; ENRICH expands it with a fuller description; DISCERN selects the best candidate category via vector search; JUDGE evaluates the choice and assigns a confidence score; and a final step returns the categorization with confidence metrics. Different models (Claude 3.5 Sonnet, Llama 3.1, GPT-4o-mini) are used per stage.

AI Model

Claude-3.5-Sonnet, Llama-3.1, GPT-4o-mini, Braintrust (evals), OpenAI Structured Output, modified-RAG vector search

Infrastructure

Shopify's 10,000-category product taxonomy as the target schema • Modified-RAG vector search index • Braintrust for evaluation • High-volume unstructured product and audience records as input

Integration Points

Five-stage agent pipeline (classify → enrich → discern → judge → return) • Modified-RAG vector search to shortlist candidate categories • OpenAI Structured Output for machine-readable results • Real-time ingestion of 1–2M records per month with confidence scoring

Impact

85%+ accuracy

Categorization accuracy consistently above 85%.

1-2M records monthly

Processes one to two million unstructured records per month.

Real-time processing

Records are categorized in real time against a 10,000-category taxonomy.

Technology Utilized

Implementation Complexity

Time Savings

Customer Experience

Best Fit For

Best fit for data or adtech companies that must normalize high volumes of messy, inconsistent records into a structured taxonomy in real time.

Chris Taylor

CEO & Co-Founder

Fractional AI

CEO & Co-Founder of Fractional AI, helping PE firms and portfolio companies implement AI workflow automations, product features, and diligence at scale.

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Industry:

Technology & Software

Business Function:

Product & Engineering

Company Size:

Project Cost:

Cost not disclosed

Ownership:

Organization Type:

AI Pattern:

Natural Language Processing

Knowledge Management & Search (RAG)

Value Type:

Time Savings

Customer Experience

AI Model:

Frequently Asked Questions

How does Sincera categorize 1-2 million unstructured records a month at 85%+ accuracy?

Through a five-agent AI pipeline that classifies each record, enriches it with a fuller description, selects the best category via vector search, and judges the result with a confidence score. It maps records to Shopify's 10,000-category taxonomy in real time at accuracy consistently above 85%.

What AI models and tools power Sincera's categorization pipeline?

The pipeline uses Claude 3.5 Sonnet, Llama 3.1, and GPT-4o-mini across its stages, with OpenAI Structured Output for machine-readable results, a modified-RAG vector search to shortlist categories, and Braintrust for evaluation.

What results did Sincera achieve with the AI categorization system?

The system holds categorization accuracy consistently above 85%, processes one to two million unstructured records per month, and works in real time against a 10,000-category taxonomy.

How quickly does Sincera's system categorize records?

Records are categorized in real time as they arrive, at a volume of one to two million per month. A separate implementation timeline was not disclosed.

Who is this agentic categorization approach best for?

It is best suited to data or adtech companies that must normalize high volumes of messy, inconsistent records into a structured taxonomy in real time.