Zeer CoreLogic / intent / corelogic

CoreLogic

5001-10000 employees·Irvine, California, United States·corelogic.com

Observed, not inferred · updated weekly

Buying intent

91 tracked signals | Top 15 topics are below | Engineering and Support are carrying most of it.

55signals · 30 days
51topics tracked
94.59%attributed to a team

Attention by team

LinkedIn activity, by team

Where CoreLogic's own people are actually spending their attention, by team, by topic. Bands run Low to High against the busiest pairing on this page, and each cell also shows how much of that team's own activity it represents.

Artificial Intelligence
Data Engineering
Machine Learning
Azure Databricks
Generative AI
Big Data
Engineering
High23% of team Engineering to Artificial Intelligence: High, 23% of this team's signals
High23% of team Engineering to Data Engineering: High, 23% of this team's signals
High19% of team Engineering to Machine Learning: High, 19% of this team's signals
High16% of team Engineering to Azure Databricks: High, 16% of this team's signals
Medium10% of team Engineering to Generative AI: Medium, 10% of this team's signals
Medium10% of team Engineering to Big Data: Medium, 10% of this team's signals
Support
Low100% of team Support to Artificial Intelligence: Low, 100% of this team's signals
Support to Data Engineering: no signal
Support to Machine Learning: no signal
Support to Azure Databricks: no signal
Support to Generative AI: no signal
Support to Big Data: no signal
Marketing
Low100% of team Marketing to Artificial Intelligence: Low, 100% of this team's signals
Marketing to Data Engineering: no signal
Marketing to Machine Learning: no signal
Marketing to Azure Databricks: no signal
Marketing to Generative AI: no signal
Marketing to Big Data: no signal
Sales
Low100% of team Sales to Artificial Intelligence: Low, 100% of this team's signals
Sales to Data Engineering: no signal
Sales to Machine Learning: no signal
Sales to Azure Databricks: no signal
Sales to Generative AI: no signal
Sales to Big Data: no signal
Others
Low50% of team Others to Artificial Intelligence: Low, 50% of this team's signals
Others to Data Engineering: no signal
Others to Machine Learning: no signal
Others to Azure Databricks: no signal
Low50% of team Others to Generative AI: Low, 50% of this team's signals
Others to Big Data: no signal
LowMediumHigh·  banded against the busiest pairing on this page

Topics being researched

30-day window

Every tracked topic, ranked by volume, not by our guess at what matters. Confidence is the classifier's own certainty that a signal belongs where we've filed it.

Artificial Intelligence
LinkedIn
High volume
96%
last
2d ago
Data Engineering
LinkedIn
Medium volume
97%
last
10d ago
Machine Learning
LinkedIn
Medium volume
97%
last
5d ago
Azure Databricks
LinkedIn
Medium volume
92%
last
5d ago
Generative AI
LinkedIn
Medium volume
98%
last
6d ago
Big Data
LinkedIn
Low volume
96%
last
10d ago
Apache Spark
LinkedIn
Low volume
98%
last
5d ago
Data Architecture
LinkedIn
Low volume
98%
last
14d ago
Spark SQL
LinkedIn
Low volume
98%
last
5d ago
Real Estate
LinkedIn
Low volume
98%
last
7d ago
Agentic AI System
LinkedIn
Low volume
96%
last
6d ago
Data Analytics
LinkedIn
Low volume
96%
last
20d ago
Business Intelligence Analytics
LinkedIn
Low volume
98%
last
12d ago
Software Development
LinkedIn
Low volume
98%
last
14d ago
Product Analytics Software
LinkedIn
Low volume
98%
last
4d ago

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Who's active at CoreLogic

verified title on file

Titles, seniority and topic straight from each person's own activity, with a LinkedIn link so you can check any of them yourself.

Engineering2 active
researching Business Intelligence Analytics
in
Others2 active
researching Financial Services
in
Operations1 active
researching Air Cargo
in
Support1 active
researching Artificial Intelligence
in
Marketing1 active
Leadershipresearching Artificial Intelligence
in
Sales1 active
researching Artificial Intelligence
in

Primary products / business lines

LinkedIn company profile

Formerly CoreLogic. We accelerate data, insights and workflows across the property ecosystem to enable industry professionals to surpass their ambitions and impact society. With billions of real-time data signals across the life cycle of a property, we unearth hidden risks and transformative opportunities for agents, lenders, carriers and innovators.

Top accounts researching CoreLogic

names withheld on the public page

These are companies whose own people brought up CoreLogic unprompted, not accounts we guessed might be interested. We can't yet tell an implementation partner from a genuine buyer here, names unlock along with the buyer profile below.

129,094 companies · 649,540 people are researching Artificial Intelligence

CoreLogic's own team shows 12 signals on this topic. No one outside CoreLogic has been seen researching the company by name yet — so this is the market it sits in, not a list of its buyers.

  • Data Engineering8,055 cos · 34,411 people
  • Machine Learning7,425 cos · 35,104 people
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Buyer profile

company size · seniority

Company size and how senior the people involved are, the two things that decide whether this is a real deal. Competitor overlap isn't computed yet for this account.

Unlock the buyer profile

Company-size breakdown and buyer seniority mix for accounts researching CoreLogic.

Company size breakdown
Buyer seniority mix

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Buying committee functions

Employee job titles (LinkedIn)

Engineering — 1 person; Marketing — 1 person; Sales — 1 person

What's been said

public posts by CoreLogic's team

No public post naming CoreLogic has surfaced in the past year, so this is what CoreLogic's own team is posting about publicly — their topics, in their words.

LinkedIn

I found this article on leadership and approach to be very insightful -and worth your time to read, re-read (slowly) and ponder. What wake do you leave in your path?

May 2026
LinkedIn

Hope to see you there!

May 2026
LinkedIn

Thank you Mike DelPrete for putting on such a unique and valuable experience with Further 2026. You did an amazing job curating content that cut through the AI hype to focus on solutions that are moving the needle. The breakout sessions and the conversation that they generated were a brilliant addition. Thank you for allowing me to be part of it! John Rogers Devi Mateti Jayme (Poladian) Beck Amy Gromowski Joan Dailey Jason Nicosia Saish Gadamsetty Marc-Antoine Juanéda Charles Reynolds, GISP

May 2026
LinkedIn

We partner with the best. Looking forward to an agentic future.

Apr 2026
LinkedInGoogle Cloud

Congratulations to our engineering, data science, and product teams as Cotality has been named the 2026 Google Cloud Partner of the Year for Data and Analytics. This recognition underscores our commitment to solving the biggest barrier to AI: enabling unmatched "trusted" data to be responsibly delivered to the right place and the right professional at the right time. By delivering AI-ready data natively within Google Cloud Marketplace, we are helping property professionals move from raw information wrangling and uncertainty to confident real-time decisions. We are very fortunate to have had the opportunity to co-innovate with our pioneering clients to launch the Cotality Payoff Analysis Agent. Thank you to our team and Google Cloud for the continued collaboration. Together, we’re transforming the property ecosystem. For more information, click here: https://ctlty.co/4tZTV8f #GoogleCloudPartnerAwards #GoogleCloudNext #AI #DataAnalytics Thomas Kurian #PropTech #Innovation

Apr 2026
LinkedIn

High-performing sales teams aren’t built on pressure. They’re built on clarity and accountability. The best teams I’ve led had 3 things in common: - Everyone knew exactly what “good” looked like - Metrics weren’t debated, they were owned - Coaching was constant, not reactive What doesn’t work: ❌ Changing targets every quarter ❌ Overcomplicating comp plans ❌ Managing only to the number People don’t rise to expectations; they rise to standards + consistency. When you get that right, performance becomes predictable. #SalesLeadership #TeamBuilding #Leadership

Apr 2026
LinkedInIdentity Access Management (IAM)

As a DevOps engineer, I used to think disaster recovery was just "we have backups." Then our primary region went down for 4 hours. Backups existed. Runbooks existed. But nobody had tested failover end-to-end. Recovery took 3x longer than our RTO target. That incident changed how I think about DR architecture on AWS. Here's the breakdown every DevOps engineer should internalize: 𝗧𝗵𝗲 𝟰 𝗗𝗥 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 (and what they actually mean for your on-call rotation): 𝟭. 𝗕𝗮𝗰𝗸𝘂𝗽 & 𝗥𝗲𝘀𝘁𝗼𝗿𝗲 Cheapest. You're rebuilding from scratch during an incident. S3 cross-region replication + CloudFormation to redeploy. RTO: hours. Fine for dev/staging, risky for prod. 𝟮. 𝗣𝗶𝗹𝗼𝘁 𝗟𝗶𝗴𝗵𝘁 Data is live in the recovery region (Aurora global DB, DynamoDB global tables). Infrastructure is defined but not running. You spin it up on failover. RTO: ~30 min if automated well. 𝟯. 𝗪𝗮𝗿𝗺 𝗦𝘁𝗮𝗻𝗱𝗯𝘆 A scaled-down copy of prod is already running. Route 53 health checks trigger failover, then you scale out. RTO: minutes. The sweet spot for most production workloads. 𝟰. 𝗠𝘂𝗹𝘁𝗶-𝗦𝗶𝘁𝗲 𝗔𝗰𝘁𝗶𝘃𝗲/𝗔𝗰𝘁𝗶𝘃𝗲 Both regions serve traffic simultaneously. No failover - traffic just reroutes. Near-zero RTO. Maximum cost and operational complexity. Reserve for mission-critical systems. 𝗪𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝘄𝗮𝘆: → Your DR plan is only as good as your last test. Run game days quarterly. → Automate detection with CloudWatch + EventBridge. Humans are too slow at 3 AM. → IaC isn't optional. If you can't redeploy your infra with one command, your RTO is a lie. → Don't forget DNS, IAM, and edge configs - they're invisible until they break your recovery. → Backups protect against data loss. DR protects against regional failure. You need both. The best time to build your DR architecture was before the last outage. The second best time is today. What DR strategy does your team use? Have you actually tested it end-to-end? #DevOps #AWS #DisasterRecovery #CloudArchitecture #SRE #Resilience #InfrastructureAsCode #WellArchitected

Apr 2026
LinkedInGTM Planning

Most sales teams don’t have a “pipeline problem.” They have a focus problem. After leading teams across multiple orgs, here’s what I’ve seen consistently: → Too many accounts → Too many ICPs → Too many “priorities” The result? Activity goes up… revenue doesn’t. The teams that outperform: • Ruthlessly define their ICP • Align marketing + sales around one motion • Build repeatable plays instead of chasing one-off wins When you simplify the motion, everything changes: ✔ Higher conversion rates ✔ Better forecasting ✔ Faster ramp for new reps Growth isn’t about doing more. It’s about doing less, better. #SalesLeadership #GTM #RevenueGrowth #B2BSaaS

Apr 2026
LinkedIn

I'm attending the 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐂𝐨𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 by Data Science Dojo this April 6–10, 2026. This 𝐅𝐑𝐄𝐄 virtual conference brings together AI professionals and innovators to explore the rapidly evolving world of Agentic AI. 👉Join thousands from around the globe: https://hubs.la/Q046w0yG0 #agenticaiconference #datasciencedojo

Mar 2026

About this data. CoreLogic (corelogic.com). Department attribution is 94.59%; remaining activity is grouped under Others. Updated 2026-09-25T18:00:10.181Z.

Not yet computedPer-team narrative summaries