# Sweetspot Unifies Hybrid Search on Postgres with ParadeDB

> How SweetSpot leveraged ParadeDB for their search infrastructure.

Published: 2024-06-09 · Author: Ming Ying

Source: https://www.paradedb.com/customers/sweetspot

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## Sweetspot Overview

[Sweetspot](https://sweetspot.so) is an AI-driven platform for government procurement and contracting. Sweetspot streamlines the entire contracting process from
opportunity identification to bid/no-bid decision making to proposal development.

## The Challenge

Sweetspot needed a [hybrid search](/learn/search-concepts/hybrid-search) — the combination of full-text and vector search — solution to power its contract search and AI-assisted proposal drafting products.

Since Sweetspot was already using Postgres, it was important that their search solution interoperated seamlessly with Postgres. Before using ParadeDB, Sweetspot
experienced significant friction synchronizing large volumes of data between Postgres and external databases like Elasticsearch.
This synchronization involved writing and maintaining custom connectors, which were prone to bugs and data inconsistencies. Additionally,
many of the vector databases that Sweetspot evaluated had unreliable infrastructure which caused frequent downtime in their application.

## The Solution

Sweetspot needed a search solution that was easy to integrate, compatible with existing Postgres architecture, and offered reliable performance over
15 million documents and 2 million vectors.

After an extensive review, Sweetspot selected ParadeDB. With ParadeDB, Sweetspot unified its data stack on Postgres, which removed the need to manage a
separate database or use custom connectors. ParadeDB's advanced full-text and vector search capabilities enabled Sweetspot to build powerful hybrid search
experiences without additional operational complexity.

## Outcomes

Integrating with ParadeDB took less than a week since Sweetspot was already using Postgres and could reuse its existing code.

Post-integration, Sweetspot observed a 50% overall reduction in query latency. Hybrid search queries achieved a P99 latency of 50 milliseconds and had improved
precision and recall compared to pure vector search.

ParadeDB also simplifies the process of achieving FedRAMP and NIST compliance since Postgres has robust authentication, access control, logging, and disaster recovery built in.

To build a similar retrieval pipeline, see [Hybrid Search in PostgreSQL: The Missing Manual](/blog/hybrid-search-in-postgresql-the-missing-manual).

To bring hybrid search to your application, [get started with ParadeDB Cloud](https://app.paradedb.com) or [get started locally](https://www.paradedb.com/docs/start/install).