AI Job Tower:explore talent market insights through conversation.
AI Job Tower turns job postings into a continuously updated labour market intelligence platform: skill premiums, trends and company signals to inform staffing, upskilling and business development.
New snapshots refresh the data foundation
Start with the demand for your available skills.
Explore skill demand, then narrow the analysis to roles, locations and companies that match your team.
Read demand in context.
Job postings per 100 observed applications
Historical six-skill excerpt. A ratio of postings to applications, not a measure of candidate quality.
Build learning priorities around market signals.
Combine posting volumes with pay differences and changes across snapshots. Then compare the findings with your team’s skills.
See what companies ask for.
Postings mentioning each skill in the supplied excerpt
Historical six-skill excerpt. Skills can co-occur in the same posting; counts should not be summed.
Turn hiring signals into a relevant conversation.
Connect the technologies companies request with your capabilities and relevant project references. Use the evidence to shape the next outreach.
Make your expertise timely.
Illustrative matches between skills and project themes
A relevant monitoring or platform project.
A relevant backend or integration project.
A relevant delivery or cloud operations project.
Examples of positioning. No company targeting or outreach is performed by this demonstration.
The next opportunities.Are already in the data.
Which skills should you offer today? To which companies? And what should your people learn next? Job postings contain signals about technologies, roles, compensation and business needs. AI Job Tower was conceived as a European labour market intelligence platform that turns those signals into staffing, learning and business development decisions.
- Agentic AI
- MCP
- Skill Premium
- Time & Materials
Market intelligence you can talk to.
At the heart of the project is a dataset refreshed over time and connected to AI through MCP, the Model Context Protocol. Users can converse with processed job postings, combine dimensions and refine an analysis from the wider market down to a technology, role or group of companies.
Collect market signals over time
Successive snapshots of company job postings on LinkedIn feed the collection. Dates, companies, locations and source references provide context for analysis and comparisons across periods.
Extract value from job postings
AI structures descriptions and requirements: job family, role, seniority, technologies, programming languages, certifications, experience and compensation when disclosed. Text becomes a foundation for databases, dashboards and AI tools.
Converse with a changing dataset
The MCP exposes query and analysis tools over the refreshed dataset. The assistant can explore data, compare segments and follow up on a question without starting again from a static export.
Postings and successive snapshots
AI extraction · Database · MCP
Conversations, analysis and decisions
From job titles.To a map of skills.
The model connects what companies need with the context in which they need it. The same skill can carry different significance across roles, seniority levels, locations and periods.
The dimensions of market intelligence.
Skills and technologies
Technical and soft skills, programming languages, AI tools and required proficiency levels.
- Python · Rust · gRPC
- Frameworks and certifications
Roles and seniority
Hierarchical title classification, job family, function, experience and seniority.
- Job family · Detailed role
- Junior, senior, lead
Companies and context
Company, industry, location, working arrangements and references to the posting.
- Remote · Hybrid · On-site
- Observed postings and applications
Compensation and time
Disclosed compensation, benefits and snapshot dates to explore differences and changes.
- Salary where available
- Snapshot comparisons
A continuous flow.From source to conversation.
Refreshing the data gives subsequent analyses new information.
Each snapshot adds context.
Company postings feed a collection that evolves over time. The objective is to expand observation across the European market.
- Dated snapshots
- Sources and companies
A collection of observations to work with.
Text becomes structure.
AI extracts requirements and classifies titles into families, roles and seniority levels. Available dimensions become comparable.
- Role classification
- Requirements and skills
- Disclosed conditions
Data for databases, dashboards and AI platforms.
A connection to refreshed data.
Tools exposed through MCP let the assistant query the refreshed dataset and explore follow-up questions.
- Queries and filters
- Cross-dimensional analysis
- Conversation
Business questions translated into data analysis.
From signals to priorities.
Analyses inform staffing proposals, learning investments and the identification of commercial opportunities.
- Time & Materials
- Upskilling
- Business development
A shared market perspective to decide where to invest.
Three Skill Premiums.More ways to read demand.
Indicators that connect skills, compensation and observed demand within the segment being analysed.
Skill Premium Pay
Explore compensation differences associated with skills in the available data.
- The question
- Which skills are associated with higher pay within a given role?
- The context
- Role, seniority, location, currency and pay period support meaningful comparisons. Undisclosed salaries remain missing.
Skill Premium Time
Bring the time dimension into the analysis of skills demand.
- The question
- How does demand for a skill change across successive snapshots?
- The context
- Dates, observation periods and sample composition provide the context for exploring changes in demand.
Skill Premium Job Postings
Measure the presence of skills in job postings and relate it to other available signals.
- The question
- How often is this skill requested? How does it compare with others?
- The context
- Posting counts, differences from a reference and ratios to observed applications offer complementary perspectives.
From intelligence.To commercial action.
The analyses help identify skills to offer on a time-and-materials basis, companies with relevant needs and learning priorities for the team. Signals can also inform more relevant case-based marketing by connecting capabilities and past projects to observable business needs. The development path includes automations, including email workflows, driven by processed data.
Analyses describe the collected postings, not the entire European market. Observed differences do not establish causation; dates, coverage and data availability remain part of the interpretation.
Your next analysis.Starts with a question.
- Which companies are looking for the skills of our available consultants?
- Which skills should we develop to respond to emerging demand?
- How does disclosed pay differ when this technology is requested?
- Which companies could benefit from a relevant example of our work?
The value,for decision-makers.
- A collection of structured postings that can be refreshed and queried by AI through MCP.
- Cross-dimensional analysis of skills, roles, companies and observed market conditions.
- A shared foundation for T&M staffing, upskilling and business development.
The skills you have.The market you can reach.
Connect your data to your next decisions about people and opportunities.
Tell us about your project
AI estimation of engineering hours.
A dedicated database and an agentic system to estimate engineering effort for EPC projects, drawing on the MDR and experience from previous projects.