Zeer GoldenSource / intent / goldensource

GoldenSource

251-1K employees·New York, United States·thegoldensource.com

Observed, not inferred · updated weekly

Buying intent

23 tracked signals | Top 15 topics are below | Product and Support are carrying most of it.

21signals · 30 days
17topics tracked
50%attributed to a team

Attention by team

LinkedIn activity, by team

Where GoldenSource'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
Generative AI
Cloud Computing
Amazon Web Services (AWS)
Serverless Computing
Data Model
Product
Medium50% of team Product to Artificial Intelligence: Medium, 50% of this team's signals
Product to Generative AI: no signal
Product to Cloud Computing: no signal
Product to Amazon Web Services (AWS): no signal
Product to Serverless Computing: no signal
Medium50% of team Product to Data Model: Medium, 50% of this team's signals
Support
Medium50% of team Support to Artificial Intelligence: Medium, 50% of this team's signals
Medium50% of team Support to Generative AI: Medium, 50% of this team's signals
Support to Cloud Computing: no signal
Support to Amazon Web Services (AWS): no signal
Support to Serverless Computing: no signal
Support to Data Model: no signal
Engineering
Engineering to Artificial Intelligence: no signal
Engineering to Generative AI: no signal
Medium100% of team Engineering to Cloud Computing: Medium, 100% of this team's signals
Engineering to Amazon Web Services (AWS): no signal
Engineering to Serverless Computing: no signal
Engineering to Data Model: no signal
Sales
Medium100% of team Sales to Artificial Intelligence: Medium, 100% of this team's signals
Sales to Generative AI: no signal
Sales to Cloud Computing: no signal
Sales to Amazon Web Services (AWS): no signal
Sales to Serverless Computing: no signal
Sales to Data Model: no signal
Others
High33% of team Others to Artificial Intelligence: High, 33% of this team's signals
Medium17% of team Others to Generative AI: Medium, 17% of this team's signals
Medium17% of team Others to Cloud Computing: Medium, 17% of this team's signals
Medium17% of team Others to Amazon Web Services (AWS): Medium, 17% of this team's signals
Medium17% of team Others to Serverless Computing: Medium, 17% of this team's signals
Others to Data Model: 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
7d ago
Generative AI
LinkedIn
Medium volume
97%
last
6d ago
Cloud Computing
LinkedIn
Medium volume
97%
last
6d ago
Amazon Web Services (AWS)
LinkedIn
Low volume
98%
last
6d ago
Serverless Computing
LinkedIn
Low volume
98%
last
6d ago
Data Model
LinkedIn
Low volume
98%
last
7d ago
Financial Services
LinkedIn
Low volume
96%
last
27d ago
AWS Lambda
LinkedIn
Low volume
98%
last
6d ago
AI Content Generation
LinkedIn
Low volume
94%
last
6d ago
Career Development
LinkedIn
Low volume
98%
last
29d ago
Software Development
LinkedIn
Low volume
98%
last
29d ago
Hiring
LinkedIn
Low volume
98%
last
29d ago
Professional Development
LinkedIn
Low volume
98%
last
29d ago
Root Cause Analysis
LinkedIn
Low volume
98%
last
28d ago
Anthropic
LinkedIn
Low volume
98%
last
27d ago

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Who's active at GoldenSource

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.

Others2 active
researching AI Content Generation
in
Executiveresearching Artificial Intelligence
in
Engineering2 active
researching Cloud Computing
in
researching Root Cause Analysis
in
Sales1 active
Support1 active
researching Anthropic
in
Product1 active
Leadershipresearching Artificial Intelligence
in

Primary products / business lines

LinkedIn company profile

GoldenSource is the Modern Financial Data Management Platform for capital markets, delivered through cloud-native infrastructure designed to ground AI in governed capital markets context. For buy-side firms, GoldenSource serves as an Investment Data Platform, helping clarify what they own, what it is worth and where they are exposed. For sell-side institutions, it serves as a Trading, Risk and R

Top accounts researching GoldenSource

names withheld on the public page

These are companies whose own people brought up GoldenSource 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

GoldenSource's own team shows 5 signals on this topic. No one outside GoldenSource has been seen researching the company by name yet — so this is the market it sits in, not a list of its buyers.

  • Generative AI17,748 cos · 70,837 people
  • Cloud Computing6,216 cos · 25,686 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 GoldenSource.

Company size breakdown
Buyer seniority mix

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Buying committee functions

Employee job titles (LinkedIn)

Engineering — 2 people; Product — 1 person; Sales — 1 person

What's been said

public posts by GoldenSource's team

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

LinkedIn

XVA XVA emerged after the GFC as a set of valuation adjustments applied to the classical risk-neutral valuation framework. OTC options are priced by a) predicting forward prices of the underlying, and b) discounting those forward prices to PV. In the 1970s and 80s, risk-neutral valuation theories were developed which allowed prices of options to be simulated as functions of the underlying. The essence of those theories was the substitution in option formulas and simulation processes of the difficult to estimate underlying price, μ, with the directly observable risk-free rate, r. Once r had replaced μ, the discounting processes in b) above became standardised for all types of derivatives. Applying them meant that prices of derivatives could be easily modelled as discounted asset prices which in turn allowed future derivative prices to be modelled consistently from current market prices. That is, discounted derivative prices could be modelled as martingales. The RNV framework became the foundation for derivative pricing for the next three decades with LIBOR indices acting as proxies for r. In 2008, the GFC disrupted the simplicity of RNV martingale pricing. Inter-bank LIBOR rates became a poor proxies for r and banks and financial markets were forced to fit RNV theory into the reality of their institutional funding frameworks. In the largest, most important derivatives market - the IRD market - a) and b) above had to be split into projection curves and funding curves with OIS rates acting as the benchmark for funding curves. The unrealised gains on derivatives became material credit risks and rates used in funding curves were forced higher where the credit quality of collateral posted was deemed lower. Banks started charging upfront fees (CVA) for the risk of default by derivative counterparties and they started accounting for the expected benefit from their own default (DVA). FVA emerged when they realised the cost of funding collateral posted on unrealised losses was materially higher than the risk-free rate, r, and some banks started charging MVA to counterparties as the pass-through costs of funding initial margin. KVA became the PV of the lifetime opportunity cost of setting aside the capital regulators were now insisting on for credit and market risk. The GFC did not invalidate RNV theory. Instead, it embedded it within the institutional realities of collateral, funding, capital and counterparty credit risk. The term XVA began to be used around 2010-2012 as banks brought the hedging and management of the post-GFC valuation adjustments under centralized XVA desks.

May 2026
LinkedInProject Management

View my verified achievement from Project Management Institute .

May 2026
LinkedIn

I had such a blast at my 20th Carnegie Mellon University reunion this weekend. Coming back as a full-fledged adult gave me a new appreciation for so many things I didn’t notice back when I was on campus. The history, the opportunities, the availability of amenities, and especially the precious time we had with our peers. Being surrounded by so many smart, driven, innovative people all in one place, at that moment in time, is truly a once-in-a-lifetime experience. Much is the same, but so much has changed. New CMU buildings, new restaurants in the surrounding area, and so much new development around Pittsburgh. It was heartwarming to see some beloved spots still around, like Rose Tea Cafe, which used to be one of my favorites. It was also special coming back as the charter class of alpha Kappa Delta Phi International Sorority, Inc. and meeting the active sisters, seeing how the sorority has grown and evolved over the years. The best part of the trip was definitely meeting all the new people and being able to spend real, quality time reconnecting with old friends. It’s fascinating to learn about all the different paths people have taken. So many different careers paths, so many interesting stories. I’m more proud than ever to be an alum of CMU. I’ll definitely be back for the 25th, and I hope to see even more of the Class of 2006 at the next reunion! (And thank you to my amazing husband for taking care of our toddler to make my trip possible!)

Apr 2026
LinkedIn

View my verified achievement from Amazon Web Services (AWS) .

Apr 2026

About this data. GoldenSource (thegoldensource.com). Department attribution is 50%; remaining activity is grouped under Others. Updated 2026-09-25T18:00:10.181Z.

Not yet computedPer-team narrative summaries