AGGKNOWLEDGE
built for the
knowledge economy
AggKnowledge is a purpose-built data management and enrichment service for professional knowledge communities, networks, and panels
Powering
Our Approach
AggKnowledge is revolutionizing the efficiency of knowledge networks and person-record management through robust data enrichment and timely updates, leading to higher match rates and increased revenue
Here are some of our features:
Enrichment-as-a-Service
Start by enriching your existing network and maintain always-on connectivity to push changes and new data as it becomes available
Job Changes
Company Tags
Entity Structure
Competitors
Vendorgraphics
Pharmagraphics
Company: Wayne Enterprises
Location: Gotham City
Website: batman.com
Type: Public Subsidiary
Industry: Defense & Space
Revenue Range: $10B+
Ultimate Parent: Warner Bros. Discovery
Data Cleaning & Mapping
Data mapping and removal of noise and low-value datapoints related to knowledge and experience. Datasets are mapped to industry-standard taxonomies and hierarchies
Lead Sourcing
for Customers & Users
or for Executive Movement
Aside from identifying candidates in our client's databases, AggKnowledge can also surface suggested leads based on specified criteria using all our fields (including Vendorgraphics) and keep tabs on industries by surfacing new or recently-departed executives.
Product, Service, Vendor
Customer Companies
Customer Roles
Search for products, services, vendors, and categories across various detection types
Build a list of customer companies and narrow using AggKnowledge firmographic data such as company size, stage, industry
Vendorgraphics are mapped to individual role and sub-role taxonomies and classifications
New Profiles
Enrichment
Industry
Executive Movements
Track entire industries for mid and senior executive transition
Set departure or arrival criteria
Search across over 700M profiles, return a list of potential individual profiles
Enrich selected profiles with full depth of AggKnowledge data
Data
We evaluate third-party data providers so you don't have to. We have active agreements with leading providers across various methodologies, in addition to running our own proprietary models, bringing you enrichment accuracy and timely updates
Career History
Job Titles
Roles & Functions
Board Positions
Locations​
Education
Date Last Checked
Entity Structure
Ownership
Public Tickers
Company Descriptions
Sectors & Industries
Competitors
Headcount
Revenue
Location
Technologies Used by Entity​
Technologies Known by Role
Customers of Technologies
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Web Detections, Customer Reviews,
Job Postings
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Expanding beyond technologies used (technographics)
Pharmagraphics
NPI or equivalent number
Procedures
Diagnoses
Prescriptions
Publications
Clinical Trials
AGGKNOWLEDGE
Our Data
We aggregate publicly available data and run our own models using the latest LLMs to create associations and deeper insights into people, companies, experiences, and vendors
The Future of Knowledge Network Data Management
Status Quo
Manual data entry and UI-based data transfer
Static records
Lengthy vendor evaluation
Integration complexities
Piecemeal approach
Expensive datasets
AggKnowledge
APIs and Webhooks
Dynamic, automatically updated records
Vendor evaluation complete (and always ongoing)
One consolidated pipeline
All datasets combined and mapped
Lower data price with single licensing agreement
Client Impact
We support leading global firms managing knowledge communities and professional panels by increasing match rates, leading to higher revenue and reduced costs
Automate career history updates, so you know who to reach out to
Identify likely vendors of products/services
Company descriptions, industries, corporate ownership, and entity structure
Rediscover experts already in network and refocus recruiting efforts
Deliver experts more quickly to clients
Recruit departed executives first instead of relying on industry digests
Expert Networks
Survey Panels
Automate career history updates, so you know who to reach out to
Identify likely vendors of products/services
Target the right pool of respondents at the onset
Rewrite the narrative on fraud & embellishment
Deliver respondents confidently to clients
Recruitment Firms
Automate career history updates, so you know who to reach out to
Filter by experience (i.e. CRO at vertical SaaS company during Series C)
Identify vendors and products/services integrations led (i.e. Oracle ERP, Salesforce)
Subscribe to executive updates to be the first to check in on team hires
Track candidates moving to prospective client accounts
Alumni Networks
Automate career history updates, so you know where people work
Program industry-specific and function-specific events
Pair current students with alumni for mentorship opportunities
Get your alumni base excited to give again
About
AggKnowledge was founded in 2023 to improve the relationship between experts and various knowledge networks. The team is building efficient ways to improve matchmaking for professional knowledge and skill-based opportunities. AggKnowledge is led by a team with unique qualifications and deep subject matter expertise in expert-based research and data.
Dan Entrup
Co-Founder
Former AlphaSights and GLG. Previously SVP, Partnerships at FactSet where he developed strategies and led initiatives for content and data acquisition, product partnerships, and go-to-market channel relationships.
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Since then, he’s been advising data and insights companies on overall strategy, go-to-market, and partnerships.
Bill Ronkoski
Co-Founder
Former Managing Director, Divisional Head of Sales at GLG where he consistently exceeded growth targets of 10%+ and was a magnet for talent. Previously the Account Manager for GLG’s largest client.
Since then, he’s been advising early-stage fractional work companies on market entry.
Jon Zajac
Co-Founder & CTO
Former Engineering Leader at HubSpot and Teamshares. He led HubSpot's FinTech payments team, which handled >$1.6B annually, and led the direct debit initiative, a strategic play to expand the EU customer base and reduce credit card transaction fees.
Since then, he's been working on novel ways to apply LLMs to data problems.
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