#Market Intelligence

Market Intelligence is TechWolf's external labour-market data layer. It is built on a continuously refreshed corpus of over two billion public job postings, collected worldwide, and turned into structured market data. Every posting is tagged with the same skills and tasks that TechWolf uses for the organisation's own jobs. That lets an organisation compare itself with the market: which skills the market asks for, which employers are hiring for them, where, and at what pay.

#Data sources

Public job postings

• Market Intelligence is built from public job postings, collected every day.
• Each posting is captured with its employer, job title, location and posting month.
• From the posting text, structured data is extracted: skills, tasks, industry, seniority, contract type, compensation, experience and education requirements, certifications, language, and a normalised job title.

Privacy

• Market Intelligence describes jobs, not people. It never holds data about individual people: no LinkedIn data, resume databases or candidate profiles, and contact details that appear in a posting are removed.
• An organisation's own workforce data is never added to the market data and is never visible to another customer. Comparisons with the organisation's own data happen inside its own tenant.

Coverage

• Market Intelligence is built from more than 2 billion collected job postings, with history back to January 2021.
• The main markets are 22 countries that together make up about three quarters of global GDP:
  • Americas: United States, Canada, Mexico, Brazil, Argentina.
  • Europe, Middle East and Africa: United Kingdom, Germany, France, Netherlands, Ireland, Switzerland, Sweden, Norway, Finland, South Africa.
  • Asia-Pacific: Singapore, Australia, Japan, India, Philippines, Hong Kong, China.
• Depth varies by country. Postings are most plentiful in large, mature job markets and thinner where fewer jobs are advertised online.

#Key attributes

Market Intelligence is organised around a set of connected pieces of data:

• Skills and tasks, extracted from each posting and mapped to the TechWolf skill taxonomy, the same taxonomy that describes the organisation's own workforce.
• Job titles, kept as written and also given a standard title, so that "Registered Nurse" and "RN, Med-Surg" count as the same role, even across languages.
• Pay, taken from postings that state it and converted to one comparable annual amount in local currency, so hourly, weekly and monthly rates can be compared. Posted pay is a market signal, best used alongside salary surveys.
• Locations, standardised to city, region and country where the posting names them, so places can be compared directly.
• Industry, the employer's industry, classified on the international standard (ISIC) for every posting.
• Posting month, on every record, so any question can be limited to a period, such as the last six months or a single quarter.
• Skill trends, how often each skill is asked for in postings and how that share changes over time, which shows the skills that are emerging, growing or declining for a country, industry or role.
• Peer groups, the sets of companies an organisation chooses to compare itself with.

#How it works

• Collect. Public job postings are gathered every day.
• Deduplicate. Copies of the same posting are merged into one record, attributed to the hiring employer.
• Enrich. A fixed set of fields is extracted from each posting: skills, tasks, a standard job title, an industry code, pay and location.
• Publish. The records go live in the dataset that the Analyst Agent and the MCP connector read.

Coverage per country is checked against official labour statistics.

#Availability

Market Intelligence is available through the Analyst Agent and the MCP connector, and the peer-group comparison through the Console, exports and API:

• Agent: the Analyst Agent answers market questions in natural language: demand for a role or skill, what competitors are hiring for, pay, emerging and declining skills, and where talent is being hired. It can scope a question to one of the organisation's peer groups. For example:
  • "What skills are our competitors hiring for?"
  • "We're entering India. Which skills do we lack locally and globally?"
  • "Which banking skills have been emerging over the last three years?"
  • "How does our pay for engineers compare with our closest competitor?"
• MCP connector: the MCP connector brings the same market data into the tools you already work in, such as Claude, ChatGPT and Microsoft Copilot Studio.
• Console, exports and API: the market skill profiles and alignment scores per peer group, inside job skill governance, as exports and through the API.