Zeer Avaloq / intent / avaloq

Avaloq

1001-5000 employees·Zurich, Switzerland·avaloq.com

Observed, not inferred · updated weekly

Buying intent

72 tracked signals | Top 15 topics are below | Engineering and Operations are carrying most of it.

41signals · 30 days
46topics tracked
57.69%attributed to a team

Attention by team

LinkedIn activity, by team

Where Avaloq'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
Wealth Management
Hiring
Data Engineering
Kubernetes
Software-Defined Infrastructure
Engineering
High36% of team Engineering to Artificial Intelligence: High, 36% of this team's signals
Engineering to Wealth Management: no signal
Medium18% of team Engineering to Hiring: Medium, 18% of this team's signals
High27% of team Engineering to Data Engineering: High, 27% of this team's signals
Low9% of team Engineering to Kubernetes: Low, 9% of this team's signals
Low9% of team Engineering to Software-Defined Infrastructure: Low, 9% of this team's signals
Operations
Low50% of team Operations to Artificial Intelligence: Low, 50% of this team's signals
Low50% of team Operations to Wealth Management: Low, 50% of this team's signals
Operations to Hiring: no signal
Operations to Data Engineering: no signal
Operations to Kubernetes: no signal
Operations to Software-Defined Infrastructure: no signal
Product
Medium100% of team Product to Artificial Intelligence: Medium, 100% of this team's signals
Product to Wealth Management: no signal
Product to Hiring: no signal
Product to Data Engineering: no signal
Product to Kubernetes: no signal
Product to Software-Defined Infrastructure: no signal
Others
High36% of team Others to Artificial Intelligence: High, 36% of this team's signals
High27% of team Others to Wealth Management: High, 27% of this team's signals
Medium18% of team Others to Hiring: Medium, 18% of this team's signals
Others to Data Engineering: no signal
Low9% of team Others to Kubernetes: Low, 9% of this team's signals
Low9% of team Others to Software-Defined Infrastructure: Low, 9% of this team's signals
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
5d ago
Wealth Management
LinkedIn
Medium volume
94%
last
5d ago
Hiring
LinkedIn
Medium volume
97%
last
22d ago
Data Engineering
LinkedIn
Low volume
96%
last
15d ago
Kubernetes
LinkedIn
Low volume
92%
last
22d ago
Software-Defined Infrastructure
LinkedIn
Low volume
94%
last
15d ago
Key Account Management
LinkedIn
Low volume
98%
last
30d ago
Financial Services
LinkedIn
Low volume
98%
last
19d ago
HashiCorp Terraform
LinkedIn
Low volume
95%
last
15d ago
Infrastructure as code (IaC)
LinkedIn
Low volume
98%
last
15d ago
Big Data
LinkedIn
Low volume
96%
last
15d ago
Digital Transformation
LinkedIn
Low volume
98%
last
14d ago
AI Agent Software
LinkedIn
Low volume
92%
last
26d ago
Retrieval-Augmented Generation (RAG)
LinkedIn
Low volume
98%
last
26d ago
Warehouse Picking
LinkedIn
Low volume
98%
last
15d ago

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Who's active at Avaloq

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.

Others8 active
Leadershipresearching Hiring
in
researching Wealth Management
in
researching Data transformation
in
Senior ICresearching Career Development
in
researching Digital Transformation
in
researching Artificial Intelligence
in
researching Management Development
in
Engineering5 active
researching Data Engineering
in
researching Cloud Infrastructure Automation Software
in
Software Engineer
researching AI Agent Software
in
Senior Site Reliability Engineer
researching Hiring
in
Cloud Engineer
researching Hiring
in
+ 3 more in Engineering
Product2 active
technical program manager
researching Artificial Intelligence
in
Executive Leader Avaloq - Software Testing and Quality, Data Management, Product Manager
Leadershipresearching Artificial Intelligence
in
+ 2 more in Product
Operations1 active
Head of Service Delivery Management - Securities Operations
Leadershipresearching Artificial Intelligence
in
+ 1 more in Operations
Marketing1 active
Senior Regional Marketing and Communication Manager
researching Key Account Management
in
+ 1 more in Marketing

See everyone, not just the first 10

17 people across every department at Avaloq, plus a LinkedIn profile link for each.

Primary products / business lines

LinkedIn company profile

Avaloq is a premium provider of front-to-back software and services for over 170 financial institutions around the world. Our clients include private banks, wealth managers and investment managers, as well as retail banks. We develop software that can be deployed flexibly through cloud-based Software as a Service (SaaS) or on-premises, and we offer Banking Operations outsourcing through our Busine

New capability sought

Employee posts (LinkedIn)

HashiCorp Terraform; Infrastructure as code (IaC); Key Account Management; Kubernetes; Software-Defined Infrastructure

Top accounts researching Avaloq

names withheld on the public page

These are companies whose own people brought up Avaloq 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

Avaloq's own team shows 11 signals on this topic. No one outside Avaloq has been seen researching the company by name yet — so this is the market it sits in, not a list of its buyers.

  • Wealth Management7,266 cos · 27,761 people
  • Hiring66,174 cos · 318,882 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 Avaloq.

Company size breakdown
Buyer seniority mix

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Buying committee functions

Employee job titles (LinkedIn)

Engineering — 6 people; Operations — 2 people; Product — 1 person; Marketing — 1 person; HR / Talent — 1 person; IT — 1 person

What's been said

public posts by Avaloq's team

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

LinkedIn

I just earned my certification in Mastering Sales: The Digital and AI Toolkit for Success from Kellogg Executive Education ! This program was transformative because it replaces accidental success with deliberate discipline. I’ve sharpened my approach to strategic partnerships by using structured frameworks that ensure every interaction is high-impact and outcome-driven. By systematizing my preparation and mastering strategic inquiry, I now deliver a consistent, future-ready brand promise that my partners and clients can rely on every time. A sincere thank you to Craig Wortmann for the world-class frameworks, and to Katie C. Kelley and Richard Elliott for their exceptional leadership and guidance throughout this journey. #KelloggExecutiveEducation #SalesExcellence #StrategicPartnerships #Leadership #ContinuousLearning

May 2026
LinkedIn

For months I thought AI models and AI agents were the same thing. They're not. ❌ Here's the difference — explained the way I wish someone had explained it to me. A model is the trained neural network. Claude Opus 4.7. GPT-5. Gemini. Text in, text out. That's it. It can reason, but it can't *do* anything in the world. An agent is a system built around the model. It adds three things: → Tools — file access, code execution, MCP servers, APIs → A loop — observe results, decide next step, act again → Autonomy — pursue a goal across multiple steps The model is the brain. The agent is the brain plus hands, eyes, and the loop that connects them. Concrete example I work with daily: Claude Code is an agent. When my /tdd command runs, the model (Opus or Sonnet) makes the decisions. The agent reads files, runs tests, edits code, sees what failed, tries again. The same model can power a chatbot or Claude Code. The difference is everything around the model — not the model itself. If you're new to this, here's the framing that I keep in mind: Model = reasoning engine. Agent = reasoning engine + tools + loop + autonomy. #ai #claudecode #agenticworkflow

May 2026
LinkedIn

I'm attending APTITUDE Connect in Berlin on 20–21 April, 2026. Over two days, experts, public institutions, and private organisations from 11 EU Member States and Ukraine come together to review six months of progress and shape the road ahead. Aptitude is a European Large-Scale Pilot co-funded by the European Union, working to test and deploy the EU Digital Identity Wallet in real-life conditions across Europe. The project covers four use cases: Digital Travel Credentials, Tickets & Check-in, Mobile Vehicle Registration Certificate, and Payments & Banking. Learn more: https://lnkd.in/dJ5Ywqf6 #AptitudeEU #EUDIW #EUDigitalIdentityWallet #DigitalEurope

Apr 2026
LinkedInArtificial Intelligence

While using AI tools such as claude by Anthropic specially when just starting up we generally hit a wall for not getting appropriate results. The reason is almost always the same: the prompt and the CLAUDE.md are too generic. Claude isn't a mind reader. It behaves like a human, but it isn't one. Vibes don't scale. Five things that fixed it for me: 1. Be specific. Not "refactor this" — "extract the retry logic into its own class, keep the public API unchanged." 2. Don't assume shared context. Spell out the stack, the constraints, the non-goals. 3. Break big tasks into small ones. One PR's worth of work per prompt, not one sprint's worth. 4. Tell the agent to ask questions on critical tasks instead of assuming. 5. Clear the context between tasks. A stale context window will bloat the focus and accuracy. None of this is magic. It's just writing down what you'd do yourself manually on day one. Follow Boris Cherny for insider tips. #claude #ai #agenticworkflow

Apr 2026
LinkedInArtificial Intelligence

The hardest skill as a software engineer isn't Java, Spring, DSA, or even AI. It's figuring out what to learn next — and in what order. I spent 2 months stuck on this. Tutorials that went nowhere. Roadmaps that assumed I already knew the thing I was trying to learn. Playlists that skipped the fundamentals and jumped straight to Kafka. The best path is to just stop collecting resources. Pick one topic, go deep, ship something small, and move on to the next. Here's the HLD starter list I wish someone had handed me on day one, and an youtube resource : https://lnkd.in/g5PpbRRn #java #hld #systemdesign

Apr 2026
LinkedIn

Very meaningful traction

Apr 2026
LinkedIn

Brand new Opus model is here

Apr 2026
LinkedIn

Proof that great conversations and champagne make a powerful combination! Really lovely to see so many people join us for the Evooq x Backbase evening at Convival Champagne Bar. A heartfelt thanks to everyone who came along. The conversations, the atmosphere, and the mix of familiar and new faces made it a very special evening.

Apr 2026
LinkedIn

We recently kicked off our campaign “Care for a Cuppa” ☕️ We hosted one in the office today! It was a great opportunity to bring people together, start conversations, and highlight the importance of taking a moment to check in with one another. A special mention to Kirsty for bringing along Hettie, officially the cutest guest of the day. If you’re thinking about getting involved, sign up for your own Care for a Cuppa here: https://lnkd.in/ePVGsDJH , every cuppa helps support hospice care and makes a real difference for people and families who need it most.💙

Apr 2026

About this data. Avaloq (avaloq.com). Department attribution is 57.69%; remaining activity is grouped under Others. Updated 2026-09-25T18:00:10.181Z.

Not yet computedPer-team narrative summaries