Introducing Parallel Search Turbo: the fastest and lowest-cost search for AI. [Learn more.](/content/blog/parallel-search-turbo/index.html)[\[Learn more.\](/content/blog/parallel-search-turbo/index.html)](/content/ai/blog/parallel-search-turbo/index.html)

HumanMachine

October 23, 2025

# \\# How Starbridge powers public sector GTM with state-of-the-art web research

By using the Parallel Task API, Starbridge helps companies scale public sector sales with efficiency and accuracy.

Tags:[Customers](/content/blog?tag=customers/index.html)

Reading time: 4 min

[Starbridge](http://starbridge.ai/)[\[Starbridge\](http://starbridge.ai/)](http://starbridge.ai/) is a GTM intelligence platform designed for businesses that sell to the public sector, targeting local & state government, school districts, and higher education. Starbridge uncovers intent signals unique to the public sector— competitor contracts, board minutes, budget data, and RFPs to enable instant, targeted sales actions.

Starbridge built its GTM platform with AI at the core, using Parallel's Task API to conduct large-scale web research tasks that deliver high-quality signal data from the public web. This represents a new wave of AI-native vertical software that leverages web intelligence as a foundational data layer to transform how specialized industries access and use public web information.

## \\#\# The challenge of public sector sales

Traditional GTM teams spend thousands of manual hours combing the web for buying signals and contact information. This challenge is exponentially harder for public sector teams. Winning in this space means tracking budget data, grants, and vendor footprints across hundreds of thousands of local government, district, and school sites - at a scale no manual team can match.

_\_“Our early approach relied on human labeling, which was slow, error-prone, and expensive,” \__

explains the Starbridge team.

_\_“With Parallel, we now systematically scan government websites, extracting high-quality, sector-specific buying signals at scale - with price-performance that blows manual methods out of the water.”\__

By replacing manual research with comprehensive, AI-driven coverage, Starbridge customers see larger, more qualified pipelines - and ultimately, higher win rates.

## \\#\# Choosing Parallel for deep research price-performance that scales

Starbridge evaluated Parallel against OpenAI, Anthropic, and Exa. The evaluation tested each provider's ability to navigate complex government websites, extract accurate contact information, and identify buying signals from public procurement data.

_\_"We evaluated Parallel against alternatives and found that it consistently delivered the highest quality results across all our test cases, at the best price on the market. We felt confident that Parallel's flexible and easy-to-use APIs would scale with Starbridge as we grew. What really set Parallel apart was the ability to handle complex government websites that other providers struggled with - the depth of the crawling and coverage made a huge difference for our use case. Parallel was the clear choice to power our core technology.”\__

## \\#\# Redefining public sector intelligence with Parallel web research

Within days, Starbridge successfully integrated the Parallel Task API into production and has since built a powerful product suite that delivers unique GTM intelligence features, helping companies scale public sector sales more efficiently.

**\*\*Real-time structured contact enrichment\*\***: The Starbridge Contact Agent, powered by Parallel's structured web research capabilities, automatically identifies relevant contacts and provides accurate, fresh contact details within public sector agencies. In the public sector, LinkedIn is often insufficient - employees frequently don't maintain updated profiles. The most trustworthy sources are staff directories on government websites. Parallel enables Starbridge to systematically extract contact information from thousands of these directories at scale, sourcing data directly from authoritative first-party sources.

**\*\*Building comprehensive buyer attribute data\*\***: Starbridge uses Parallel to automatically discover and enrich public sector buyer information on a recurring basis - monitoring grant funding and allocation, analyzing budget distributions by line item, tracking organizational changes, identifying relevant news mentions, and detecting personnel changes that signal new opportunities.

**\*\*Deep research on public sector AI adoption\*\***: Starbridge used its web research agent, powered by Parallel Deep Research, to scan 5,000+ county websites, news sources, and public documents to surface real-time signals on [AI adoption](https://starbridge.ai/research-and-reports/school-districts-ai-adoption-index)[\[AI adoption\](https://starbridge.ai/research-and-reports/school-districts-ai-adoption-index)](https://starbridge.ai/research-and-reports/school-districts-ai-adoption-index). To do this, Starbridge's engineering team took raw Parallel data and built sophisticated analytics on top. The result is unprecedented visibility into public sector AI adoption patterns and pinpoint high-value targeting opportunities with precision.

**\*\*Flexible in-depth research\*\***: Through complex API orchestration and data pipeline engineering, Starbridge created a powerful integration with Parallel that handles arbitrary search queries specific to a market segment or workflow, across disparate data sources - intelligently combining public web intelligence, proprietary datasets, and internal company data into a single, queryable interface.

## \\#\# Building the bridge between business and government

Today, Parallel powers all of Starbridge's web research queries - enabling Starbridge to provide the highest quality public sector intelligence on the market. Parallel’s web research infrastructure has transformed how Starbridge's customers approach the public sector market. On average, Starbridge customers see public sector booked meeting rates increase by 20% with many customers booking >5+ meetings within their first 1-2 days of using Starbridge.

_\_"We're creating entirely new capabilities that weren't possible before," \__

reflects the Starbridge team. _\_\__

_\_"Parallel's web research infrastructure is easy to use and makes it possible to turn the entire public web into a structured queryable intelligence layer for public sector sales. We’re excited to continue to scale with Parallel to help businesses better collaborate and make an impact with the government."\__

## \\#\# Ready to get started?

Sign up for free. No credit card required.

[Try Parallel](https://platform.parallel.ai/home)[\[Try Parallel\](https://platform.parallel.ai/home)](https://platform.parallel.ai/home) [Contact sales](https://contact.parallel.ai/)[\[Contact sales\](https://contact.parallel.ai/)](https://contact.parallel.ai/)

[Are you an agent? Read this to onboard Parallel](/content/agents.md)[\[Are you an agent? Read this to onboard Parallel\](/content/agents.md)](/content/ai/agents.md)

By Parallel

October 23, 2025

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Author: By Parallel

[Sep 11, 2025\\
**\- \[Introducing Parallel Deep Research reports\](/content/blog/deep-research-reports/index.html)**](/content/blog/deep-research-reports/index.html)

[Tags:](/content/blog/deep-research-reports/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[Sep 9, 2025\\
**\- \[A new pareto-frontier for Deep Research price-performance\](/content/blog/deep-research-benchmarks/index.html)**](/content/blog/deep-research-benchmarks/index.html)

[Tags:](/content/blog/deep-research-benchmarks/index.html) [Benchmarks](/content/blog?tag=benchmarks/index.html)

Author: By Parallel

[Sep 5, 2025\\
**\- \[Building a Full-Stack Search Agent with Parallel and Cerebras\](/content/blog/cookbook-search-agent/index.html)**](/content/blog/cookbook-search-agent/index.html)

[Tags:](/content/blog/cookbook-search-agent/index.html) [Developers](/content/blog?tag=developers/index.html)

Author: By Parallel

[Aug 21, 2025\\
**\- \[Webhooks for the Parallel Task API\](/content/blog/webhooks/index.html)**](/content/blog/webhooks/index.html)

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Author: By Parallel

[Aug 14, 2025\\
**\- \[Introducing Parallel: Web Search Infrastructure for AIs \](/content/blog/introducing-parallel/index.html)**](/content/blog/introducing-parallel/index.html)

[Tags:](/content/blog/introducing-parallel/index.html) [Benchmarks](/content/blog?tag=benchmarks/index.html),[Product](/content/blog?tag=product/index.html)

Author: By Parallel

[Aug 7, 2025\\
**\- \[Introducing SSE for Task Runs\](/content/blog/sse-for-tasks/index.html)**](/content/blog/sse-for-tasks/index.html)

[Tags:](/content/blog/sse-for-tasks/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[Aug 5, 2025\\
**\- \[A new line of advanced Processors: Ultra2x, Ultra4x, and Ultra8x \](/content/blog/new-advanced-processors/index.html)**](/content/blog/new-advanced-processors/index.html)

[Tags:](/content/blog/new-advanced-processors/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[Aug 4, 2025\\
**\- \[Introducing Auto Mode for the Parallel Task API\](/content/blog/task-api-auto-mode/index.html)**](/content/blog/task-api-auto-mode/index.html)

[Tags:](/content/blog/task-api-auto-mode/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[Jul 31, 2025\\
**\- \[A state-of-the-art search API purpose-built for agents\](/content/blog/search-api-benchmark/index.html)**](/content/blog/search-api-benchmark/index.html)

[Tags:](/content/blog/search-api-benchmark/index.html) [Benchmarks](/content/blog?tag=benchmarks/index.html)

Author: By Parallel

[Jul 31, 2025\\
**\- \[Parallel Search MCP Server in Devin\](/content/blog/parallel-search-mcp-in-devin/index.html)**](/content/blog/parallel-search-mcp-in-devin/index.html)

[Tags:](/content/blog/parallel-search-mcp-in-devin/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[Jul 28, 2025\\
**\- \[Introducing Tool Calling via MCP Servers\](/content/blog/mcp-tool-calling/index.html)**](/content/blog/mcp-tool-calling/index.html)

[Tags:](/content/blog/mcp-tool-calling/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[Jul 14, 2025\\
**\- \[Introducing the Parallel Search MCP Server \](/content/blog/search-mcp-server/index.html)**](/content/blog/search-mcp-server/index.html)

[Tags:](/content/blog/search-mcp-server/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[Jul 8, 2025\\
**\- \[Introducing Source Policy\](/content/blog/source-policy/index.html)**](/content/blog/source-policy/index.html)

[Tags:](/content/blog/source-policy/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[Jul 2, 2025\\
**\- \[The Parallel Task Group API\](/content/blog/task-group-api/index.html)**](/content/blog/task-group-api/index.html)

[Tags:](/content/blog/task-group-api/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[Jun 17, 2025\\
**\- \[State of the Art Deep Research APIs\](/content/blog/deep-research-browsecomp/index.html)**](/content/blog/deep-research-browsecomp/index.html)

[Tags:](/content/blog/deep-research-browsecomp/index.html) [Benchmarks](/content/blog?tag=benchmarks/index.html)

Author: By Parallel

[Jun 10, 2025\\
**\- \[Parallel Search API is now available in alpha\](/content/blog/search-api-alpha/index.html)**](/content/blog/search-api-alpha/index.html)

[Tags:](/content/blog/search-api-alpha/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[May 29, 2025\\
**\- \[Introducing the Parallel Chat API \](/content/blog/chat-api/index.html)**](/content/blog/chat-api/index.html)

[Tags:](/content/blog/chat-api/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[May 16, 2025\\
**\- \[Introducing Basis with Calibrated Confidences \](/content/blog/introducing-basis-with-calibrated-confidences/index.html)**](/content/blog/introducing-basis-with-calibrated-confidences/index.html)

[Tags:](/content/blog/introducing-basis-with-calibrated-confidences/index.html) [Product](/content/blog?tag=product/index.html)

Author: By Parallel

[Apr 24, 2025\\
**\- \[Introducing the Parallel Task API\](/content/blog/parallel-task-api/index.html)**](/content/blog/parallel-task-api/index.html)

[Tags:](/content/blog/parallel-task-api/index.html) [Product](/content/blog?tag=product/index.html),[Benchmarks](/content/blog?tag=benchmarks/index.html)

Author: By Parallel