Draup for Talent

Turn a company search query into company directory filters Interprets a natural-language search query about companies and returns structured filters for the Draup company directory: boolean_response, an entity-keyed tree of or/and/not value lists (for example {"companies": {"or": ["Nvidia Corporation"]}}) to send as response_data to get_universe_companies_v3; synon_list with the Draup values each term was matched or expanded to; range filters such as revenue, employee count, funding amount or year established; a suggested pagination sort and view_name; and an identifier. Use it when you need company-search filter values from a free-text request. An AI model interprets the query together with today's date, the directory's filter vocabulary and the sortable columns available for the caller's product. Terms that do not map to a usable filter come back as free-text keywords. Only current_input is interpreted; the other body fields are accepted but ignored. When the query cannot be interpreted the call still returns HTTP 200, with is_valid false and an error_message instead of filters. Request Examples: Minimal body with only free-text input: { "current_input": "semiconductor companies in Bangalore" } Parse input with project context and different sort field: { "view_name": "Opportunities", "pagination": { "limit": 25, "offset": 0, "sortBy": "exec_movement_flag", "sortType": "DESC nulls last" }, "project_id": 267402, "current_input": "google" } Parse company name for Opportunities view with scoring sort: { "view_name": "Opportunities", "pagination": { "limit": 25, "offset": 0, "sortBy": "talent_oi_score", "sortType": "DESC nulls last" }, "current_input": "Nvidia Corporation" }

build_company_filters_v3

Turn a news search query into Signals filters Interprets a natural-language search query about company news and returns structured Signals filters: a rephrased search phrase, plus any companies, industry verticals, signal categories and saved project lists it recognises, a capture-date range when the query implies one, and synonym records for the matched company names. Use it when you need Signals filter values from a free-text request, then send boolean_response as response_data (and filters as filters) to a Signals search tool such as get_signal_news_v3. An AI model interprets the query, so the same query can give slightly different filters, and only entities enabled for the caller's Signals search are returned. When the query cannot be interpreted, the response has is_valid false and an optional error_message, and carries no filters. Request Examples: Topic query: { "current_input": "labor market news" } Query naming a signal category and a company: { "current_input": "recent deals and partnership news about Microsoft" }

build_news_filters_v3

Get a service provider's top customers, customer verticals, delivery locations and workloads Returns a snapshot of a service provider's outsourcing business, built from the active outsourcing deals in which the company is the vendor: its top customers, the verticals those customers belong to, its main delivery locations and the workloads it delivers. Use it to see who an IT services, engineering services or professional services firm works for and what it delivers. Data exists only when the company's primary vertical is IT Services, Product Engineering Services or Professional Services; for any other company, or a service provider with no active deals, every list is null or left out. Each list holds up to the top three entries: customers by number of active deals with this vendor, customer verticals by number of distinct customers, and delivery locations and workloads by number of deals. Only deals whose customer is visible in the caller's product are counted, for all four lists. A list is null or left out when there is no data for it. A deal is active when it is not flagged inactive and has no end date or one in the future. Each customer's account_id is a Draup account ID. The endpoint takes no filters. An unknown account ID, or one the caller cannot access, returns an empty response with no result.

customers_deals_v3

Get organization data with decreasing workforce for a vertical Returns a list of organizations within the specified vertical/industry that have experienced a decrease in workforce, along with attrition percentages and percentile statistics. Request Examples: Automotive vertical example: { "analysis_entity": "Automotive" }

ecosystem_get_organization_decreasing_workforce

Get organization data for increasing workforce by vertical Retrieves a list of organizations within a specified vertical (analysis_entity) that are experiencing an increasing workforce trend. The response includes workforce counts, attrition percentages, and percentile thresholds (33rd and 67th) to help classify organizations into high, medium, or low categories based on attrition. Request Examples: Automotive vertical example: { "analysis_entity": "Automotive" }

ecosystem_get_organization_increasing_workforce

Get IT Spend Top Metrics Returns the current value and change percentage of IT spend for a specified analysis entity (vertical). Request Examples: Automotive vertical: { "analysis_entity": "Automotive" }

ecosystem_get_vertical_it_spend

Get vertical key priorities Fetches a list of key priorities for a specific vertical or analysis entity. The response includes detailed descriptions, functional categories, and a relevance flag for each priority item. Request Examples: Automotive vertical: { "analysis_entity": "Automotive" }

ecosystem_get_vertical_key_priorities

Get Top Employers for an Analysis Entity Returns a list of top employer organizations for the specified analysis entity (such as a vertical or industry). Each employer includes its account ID, name, logo URL, and employee count. Also returns percentile information for employee counts. Request Examples: Automotive vertical: { "analysis_entity": "Automotive" }

ecosystem_get_vertical_organization_top_employers

Get Top Startups Organization Data Returns a list of top startups for a given analysis entity, with optional filters for sub-verticals or locations. Request Examples: No filters: { "analysis_entity": "Product Engineering Services" } Filter by locations: { "response_data": { "locations": { "AND": [ { "OR": [ [ "Greater Los Angeles Area, United States", "locations", false, true ] ] } ] } }, "analysis_entity": "Aerospace" } Filter by sub-verticals: { "response_data": { "sub_verticals": { "AND": [ { "OR": [ [ "Oil & Gas Upstream", "sub_verticals", false, true ] ] } ] } }, "analysis_entity": "Energy & Utilities" }

ecosystem_get_vertical_organization_top_startups

Get Outsourcing Customers by Vertical Retrieves a list of outsourcing customers for the specified vertical (analysis_entity). The response includes company details and percentile information. Request Examples: Automotive vertical: { "analysis_entity": "Automotive" }

ecosystem_get_vertical_outsourcing_customers

Get Outsourcing Vendors by Vertical Retrieves a list of outsourcing vendors for the specified vertical (analysis_entity). The response includes vendor details and percentile information. Request Examples: Automotive vertical: { "analysis_entity": "Automotive" }

ecosystem_get_vertical_outsourcing_vendors

Get Outsourcing Workloads by Analysis Entity Returns a list of outsourcing workloads and their sizes for a specified analysis entity (vertical/industry). Request Examples: Automotive vertical: { "analysis_entity": "Automotive" }

ecosystem_get_vertical_outsourcing_workloads

Get Top Metrics Revenue Fetches the top metrics revenue for a specified analysis entity (vertical). Request Examples: Automotive vertical: { "analysis_entity": "Automotive" }

ecosystem_get_vertical_revenue

Get Top Metrics R&D Spend Returns the current value and change percentage of R&D spend for a given analysis entity (vertical). Request Examples: Automotive vertical: { "analysis_entity": "Automotive" }

ecosystem_get_vertical_rnd_spend

Get Growing Tech Stack Products Retrieves a list of growing technology stack products for a specified analysis entity (vertical/industry). The response includes product details and percentile statistics. Request Examples: Automotive vertical example: { "analysis_entity": "Automotive" }

ecosystem_get_vertical_techstack_growing

Get Popular Tech Stack Products Retrieves a list of popular technology stack products for a specified analysis entity (vertical/industry). The response includes product details and percentile statistics. Request Examples: Automotive vertical example: { "analysis_entity": "Automotive" }

ecosystem_get_vertical_techstack_popular

Resolve names in a user query to Draup taxonomy values Maps names and phrases in a user query to Draup's canonical taxonomy values, including synonyms, alternate spellings, abbreviations and former names (for example 'Bangalore' resolves to 'Bengaluru Area, India'). Call this first whenever a query mentions a company, location, industry, business function, technology or workload, and pass the returned values exactly as given to other Draup tools, which expect taxonomy values rather than free text. Supported entity types: company, location (cities, metro areas, states, countries), vertical, sub_vertical (industries and sub-industries), business_function, tech_stack, workload. One query can return several entities. If an expected entity is missing from the result, the term was not recognised: retry with just that phrase, or ask the user to clarify. Request Examples: Resolve a location written in a common alternate spelling: { "user_query": "quality of life in bangalore" } Resolve an industry name to its vertical: { "user_query": "key trends of automobile industry" } Resolve a company and a location from one query: { "user_query": "Infosys hiring for data engineers in Pune" }

extract_entities

Get a company's profile overview Returns the profile overview of one company tracked by Draup: name, business description, primary vertical, verticals and sub-verticals, headquarters and HQ street address, employee count, year established, ownership type (Public or Private), vision and mission statements, website and social media links, parent company, and the Draup Opportunity Index score. Use it for a quick factual summary of a company before deeper research. oi_score is Draup's Talent Opportunity Index, a 0-10 score rounded to two decimals that weighs total headcount, R&D workforce, job postings, layoff signals, revenue, revenue growth, executive movement and mergers and acquisitions equally; it is null for companies without an index. Parent company fields are filled only when the parent is itself a Draup account. Fields without data are null or left out. The endpoint takes no filters. An unknown account ID, or one the caller cannot access, returns an empty response with no result.

get_account_details_v3

Get a company's latest financial snapshot Returns the latest financial snapshot of one company tracked by Draup: revenue, IT spend, R&D spend and EBITDA with their year-over-year growth, the financial year they refer to, the ownership type, and private funding details (number of rounds, total raised, date and amount of the last round, latest valuation, latest investor). Use it to size a company or check its recent financial trajectory. The four headline figures are in millions of the company's reporting currency, named in each figure's currency field. avg_conversion_rate is the US-dollar value of one unit of that currency, so multiply by it to get US dollars. A figure's value is null or left out when the figure or the rate is unavailable. Growth fields are percentages versus the previous year. concerned_year is the latest financial year with revenue data. Funding amounts are absolute US-dollar values, not millions, and funding fields are null or left out for companies with no recorded funding rounds, which is typical of public companies. The result holds at most one record. The endpoint takes no filters. An unknown account ID, or one the caller cannot access, returns an empty response with no result.

get_account_financials_v3

Find a company's Draup account ID Looks up companies by name or website in Draup's company directory and returns each match with its Draup account id (`id`, the id that account tools such as get_account_details_v3 take), company name (`key`), logo, a 0 to 1 match `score` and whether the match was AI-suggested. Use it first whenever you have a company name or website and need its account id. A search term that looks like a website domain (for example microsoft.com) is matched against every known company web address, including subsidiary and brand domains, and returns those companies with score 1, up to ten. Otherwise the name goes to Draup's company name-matching service, which keeps companies scoring at least 0.5 and returns at most five, highest score first (direct matches before AI-suggested ones at the same score). Send `type` as "universe". Results are empty when nothing matches or the caller's plan does not include company search. Request Examples: Look up by website: { "type": "universe", "search_key": "microsoft.com" } Look up Microsoft: { "type": "universe", "search_key": "microsoft" }

get_account_id

Get the most widely used technology products a company develops Returns up to 20 technology products developed by the company, ranked by how many companies use them. Use it to see which of a software or technology vendor's products are most widely adopted. Each product has its Draup product ID, name, logo URL and accounts_using, the number of companies whose tech stack includes it; products are sorted by accounts_using, highest first. The result is empty when the company develops no tracked product, which is common for companies that are not technology vendors. The endpoint takes no filters. An unknown account ID, or one the caller cannot access, returns an empty response with no result.

get_account_key_solution_offerings_v3

Get a company's strategic priorities Returns the strategic business priorities Draup has identified for one company. Each priority includes its name and narrative description, business function, the functional workloads it involves, the use cases it applies to (with each use case's description and technology areas), its source (earnings call Transcript and/or news Signal) with the fiscal quarter or publication month, up to six of the company's tech stack products whose workloads overlap the priority plus the total number of overlapping products, and up to six service providers with active outsourcing engagements on those workloads. Use it to learn what a company is investing in or trying to achieve, and which tools and vendors are linked to each initiative. {id} is the company's Draup account ID, as returned by get_account_id or get_universe_companies_v3. Narrow the list with response_data (business_functions, workloads, usecases, technology_areas) or with search_key, which matches part of the priority name. Earnings call (Transcript) priorities come first, latest fiscal quarter first, followed by news (Signal) priorities, latest month first, so an older transcript priority is listed before a recent signal priority. Results come 50 per page by default; use pagination.limit and pagination.offset for more. Fields with no value are left out, and is_competitor is always false for API calls. Request Examples: ER&D priorities: { "response_data": { "business_functions": { "or": [ "ER&D" ] } } } Priorities whose name contains 'service', excluding Legal: { "search_key": "service", "response_data": { "business_functions": { "not": [ "Legal" ] } } } Priorities with AI use cases, first 10: { "pagination": { "limit": 10, "offset": 0 }, "response_data": { "technology_areas": { "or": [ "Artificial Intelligence" ] } } }

get_account_priorities_v3

Get the technology products a company uses Returns the software and technology products Draup has detected in one company's tech stack, one row per product: product name and ID, tech stack category, G2 sub-category, developer company, the workloads the product supports, the countries where its use was evidenced (which can include Remote or Hybrid), first and last evidence dates, a usage index (High, Medium or Low; rarely NA) and a detection confidence score from 0 to 1. Products Draup no longer detects are still listed but have no usage index; use last_evidence_date to judge how current a product is. Rows are sorted by G2 sub-category, then product name. Use it to find which tools and vendors a company uses, or whether it uses a given product or vendor. {id} is the company's Draup account ID, as returned by get_account_id or get_universe_companies_v3. Narrow the list with response_data (tech_stack_categories, tech_stack_sub_categories, tech_stack_developer_companies, workloads, countries). Each product has one category and one sub-category, so use only or / not for those two; "and" on them returns an error. Results come 50 per page by default; use pagination.limit and pagination.offset for more, since large companies can have thousands of products. Fields with no value are left out, and is_competitor is always false for API calls. Request Examples: Security software used in the United States, first 20: { "pagination": { "limit": 20, "offset": 0 }, "response_data": { "countries": { "or": [ "United States of America" ] }, "tech_stack_categories": { "or": [ "Security Software" ] } } } Cybersecurity products not developed by Microsoft: { "response_data": { "workloads": { "or": [ "Cybersecurity" ] }, "tech_stack_developer_companies": { "not": [ "Microsoft" ] } } } Products developed by Microsoft: { "response_data": { "tech_stack_developer_companies": { "or": [ "Microsoft" ] } } }

get_account_tech_stack_products_v3

Get a company's most-outsourced workloads Returns the five workloads a company outsources most, ranked by how many of its active outsourcing deals cover each workload. Use it to see where a company relies most on external service providers. {id} is the company's Draup account id, as returned by get_account_id or get_universe_companies_v3. Only deals that name at least one workload, are flagged active, have no end date or one in the future, and have a vendor that is itself a Draup account are counted. Workload names come from Draup's workload taxonomy, such as Customer Service & Support or Software Testing. The deal counts drive the ranking but are not returned, and ties are ordered by workload name in reverse alphabetical order. The list is empty when the company has no qualifying deal. The request body is optional: widget and filters are accepted but do not change the result. Request Examples: Basic request with no filters: { "widget": "top_outsourced_workloads" }

get_account_top_outsourced_workloads

Compare base pay across 2+ job roles (base pay, compare_roles) Annual base salary for 2-10 job roles side by side: pay_data has one row per role and MSA/country passing the talent-size threshold (median_bp, P10/P90, conf_level, reported_salaries). Use for: which role pays more in a place. Not for: titles or experience curve (get_base_pay_* analyze_roles), one role across locations (compare_locations), talent supply (get_talent_size_*), benefits (get_top_benefits_*), employer pay (get_base_pay_by_companies_*), metros in a country (get_base_pay_by_locations_*), postings with salaries (get_posted_pay_examples_*). Send one MSA or country (several give a row per role x location); regions and states return no rows. Only job_roles and multiple_locations are used. Request Examples: compare_roles: job_roles=Data Scientist,Data Engineer; multiple_locations=United States of America: { "response_data": { "job_roles": { "or": [ "Data Scientist", "Data Engineer" ] }, "multiple_locations": { "or": [ "United States of America" ] } } }

get_base_pay_compare_roles_v3

Core skill filter options for a job role, job family or occupation (any scenario) Suggested skills for the given job roles, job families or occupations in data.results[].value, ordered by skill category (root, core, emerging, new, declining) then job-posting frequency; no counts. Soft skills and skills already in core_skills are left out. Use for: choosing skill names to pass as core_skills to other tools. For experience ranges use get_yoe_filter_options_v3; for industry vertical names use get_vertical_filter_options_v3. Not for: a role's categorised skill profile (get_talent_skills_* or get_talent_skills_by_role); skill name look-up (search_core_skills_v3). The analysis scenario does not change the list and is not sent. Send one of job_roles, job_families or job_occupations (first present wins, in that order); several names under one key are pooled. Names must match Draup's spelling exactly, including case (e.g. as returned by search_job_roles_v3); a misspelt name gives an empty list. Skill-only and location-only scenarios have no role, job family or occupation: there only search_key returns suggestions. Locations, verticals and experience have no effect. With none of the three keys and no search_key, data.results is empty. Send pagination.limit 100 (default 10). Before applying a skill, check show_filters from get_talent_size_*. Request Examples: One job role: { "pagination": { "limit": 100, "offset": 0 }, "response_data": { "job_roles": { "or": [ "Data Scientist" ] } } } One job family, leaving out a skill already chosen: { "pagination": { "limit": 100, "offset": 0 }, "response_data": { "core_skills": { "or": [ "Python (Programming Language)" ] }, "job_families": { "or": [ "Artificial Intelligence & Data Science" ] } } } One occupation: { "pagination": { "limit": 100, "offset": 0 }, "response_data": { "job_occupations": { "or": [ "Software and Mathematics" ] } } }

get_core_skill_filter_options_v3

Get Employers Data Fetches employers data based on provided filters such as location, vertical, and sub-verticals. Returns a list of employers with their account details and percentile information. Request Examples: Location - India: { "widget": "employers_v2", "location": "India", "is_country": "true", "analysis_type": "location" } Vertical - Automotive: { "widget": "employers_v2", "vertical": "Automotive", "analysis_type": "vertical" } Location - Capital District, Colombia: { "widget": "employers_v2", "location": "Capital District, Colombia", "analysis_type": "location" } Location with vertical filter: { "widget": "employers_v2", "filters": { "verticals": [ "media & entertainment" ] }, "location": "Greater Los Angeles Area, United States", "analysis_type": "location" } Location with vertical and sub-vertical filters: { "widget": "employers_v2", "filters": { "verticals": [ "media & entertainment" ], "sub_verticals": [ "film & music" ] }, "location": "Greater Los Angeles Area, United States", "analysis_type": "location" }

get_ecosystem_employers_data

Get Globalization Summary Returns the globalization summary of the company given by `id` for one business function, or site class: current headcount at its sites, the number of site locations and its globalization index score with the change from the previous record. Use it to size an account's footprint for a given function. A site class is required, chosen from IT, R&D, Sales & Marketing, Customer Support & Service, Legal, Procurement and Finance & Accounting, and an option widens headcount and locations to all of the account's sites. Results can also be limited to sites in one city, state or country. Headcount is summed across the matching sites, and the index score is rounded to two decimals and left empty when there is no index data. With a location limit the index is left out, IT and R&D return headcount while other functions return a headcount range, and the location count is kept only when the location is not a city.

get_globalization_summary

Location ecosystem profile of one country or MSA (path scenario analyze_locations) Ecosystem indicators of ONE country or MSA in data.<section>.<attribute>: cost of living, corporate tax, CPI, unemployment, rule of law, political stability and other governance indices, labor freedom, civil liberties, time zone, pollution, crime, travel time to work. Each: value, band (index), change, period, source, definition, up to 5 trend points. The analyze_locations path does not change it. Use for: cost, governance, stability and living conditions of a place. Not for: talent numbers (use get_talent_size_*), universities (use get_top_universities_v3), employers (use get_org_top_employers_*), pay (use get_base_pay_*). Find attributes by name across sections; one place per call. Request Examples: Profile of a country: { "response_data": { "multiple_locations": { "or": [ "United States of America" ] } } }

get_location_ecosystem_analyze_locations_v3

Get a public company's revenue split by business unit Returns the revenue of a public company split by business unit for its latest reported revenue year, with each unit's share of the total and its growth over the previous year. Use it to understand a public company's revenue mix and which units are growing. Each unit with non-zero revenue that year is listed with its amount in the reporting currency as reported (typically millions), the currency code and the year used. percentage_share is the unit's percent of the sum of all units that year; shares total 100 including adjustment lines such as Eliminations, which can be negative. growth_percentage is the percent change from the previous year, null when the unit had no positive revenue in the previous year. Units come in no guaranteed order. The result is empty for private companies, for companies without business-unit data, and when business-unit figures for the latest revenue year are not yet available. The endpoint takes no filters. An unknown account ID, or one the caller cannot access, returns an empty response with no result.

get_revenue_by_business_v3

Get CoPilot News Suggestions Returns up to 20 recent company news signals captured by Draup that best match a plain-language question, each with its headline, source, link, highlights, dates, categories, locations, companies, your helpful vote and its recent helpful-vote count. It powers the Copilot news search on the Signals page. Only the first search phrase is used as the question. Results can be narrowed by signal category, company vertical, company name or Draup company ID, signal location, saved lists and a capture-date window that defaults to the last 90 days and spans at most 365 days. Candidates must pass a minimum similarity score, are reranked, deduplicated by headline and scored 70 percent on relevance and 30 percent on recency, so the list is not in date order. Highlights are an AI summary when available, otherwise article snippets. API-token callers always get an empty AI summary and no citations, and no match returns empty results. Request Examples: All-of word list; only the first word is used: { "response_data": { "rephrased_query": { "and": [ "Current", "trends", "developments", "in", "the", "global", "regional", "labor", "market,", "including", "hiring", "patterns,", "employment", "statistics,", "workforce", "shifts,", "talent", "acquisition", "dynamics.", "Analysis", "of", "job", "market", "growth,", "unemployment", "rates,", "recruitment", "activity,", "workforce", "participation", "trends." ] } } } Search phrase with an empty capture-date range: { "response_data": { "rephrased_query": { "or": [ "General trends and developments in the labor market, including employment rates, hiring patterns, and workforce dynamics. Analysis of the job market, recruitment environment, and talent landscape." ] } } } Search phrase with a capture-date range: { "filters": { "date_captured": { "max": "08-14-2026", "min": "08-15-2025" } }, "response_data": { "rephrased_query": { "or": [ "Global and regional labor market trends, workforce dynamics, and employment patterns. Developments in hiring, unemployment rates, and shifts in the job market or labor force statistics." ] } } } Search phrase with the summary flag set: { "summary": true, "response_data": { "rephrased_query": { "or": [ "Geographic hubs, innovation centers, and corporate environments where senior mainframe professionals and IBM Z experts show the highest levels of activity and engagement. Regions and organizations with a significant presence of z/OS specialists, COBOL experts, and mainframe modernization talent." ] } } } Any-of search phrase, no filters: { "response_data": { "rephrased_query": { "or": [ "Software engineering salaries and diverse consulting roles offered by Global System Integrators (GSIs) and IT service providers in Chicago. Compensation benchmarks and job titles for software developers and technical consultants within the Chicago professional services and IT services market." ] } } }

get_signal_news_v3

Analyze talent size breakup by seniority Experience mix of the talent pool under an "analyze_roles" key: breakup_by_seniority maps whole-year buckets of total career experience (0-19, 20 = 20+ years) to % of profiles, plus median_experience. Draup profiles only. Use for: how experienced a role's talent pool is, junior vs senior availability. Not for: talent size in an experience band (use get_talent_size_* with years_of_experience), pay by experience (use get_base_pay_*), education (use get_education_background_*). Send 1-25 roles and 1-10 locations (one pooled answer). Years-of-experience filter active: do not call. Read-only; no credits or user context. Request Examples: Filter by job role, core skill, and location: { "response_data": { "job_roles": { "OR": [ "Software Development Engineer" ] }, "core_skills": { "OR": [ "Python (Programming Language)" ] }, "multiple_locations": { "OR": [ "Bengaluru Area, India" ] } } } Filter by job role and single location: { "response_data": { "job_roles": { "OR": [ "Software Development Engineer" ] }, "multiple_locations": { "OR": [ "Bengaluru Area, India" ] } } } Filter by job role, core skill, primary vertical, and location: { "response_data": { "job_roles": { "OR": [ "Software Development Engineer" ] }, "core_skills": { "OR": [ "Python (Programming Language)" ] }, "multiple_locations": { "OR": [ "Bengaluru Area, India" ] }, "primary_default_verticals": { "OR": [ "it services" ] } } } Filter by data scientist role, vertical, and location: { "response_data": { "job_roles": { "OR": [ "Data Scientist" ] }, "multiple_locations": { "OR": [ "San Francisco Bay Area, United States" ] }, "primary_default_verticals": { "OR": [ "consumer software" ] } } } Filter by manager role, multiple core skills, and country: { "response_data": { "job_roles": { "OR": [ "Software Development Manager" ] }, "core_skills": { "AND": [ "Distributed Systems", "Software Development" ] }, "multiple_locations": { "OR": [ "India" ] } } }

get_talent_size_breakup_seniority_roles_v3

Talent size compared across job roles Talent size per job role (2-10 roles), each pooled across the selected locations: one entry keyed by role name (roles with no data are absent) with size, 95% likely range, 3-year supply growth, RSE, Draup profile count, data_sources and region_countries, plus top-level show_filters. Use for: comparing supply across roles. Not for: one pooled estimate (get_talent_size_analyze_roles_v3), one role across locations (get_talent_size_compare_locations_v3), demand (get_talent_demand_*), pay (get_base_pay_*). Show: display_talent_size, increase_rate_percentage and display_relative_standard_error, not talent_size, increase_rate or relative_standard_error_pct. Request Examples: Talent size: job_roles=Data Scientist,Data Engineer; multiple_locations=United States of America: { "response_data": { "job_roles": { "or": [ "Data Scientist", "Data Engineer" ] }, "multiple_locations": { "or": [ "United States of America" ] } } }

get_talent_size_compare_roles_v3

Get talent vertical distribution for multiple job roles One vertical map per job role (2-10), keyed by lower-case role name as stored in the profile index; roles without matching profiles are absent. With several locations each role's mix covers them combined. Score = profile % x w + posting % (12-month postings) x (1 - w), w = that entry's Draup profiles / its talent size (not capped; 0 when unknown). Use for: comparing the industry mix across roles. Not for: one pooled mix (get_vertical_distribution_analyze_roles_v3), individual companies (get_org_top_employers_*), hiring difficulty (get_hiring_difficulty_index_*). Show: per-entry share = score / that entry's sum x 100 (1 decimal), not the raw score. Send no vertical or years-of-experience filter. Request Examples: Analyze multiple scientist roles with core skills across US locations: { "response_data": { "job_roles": { "OR": [ "Analytical Scientist", "Applied Scientist", "Fragrance Analyst", "Sensory Scientist" ] }, "core_skills": { "OR": [ "Analytical Techniques", "Experimental Design", "Natural Science", "Performance Analysis", "Performance Testing", "Scientific Problem Solving" ] }, "multiple_locations": { "OR": [ "Greater New York City Area, United States", "United States of America" ] } } } Compare Data Scientist and .NET Developer in Bengaluru Area, India: { "response_data": { "job_roles": { "OR": [ "Data Scientist", ".NET Developer" ] }, "multiple_locations": { "OR": [ "Bengaluru Area, India" ] } } } Analyze leadership product roles with AI core skill in London: { "response_data": { "job_roles": { "OR": [ "Product Director", "Product Management Director", "Head of Technology" ] }, "core_skills": { "AND": [ "Product Strategy", "Artificial Intelligence (AI)" ] }, "multiple_locations": { "OR": [ "Greater London, United Kingdom" ] } } } Analyze manufacturing roles in automotive vertical across specific US MSAs: { "response_data": { "job_roles": { "OR": [ "Manufacturing Technician", "Quality Assurance Technician" ] }, "multiple_locations": { "OR": [ "Cincinnati Metropolitan Area, United States", "Columbus, Ohio Area, United States", "Dayton, Ohio Area, United States", "Greater Grand Rapids, Michigan Area, United States", "Houston, Texas Area, United States", "San Antonio, Texas Area, United States" ] }, "primary_default_verticals": { "OR": [ "automotive" ] } } }

get_talent_vertical_distribution_roles_v3

List technology products a company added in the last 12 months Returns the technology products first detected in one company's tech stack during the past 12 months, with the date each was first detected (added_date), product name and ID, tech stack category, G2 sub-category, developer company, the workloads the product supports, the countries where its use was evidenced and its usage index (High, Medium or Low; rarely NA). Use it to see which tools a company has recently started using. It covers new additions only: it does not report products that were dropped or are in decline, and products first seen more than a year ago are not included (get_account_tech_stack_products_v3 returns the full stack). Every qualifying product comes back in one response, in no particular order (several hundred rows for large companies); there are no filters or paging, and the result is empty when nothing was added in the last year. {id} is the company's Draup account ID, as returned by get_account_id or get_universe_companies_v3. Fields with no value are left out.

get_tech_stack_trend_v3

Search Draup's company directory by filters Returns a page of companies from Draup's company universe as flat rows under `response`. Each row has `logo_url` and `company_tags` plus the columns of the view named in `view_name` (your saved view of that name, otherwise the product's default columns), keyed by attribute name: identifiers, headquarters, verticals, workforce and hiring metrics, signal flags, tech stack and financial objects with `value`, `year`, `currency` and `currencyUnit` (USD millions). Entity filters go in `response_data` as `or`/`and`/`not` lists: `competitors` expands each chosen company to its top 10 competitors, `projects` keeps companies saved in those lists, and hiring keys keep companies with matching job postings. Numeric ranges go in `filters`. `search_key` adds a company-name contains match and `project_id` scopes to a project. `pagination.sortBy` takes a column key (default: your product's opportunity-index score) and `sortType` the direction; pages hold 10 rows unless `limit` is set. Each row's `account_id` is the Draup account id that account tools such as get_account_details_v3 take as `id`. Request Examples: Opportunity view for companies in a given vertical: { "view_name": "Opportunities", "pagination": { "limit": 25, "offset": 0, "sortBy": "talent_oi_score", "sortType": "DESC nulls last" }, "response_data": { "verticals": { "OR": [ "Enterprise Software" ] } } } Companies by HQ locations in specific US metro areas: { "view_name": "About Company", "pagination": { "limit": 25, "offset": 0, "sortBy": "talent_oi_score", "sortType": "DESC nulls last" }, "response_data": { "hq_locations": { "OR": [ "Greater New York City Area, United States", "San Francisco Bay Area, United States" ] } } } Companies hiring for a specific role in a given location and date range: { "filters": { "hiring_job_postings_interval": { "max": "07-22-2026", "min": "06-23-2026" } }, "view_name": "About Company", "pagination": { "limit": 25, "offset": 0, "sortBy": "talent_oi_score", "sortType": "DESC nulls last" }, "response_data": { "hiring_locations": { "OR": [ "Bengaluru Area, India" ] }, "hiring_job_titles": { "OR": [ "hr analyst" ] } } }

get_universe_companies_v3

Industry vertical filter options with profile counts (any scenario) Industry verticals in data.results as {key, doc_count}: key = vertical name (lowercase), doc_count = matching Draup profiles, highest first; all verticals in one list, no paging. Verticals already in primary_default_verticals and 'na' are left out; data.percentile is always empty. Use for: which industries can be filtered on and valid names for primary_default_verticals in other tools (pass exactly as returned). For skill names use get_core_skill_filter_options_v3; for experience ranges use get_yoe_filter_options_v3. Not for: the talent split by vertical (get_vertical_distribution_* or get_talent_vertical_distribution_roles_v3); doc_count ranks verticals only and is not talent size. req_type is required: send analyze_skills, compare_skills or compare_locations_skills when the talent pool is defined by skills (core_skills is then applied); any other allowed value ignores core_skills. Send job_roles, job_families and/or core_skills, and/or multiple_locations (location-only is valid for analyze_locations); keys combine with AND. Occupations, filters, pagination and search_key are not applied: for an occupation scenario the list covers all profiles in the location(s); say so when it matters. Before applying a vertical, check show_filters from get_talent_size_*. Request Examples: One job role in one metro area: { "req_type": "analyze_roles", "response_data": { "job_roles": { "or": [ "Agentic AI Engineer" ] }, "multiple_locations": { "or": [ "Bengaluru Area, India" ] } } } One core skill in one country, leaving out a vertical already chosen: { "req_type": "analyze_skills", "response_data": { "core_skills": { "or": [ "Python (Programming Language)" ] }, "multiple_locations": { "or": [ "United States of America" ] }, "primary_default_verticals": { "or": [ "enterprise software" ] } } }

get_vertical_filter_options_v3

Experience percentiles and per-year histogram of talent profiles (any scenario) data.percentile.low / high = years of experience at which the cumulative count of matching Draup profiles first reaches 33% / 66%; data.results = per-year {key, doc_count} buckets in ascending order. Bands: Low = 0 to low, Medium = low to high, High = high to 20; cap bounds at 20 and drop a band whose lower bound is 20 or more. Use for: turning 'junior / mid-level / senior' into a filters.years_of_experience range for other tools. For skill names use get_core_skill_filter_options_v3; for industry vertical names use get_vertical_filter_options_v3. Not for: a seniority split of the talent pool (get_seniority_breakup_* or get_talent_size_breakup_seniority_roles_v3). Counts are Draup profiles, not talent size. req_type is required: send analyze_skills, compare_skills or compare_locations_skills when the talent pool is defined by skills (core_skills is then applied); any other allowed value ignores core_skills. Send job_roles, job_families and/or core_skills, and/or multiple_locations (location-only is valid for analyze_locations); keys combine with AND and several names under one key are pooled into one histogram. Occupations, verticals, filters, pagination and search_key are not applied: for an occupation scenario the result covers all profiles in the location(s), and with no applied key it covers all Draup profiles; say so when it matters. Before applying a range, check show_filters from get_talent_size_*. Empty results with null low / high: no matching profiles; say the experience bands are unavailable. Request Examples: One job role in one metro area: { "req_type": "analyze_roles", "response_data": { "job_roles": { "or": [ "Agentic AI Engineer" ] }, "multiple_locations": { "or": [ "Bengaluru Area, India" ] } } } One core skill in one country: { "req_type": "analyze_skills", "response_data": { "core_skills": { "or": [ "Python (Programming Language)" ] }, "multiple_locations": { "or": [ "United States of America" ] } } }

get_yoe_filter_options_v3

Get role description Draup's taxonomy record for ONE job role. Default (desc false): family, occupation, description, responsibilities, core_skills and soft_skills (often null), digitization_quotient with its category, search_assistance_query (Boolean sourcing string), O*NET code, title and link, and peer_roles (other roles in its families that have talent data). desc true: only desc (the description) and the O*NET fields. Use for: responsibilities, O*NET mapping, automation exposure, similar roles in the family, a Boolean search string. Not for: a short definition with family and occupation (get_role_overview_analyze_roles_v3); demand-ranked skills (get_talent_skills_analyze_roles_v3); career moves (get_career_paths_*); profiles (get_relevant_profiles_*). Pass one exact, case-sensitive role from search_job_roles_v3 or browse_job_roles_by_family_v3. Request Examples: Sample body payload without desc flag: { "role": "Software Development Engineer" } Sample body payload with desc flag: { "desc": true, "role": "Software Development Engineer" }

role_description_v3

Top business functions represented in matching job postings Returns the business functions that matching job postings belong to, meaning departments such as Sales & Marketing, ER&D or Human Resources, ranked by the number of postings in each. It powers the Talent Demand business function chart, showing which parts of a company, vertical or market are hiring for a role, skill or keyword. The posting set can be filtered by title, company or Draup account, location, skill, workload, vertical, function, education and keyword, leaving out staffing firms and job portals if needed, within a posting-date window that defaults to the last 12 months. Up to 500 functions are ranked and paged ten at a time by default, each row giving the function name and its count. The 33rd and 67th percentile counts over all functions are added as low, medium and high colour bands rather than a minimum and maximum. No match returns an empty chart. Allowed keys: benefits, business_functions, certifications, companies, company_tags, core_skills, digital_products, educational_backgrounds, educations, exclusions, job_families, job_occupations, job_roles, job_titles, keywords, languages, location_types, locations, primary_verticals, soft_skills, sub_verticals, verticals, work_types, workloads. Request Examples: Business functions represented in postings from LinkedIn (exact company name): { "pagination": { "limit": 25, "offset": 0 }, "response_data": { "companies": { "or": [ "LinkedIn" ] } } } Business functions for AI-skill postings at jpmorgan chase & co. in the United States: { "pagination": { "limit": 25, "offset": 0 }, "response_data": { "companies": { "or": [ "jpmorgan chase & co." ] }, "locations": { "or": [ "united states of america" ] }, "core_skills": { "or": [ "artificial intelligence (ai)" ] } } } AI-keyword postings in enterprise software within a job-post date range: { "filters": { "job_post_date": { "max": "07-31-2026", "min": "07-01-2025" } }, "pagination": { "limit": 25, "offset": 0 }, "response_data": { "keywords": { "or": [ "Artificial Intelligence", "AI", "Generative AI", "Machine Learning", "LLM" ] }, "primary_verticals": { "or": [ "enterprise software" ] } } }

talent_demand_top_business_function_v3