Parallel
About[About](https://parallel.ai/about)Pricing[Pricing](https://parallel.ai/pricing)Careers[Careers](https://jobs.ashbyhq.com/parallel)Blog[Blog](https://parallel.ai/blog)Docs[Docs](https://docs.parallel.ai/home)
[Start Building]
[Menu]

# How Day AI merges private and public data for business intelligence

Day AI is an AI-native CRM that you can talk to. Their platform combines data from leading SaaS tools like Slack and email, with public data gathered and structured via Parallel’s Task API to help their customers sell better. With Parallel’s web search technology backing it, Day can provide superior visibility of insights across private and public data for a more holistic view of sales opportunities.

Tags:Case Study
Reading time: 4 min
How Day AI merges private and public data for business intelligence
Illustration demonstrating deep research API concepts, web search capabilities, or AI agent integration features
![](https://cdn.sanity.io/images/5hzduz3y/production/b2aefd44b37f08f8d70a73db114f9f9a9c3314ec-3542x2142.png)

## **The isolated data problem**

Internal business systems lack external context. A Slack approval message exists separately from web data showing that person's decision-making authority. Meeting notes about Q4 priorities don't reflect the company's publicly announced strategy. Customer conversations happen without knowledge of recent leadership changes or competitive moves.

"Nobody has ever had a system that woke up in the morning and said, 'I know why these deals are stuck— let's do something about it,'" says Christopher O'Donnell, co-founder of Day AI. "The magic happens when you combine what's being said privately with what's happening publicly."

## **Three levels of web intelligence**

Day AI identified three capability levels that determine whether public data can enhance private systems:

**Level 1: Basic extraction** – Extracting company names from domains using simple parsing or basic AI prompts.

**Level 2: Structured research **– Determining facts like SOC2 compliance requires navigating sites, checking search indexes, and interpreting findings.

_"You might need to decide where to look. You might need to see what capabilities you have to discover the site."_

**Level 3: Advanced reasoning** – Multi-step research that builds contextual narratives from multiple sources. For example, if selling SOC2 compliance, the system determines the market narrative for why a specific company needs SOC2, who they're selling to based on case studies, relevant customer testimonials, LinkedIn posts, and knowledge base documentation.

"Triangulating that level of reasoning data while also natively moving around the web—being able to do both of those things—that is still uncommon". This level is where Parallel's capabilities are on full display.

Illustration demonstrating deep research API concepts, web search capabilities, or AI agent integration features
![](https://cdn.sanity.io/images/5hzduz3y/production/e2f4414523a33085e839909f03e68f17796288be-3504x2104.png)

## **Technical architecture**

Day built what they describe as "a cube of sources, reasoning and versions" rather than simple key-value pairs.

**Global pre-processing with selective computation** – Day pre-processes organization data globally but performs selective, on-demand processing for specific queries. They store computed values like SOC2 sales narratives as custom properties while maintaining flexibility for real-time research.

**Version control with citation preservation** – Every data point maintains complete history. If someone updates information based on a phone call, that human input takes precedence, but the system preserves all versions with source citations.

"We need to be able to store all of those versions of the data and include all of the references to why they are what they are."

**Semantic data beyond structured fields** – Day captures semantically rich information like company values, mission statements, and marketing promises.

**Multi-source synthesis at query time** – When new data arrives (like a meeting recording), the system re-evaluates context across sources.

**LLM-optimized storage** – The data structure is designed for LLM traversal and comprehension. Standard fields like "goals and KPIs for folks in this opportunity" combine web research with meeting recordings, Slack messages, and emails.

## **Implementation example: Email generation**

When composing an email, the system analyzes the recipient company's public web presence—their stated values of "directness, accuracy, factual transparency"—and adjusts communication style automatically.

_“Fresh web context helps our users better understand their prospects and customers, and ultimately makes it easier to tune the best way to communicate with them."_

Illustration demonstrating deep research API concepts, web search capabilities, or AI agent integration features
![](https://cdn.sanity.io/images/5hzduz3y/production/943d873d0a0bdf7c55c21acc7eaf4e156f5f1933-3542x2142.png)

The data pipeline:

  1. Parallel researches and extracts company values, mission, brand voice
  2. Day stores these as semantic attributes in their multi-dimensional structure
  3. When generating communications, the LLM accesses this context
  4. Tone adjusts automatically without user configuration

This semantic data exists alongside structured fields, citations, and version history—all queryable by humans and AI systems. Users can see why any field contains specific values, when it was populated, and what sources were cited.

## **Results**

By integrating Parallel as their web intelligence infrastructure, Day built a system where private and public data streams merge into unified intelligence that can reason, explain, and act. The combination of multi-dimensional data storage, version-controlled citations, semantic enrichment, and LLM-optimized structures demonstrates how businesses can architect systems that leverage the full spectrum of available information.

"The magic of Day AI is it's doing this stuff without you even necessarily knowing and having those connections, and having them all just work."

Parallel avatar

By Parallel

October 8, 2025

## Related Posts24

Full Basis framework for all Task API processors
Parallel avatar

- [Full Basis framework for all Task API processors](https://parallel.ai/blog/full-basis-framework-for-task-api)

Tags:Product Release
Reading time: 2 min
Building a real-time streaming task manager with Parallel
Parallel avatar

- [Building a real-time streaming task manager with Parallel](https://parallel.ai/blog/cookbook-sse-task-manager-with-parallel)

Tags:Cookbook
Reading time: 5 min
How Gumloop built a new AI automation framework with web intelligence as a core node
Parallel avatar

- [How Gumloop built a new AI automation framework with web intelligence as a core node](https://parallel.ai/blog/case-study-gumloop)

Tags:Case Study
Reading time: 3 min
Introducing the TypeScript SDK
Parallel avatar

- [Introducing the TypeScript SDK](https://parallel.ai/blog/typescript-sdk)

Tags:Product Release
Reading time: 1 min
Building a serverless competitive intelligence platform with MCP + Task API
Parallel avatar

- [Building a serverless competitive intelligence platform with MCP + Task API](https://parallel.ai/blog/cookbook-competitor-research-with-reddit-mcp)

Tags:Cookbook
Reading time: 6 min
Introducing Parallel Deep Research reports
Parallel avatar

- [Introducing Parallel Deep Research reports](https://parallel.ai/blog/deep-research-reports)

Tags:Product Release
Reading time: 2 min
A new pareto-frontier for Deep Research price-performance
Parallel avatar

- [A new pareto-frontier for Deep Research price-performance](https://parallel.ai/blog/deep-research-benchmarks)

Tags:Benchmarks
Reading time: 4 min
Building a Full-Stack Search Agent with Parallel and Cerebras
Parallel avatar

- [Building a Full-Stack Search Agent with Parallel and Cerebras](https://parallel.ai/blog/cookbook-search-agent)

Tags:Cookbook
Reading time: 5 min
Webhooks for the Parallel Task API
Parallel avatar

- [Webhooks for the Parallel Task API](https://parallel.ai/blog/webhooks)

Tags:Product Release
Reading time: 2 min
Introducing Parallel: Web Search Infrastructure for AIs
Parallel avatar

- [Introducing Parallel: Web Search Infrastructure for AIs ](https://parallel.ai/blog/introducing-parallel)

Tags:Benchmarks,Product Release
Reading time: 6 min
Introducing SSE for Task Runs
Parallel avatar

- [Introducing SSE for Task Runs](https://parallel.ai/blog/sse-for-tasks)

Tags:Product Release
Reading time: 2 min
A new line of advanced processors: Ultra2x, Ultra4x, and Ultra8x
Parallel avatar

- [A new line of advanced processors: Ultra2x, Ultra4x, and Ultra8x ](https://parallel.ai/blog/new-advanced-processors)

Tags:Product Release
Reading time: 2 min
Introducing Auto Mode for the Parallel Task API
Parallel avatar

- [Introducing Auto Mode for the Parallel Task API](https://parallel.ai/blog/task-api-auto-mode)

Tags:Product Release
Reading time: 1 min
A linear dithering of a search interface for agents
Parallel avatar

- [A state-of-the-art search API purpose-built for agents](https://parallel.ai/blog/search-api-benchmark)

Tags:Benchmarks
Reading time: 3 min
Parallel Search MCP Server in Devin
Parallel avatar

- [Parallel Search MCP Server in Devin](https://parallel.ai/blog/parallel-search-mcp-in-devin)

Tags:Product Release
Reading time: 2 min
Introducing Tool Calling via MCP Servers
Parallel avatar

- [Introducing Tool Calling via MCP Servers](https://parallel.ai/blog/mcp-tool-calling)

Tags:Product Release
Reading time: 2 min
Introducing the Parallel Search MCP Server
Parallel avatar

- [Introducing the Parallel Search MCP Server ](https://parallel.ai/blog/search-mcp-server)

Tags:Product Release
Reading time: 2 min
Starting today, Source Policy is available for both the Parallel Task API and Search API - giving you granular control over which sources your AI agents access and how results are prioritized.
Parallel avatar

- [Introducing Source Policy](https://parallel.ai/blog/source-policy)

Tags:Product Release
Reading time: 1 min
The Parallel Task Group API
Parallel avatar

- [The Parallel Task Group API](https://parallel.ai/blog/task-group-api)

Tags:Product Release
Reading time: 1 min
State of the Art Deep Research APIs
Parallel avatar

- [State of the Art Deep Research APIs](https://parallel.ai/blog/deep-research)

Tags:Benchmarks
Reading time: 3 min
Introducing the Parallel Search API
Parallel avatar

- [Introducing the Parallel Search API ](https://parallel.ai/blog/parallel-search-api)

Tags:Product Release
Reading time: 2 min
Introducing the Parallel Chat API - a low latency web research API for web based LLM completions. The Parallel Chat API returns completions in text and structured JSON format, and is OpenAI Chat Completions compatible.
Parallel avatar

- [Introducing the Parallel Chat API ](https://parallel.ai/blog/chat-api)

Tags:Product Release
Reading time: 1 min
Parallel Web Systems introduces Basis with calibrated confidences - a new verification framework for AI web research and search API outputs that sets a new industry standard for transparent and reliable deep research.
Parallel avatar

- [Introducing Basis with Calibrated Confidences ](https://parallel.ai/blog/introducing-basis-with-calibrated-confidences)

Tags:Product Release
Reading time: 4 min
The Parallel Task API is a state-of-the-art system for automated web research that delivers the highest accuracy at every price point.
Parallel avatar

- [Introducing the Parallel Task API](https://parallel.ai/blog/parallel-task-api)

Tags:Product Release,Benchmarks
Reading time: 4 min
![Company Logo](https://parallel.ai/parallel-logo-540.png)

Contact

  • hello@parallel.ai[hello@parallel.ai](mailto:hello@parallel.ai)

Resources

  • About[About](https://parallel.ai/about)
  • Pricing[Pricing](https://parallel.ai/pricing)
  • Docs[Docs](https://docs.parallel.ai)
  • Status[Status](https://status.parallel.ai/)
  • Blog[Blog](https://parallel.ai/blog)
  • Changelog[Changelog](https://docs.parallel.ai/resources/changelog)
  • Careers[Careers](https://jobs.ashbyhq.com/parallel)

Info

  • Terms[Terms](https://parallel.ai/terms-of-service)
  • Privacy[Privacy](https://parallel.ai/privacy-policy)
  • Trust Center[Trust Center](https://trust.parallel.ai/)
![SOC 2 Compliant](https://parallel.ai/soc2.svg)
LinkedIn[LinkedIn](https://www.linkedin.com/company/parallel-web/about/)Twitter[Twitter](https://x.com/p0)

Parallel Web Systems Inc. 2025