# How Allspice uses Pinecone

**20% → 97%** accuracy in ingredient matching

As published on [pinecone.io](https://www.pinecone.io/customers/allspice/). Captured by usedby on 2026-10-08.

- Company: [Allspice](https://www.usedby.ai/companies/allspice.md)
- Tool: [Pinecone](https://www.usedby.ai/tools/pinecone.md)

## What the story says

Allspice, a food technology company, uses Pinecone as a vector database to match messy ingredient text to its structured ingredient database. It also powers recipe similarity, fuzzy recipe search, and chatbot input normalization.

Summary written by usedby from the source page, in English. The figures are those of Pinecone and Allspice, not ours.

> Before Pinecone, ingredient matching accuracy sat at roughly 20% — far too low to support a production feature. After implementation, accuracy jumped to 97%, with Pinecone serving as the core enabling piece of the matching system.

- **110,000**: “the platform now manages a growing library of 110,000 total embeddings”
- **100,000**: “The platform now indexes approximately 100,000 recipe embeddings.”
- **10,000**: “approximately 10,000 ingredient embeddings”

> Now more than ever, it is crucial to iterate quickly. I would have never tried Pinecone without a cloud-hosted, serverless option. I needed something that I could set up in an afternoon and get working in a basic pipeline to evaluate the effectiveness of my solution to my problems.
>
> William Templeton, co-founder and CTO at Allspice

## What usedby checked

We compared the story with its live page on 2026-10-08.

- Checked: the figure 20% → 97% is on the page; its label is our wording.
- Checked: the passage quoted above is copied word for word from the page, near the name of Allspice.
- Checked: Allspice uses Pinecone. Confirmed line. Latest check across sources: 2026-10-08.
- Not checked: the result itself. We quote it; we did not measure it.

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Source: https://www.usedby.ai/case-studies/allspice-pinecone · How we check: https://www.usedby.ai/methodology