Draup for Sales

Get hiring summary for an account Returns three headline hiring tiles for an account, built from its job postings over the last 12 months. `#_of_job_postings` is the number of matching postings and `#_of_locations` the number of distinct metro areas those postings are in, both labelled with the period (for example "Oct 2025 - Oct 2026"). `Globalization_index` is the account's overall globalization index score, a single value that filters do not change and that is null when unavailable. Optional `response_data` narrows the postings: `business_functions`, `workloads`, `core_skills`, `job_titles` and `multiple_locations` ("global" means all locations). Values within a key match any-of, `not` exclusions are ignored, and any other key returns HTTP 400. The same lists may also be sent as `filters` entries (`locations`, `business_functions`, `workloads`, `core_skills`, `job_title`); `response_data` takes precedence when both are given. Request Examples: Filter by multiple_locations and request overall business summary: { "response_data": { "multiple_locations": { "or": [ "global" ] } }, "overall_business": true } Filter using locations and business_functions inside filters: { "filters": { "locations": [ "global" ], "business_functions": [ "IT" ] } } Filter by business_functions, workloads, and multiple_locations: { "response_data": { "workloads": { "or": [ "Artificial Intelligence & Data Science", "Application Development & Maintenance" ] }, "business_functions": { "or": [ "ER&D", "Sales & Marketing" ] }, "multiple_locations": { "or": [ "global" ] } } }

accounts_hiring_summary_v3

Get hiring timeline for an account Returns the month-by-month count of an account's job postings over a rolling 12-month window ending today. `hiring_timeline` lists the complete calendar months (`year`, `month`, `job_count`); `partial_months_hiring_timeline` holds the incomplete months at the window's edges, with `start_date` on the first and `end_date` (today) on the current month. `avg` and `stddev` are the mean and standard deviation of the complete-month counts, and `plot_points` gives five reference levels: mean minus two and one standard deviations, mean, and mean plus one and two. Entries can be null when the lower levels would fall below zero. Optional `response_data` narrows the postings: `business_functions`, `workloads`, `core_skills`, `job_titles` and `multiple_locations` ("global" means all locations). Values within a key match any-of, `not` exclusions are ignored, and any other key returns HTTP 400. The same lists may also be sent as `filters` entries; `response_data` takes precedence. Request Examples: Filter by global multiple_locations only: { "filters": { "trend": false }, "response_data": { "multiple_locations": { "or": [ "global" ] } } } Filter by global location and ER&D business function: { "filters": { "trend": false }, "response_data": { "business_functions": { "or": [ "ER&D" ] }, "multiple_locations": { "or": [ "global" ] } } } Filter by locations, business functions, workloads and sort by job_posting: { "filters": { "trend": false }, "pagination": { "sortBy": "job_posting", "sortType": "DESC" }, "response_data": { "workloads": { "or": [ "Artificial Intelligence & Data Science" ] }, "business_functions": { "or": [ "ER&D", "Sales & Marketing" ] }, "multiple_locations": { "or": [ "global" ] } } }

accounts_hiring_timeline_v3

Get key locations map for an account Returns map points for an account's locations, one item per metro area (MSA) and business function. `workforce_size` is the account's current headcount at its sites in that MSA, `workforce_range` the estimated minimum-maximum headcount, `job_postings` the account's job postings for that function and MSA over the last 12 months, and `growth_index` = job_postings / workforce_size (null without workforce). `location_classes` holds site tags such as HQ Location, plus "New Site" when there is no workforce but more than four postings. Items carry country and coordinates, are sorted by workforce then postings, and are capped at `pagination.limit` (default 10). `workforce_low`/`workforce_high` (33rd/66th headcount percentiles) and `gi_low`/`gi_high` (mean growth index minus/plus one standard deviation) support banding. Optional `response_data`: `business_functions` (first value used), `locations` (metro area, country or region names; "global" means all) and `location_types` (site tags such as HQ Location or GCC Locations); other keys return HTTP 400. Request Examples: Key locations with empty response_data filter object: { "pagination": { "limit": 500, "offset": 0 }, "business_function": "ER&D" } Key locations for an account and business function with pagination: { "pagination": { "limit": 500, "offset": 0 }, "business_function": "ER&D" } Key locations filtered by company, business function, and MSA: { "pagination": { "limit": 500, "offset": 0 }, "response_data": { "company": { "or": [ "Nvidia Corporation" ] }, "locations": { "or": [ "San Francisco Bay Area, United States" ] }, "business_function": { "or": [ "ER&D" ] } }, "business_function": "ER&D" }

accounts_location_insights_key_locations_map_v3

Get partnership investments for an account Lists the investments an account has made, such as funding rounds in startups, as a table plus two aggregates. Each `rowData` row follows `columnDefs`: investment date, invested company, its focus areas, investment type, locations, a display amount with currency scaled to K, Mn or Bn, round, `partner_accounts`, the raw amount, logos, the invested company's Draup account ID when it is in the Draup universe, and the funding intent. Rows are sorted newest first and all are returned; `resultCount` is the row count. `investment_timeline` gives per-year investment counts and total size in millions, and `investments_by_stage` gives counts per round. Narrow with `response_data.investment_types` (only included values are applied; other keys return HTTP 400) and `filters.time_period`, a one-item lookback list such as `["1 year"]` or `["Maximum"]`. Without a time period, rows and stage counts cover all recorded investments while the timeline covers the last five years. Request Examples: No filters, default pagination: null Filter by investment type Angel: { "response_data": { "investment_types": { "or": [ "Angel" ] } } } Specify time_period but no entity filters: null

accounts_partnerships_investments_v3

Get mergers and acquisitions for an account Returns the acquisitions made by a company (Draup account id in the path) in three views. `rowData` is a table described by `columnDefs`, one row per acquisition: date, acquired company, the business areas acquired, primary intent, location, deal size formatted as USD K/Mn/Bn, a hidden numeric size for sorting, logos and the acquired company's Draup account id when it is in the company universe; `resultCount` is the number of rows returned. `merger_timeline` gives acquisitions per year, newest year first, and `acquisition_map` gives the acquisition count and summed deal size per country code. The only filter honoured is `filters.time_period`, a one-item list holding a period such as "1 year" or "Maximum", which limits all three views to acquisitions since then. Without it (or with Maximum) the table and map cover all years and the timeline the last five years. All matching rows are returned; `pagination.sortBy`/`sortType` set the primary sort, otherwise newest first. Request Examples: No filters, default pagination: null Filter by Angel investment type: { "filters": { "investment_types": [ "Angel" ] } }

accounts_partnerships_mergers_v3

Get private financials for an account Returns the funding history of a company, identified by its Draup account id in the path, built from its recorded funding rounds. `total_funding_amount` is the sum of all round sizes in millions, alongside `num_funding_rounds`, `last_funded_on` (date of the most recent round) and `permalink`, a short company slug. `rounds` lists every round newest first with `date`, `name`, `money_raised` and `valuation_amount` (both in millions, or the string "Null" when undisclosed), `lead_investor` (comma-separated lead investors), `investors` (other participants, excluding the leads), `funding_type` (for example debt financing) and `funding_series`. Data is returned for any company with recorded rounds, whether public or private. When none exist, the summary fields are null and `rounds` is empty.

accounts_private_financials_v3

Get talent flow insights for companies Shows which companies the company in the path gains talent from and loses talent to over `filters.monthly_time_frame` (`min`/`max` as MM-DD-YYYY; by default the 12 months ending with the month three months before the current one). Built from professional profiles and employment histories: per company, `hiring` counts professionals who joined the target in the window from it (previous employer), `attrition` those who left the target in the window for it (next employer), and `net_gain_loss` is hiring minus attrition. `req_type` and `workforce_type` are required. `workforce_type` `all` returns `table_data` plus `total_count`, with `account_id` on known companies; `is_parent`/`is_child` mark the target's parent and subsidiaries. Rows sort by `pagination.sortBy` (`net_gain_loss`, `hiring`, `attrition`, `key`; `absoluteSort` ranks by total movement) and page by `limit`/`offset` (default 10). Other types return ranked `{key, value, additional_data}` lists. `response_data` accepts only `job_families`, `locations`, `competitors` and `projects`; any other key returns HTTP 400. Request Examples: Table view for all companies over a time frame without location or job family filters: { "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } }, "req_type": "table", "pagination": { "limit": 500, "offset": 0, "sortBy": "net_gain_loss", "sortType": "desc", "absoluteSort": true }, "workforce_type": "all" } Graph view filtered by a specific location: { "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } }, "req_type": "graph", "pagination": { "offset": 0 }, "response_data": { "locations": { "or": [ "United Kingdom" ] } }, "workforce_type": "net_gain_loss" } Graph view filtered by location and job family: { "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } }, "req_type": "graph", "pagination": { "offset": 0 }, "response_data": { "locations": { "or": [ "United Kingdom" ] }, "job_families": { "or": [ "Software & Technology Management" ] } }, "workforce_type": "net_gain_loss" }

accounts_talent_flow_insights_companies_v3

Get talent flow location insights for an account Breaks down talent movement at the company given by `id` by the metro area (MSA) where professionals worked there, over `filters.monthly_time_frame` (`min`/`max` as MM-DD-YYYY; by default the 12 months ending with the month three months before the current one). Aggregated from Draup's workforce data; no individual records are returned: `hiring` counts tenures at the company that started in the window, `attrition` tenures that ended in it, and `net_gain_loss` is hiring minus attrition. `req_type` and `workforce_type` are required. `workforce_type` `all` returns `table_data` rows plus `total_count`, sorted by `pagination.sortBy` (`net_gain_loss`, `hiring`, `attrition` or `key`; `absoluteSort` with `net_gain_loss` ranks by total movement) and paged by `limit`/`offset` (default 10). `hiring` or `attrition` return a ranked `{key, value}` list with latitude, longitude and country; `net_gain_loss` returns net gainers then net losers. `response_data` accepts only `job_families`, `locations`, `competitors` and `projects`; any other key returns HTTP 400. `filters.work_experience` narrows by years of experience. Request Examples: Table view, all workforce, time-bounded, no entity filters: { "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } }, "req_type": "table", "pagination": { "limit": 500, "offset": 0, "sortBy": "net_gain_loss", "sortType": "desc", "absoluteSort": true }, "workforce_type": "all" } Graph view, net gain/loss, filtered to a specific country location: { "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } }, "req_type": "graph", "pagination": { "offset": 0 }, "response_data": { "locations": { "or": [ "United Kingdom" ] } }, "workforce_type": "net_gain_loss" } Graph view, net gain/loss, filtered by country and two job families: { "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } }, "req_type": "graph", "pagination": { "offset": 0 }, "response_data": { "locations": { "or": [ "United Kingdom" ] }, "job_families": { "or": [ "Software & Technology Management", "Systems Management & Engineering" ] } }, "workforce_type": "net_gain_loss" }

accounts_talent_flow_insights_locations_v3

Get top talent flow metric for an account Returns one headline talent-flow figure for the company given by `id` as `{value, percent}`, selected by the required `metric`: `hiring`, `attrition` or `workforce_growth` (anything else returns HTTP 400). Over `filters.monthly_time_frame` (`min`/`max` as MM-DD-YYYY; by default the 12 months ending with the month three months before the current one), `hiring` counts professionals who joined the company and `attrition` those who left; their `percent` is that count divided by the average of current and previous workforce, times 100. Workforce is the company's headcount at the end of the window, and previous workforce the same at the end of the preceding period of equal length. For `workforce_growth`, `value` is the workforce change and `percent` the percentage change; a zero denominator yields 0 for both. `response_data` accepts only `job_families`, `locations`, `competitors` and `projects`; any other key returns HTTP 400. `filters.work_experience` narrows by years of experience. Request Examples: Hiring metric with only time frame: { "metric": "hiring", "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } } } Workforce growth filtered by United Kingdom location: { "metric": "workforce_growth", "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } }, "response_data": { "locations": { "or": [ "United Kingdom" ] } } } Hiring filtered by SF Bay Area and selected companies: { "metric": "hiring", "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } }, "response_data": { "locations": { "or": [ "San Francisco Bay Area, United States" ] }, "competitors": { "or": [ "Intel Corporation", "Apple Inc." ] } } }

accounts_talent_flow_top_metrics_v3

Get workforce trends for an account Returns a month-by-month talent-flow series for the company given by `id` across `filters.monthly_time_frame` (`min`/`max` as MM-DD-YYYY; by default the 12 months ending with the month three months before the current one), one entry per month keyed like `Aug 2025`. Counts are aggregated from Draup's workforce data; no individual records are returned: `hiring` is tenures at the company that started in the month, `attrition` tenures that ended in it, `net_gain_loss` their difference, and `total_workforce` the company's workforce at the end of the month. `req_type` and `workforce_type` are required. `req_type` `table` always returns all four measures per month; otherwise `workforce_type` `hiring`, `attrition`, `total_workforce` or `net_gain_loss` returns `{key, value, additional_data}` per month. Months are chronological unless `pagination.sortBy`/`sortType` is given. `response_data` accepts only `job_families`, `locations`, `competitors` and `projects`; any other key returns HTTP 400. `filters.work_experience` narrows by years of experience. Request Examples: Table view for all workforce without entity filters: { "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } }, "req_type": "table", "workforce_type": "all" } Graph view for total workforce filtered by location: { "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } }, "req_type": "graph", "response_data": { "locations": { "or": [ "United Kingdom" ] } }, "workforce_type": "total_workforce" } Graph view for total workforce filtered by location, job families, and companies: { "filters": { "monthly_time_frame": { "max": "07-31-2026", "min": "08-01-2025" } }, "req_type": "graph", "response_data": { "locations": { "or": [ "San Francisco Bay Area, United States" ] }, "competitors": { "or": [ "Intel Corporation", "Apple Inc." ] }, "job_families": { "or": [ "Software & Technology Management", "Systems Management & Engineering" ] } }, "workforce_type": "total_workforce" }

accounts_talent_flow_workforce_trends_v3

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 Customers Deals for Account Profile Returns a service-provider snapshot of the Draup account in the path, built from the active outsourcing deals where it is the vendor: its top customers, the verticals of the customers it serves, its main delivery locations and the workloads it delivers. It powers the Customers and Deals widget on the account profile of IT, engineering and professional services firms. Data exists only when the account's primary vertical is IT Services, Product Engineering Services or Professional Services; for any other company all four parts come back empty. Each part holds at most three entries: customers ranked by the number of active deals with this vendor, counting only customers visible to the caller's product, customer verticals ranked by the number of distinct customers, and delivery locations and workloads ranked by the number of deals. A deal counts as active when it is not flagged inactive and has no end date or one in the future. No filters apply. Request Examples: Basic request with no filters: { "widget": "customers_deals" }

customers_deals

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

Count outsourcing deals matching entity filters Returns the total number of outsourcing/service-provider deals matching the given entity filters, as a single-element result list with a total_count field. Call it with the same response_data used for the matching deals_get_landing_data list to learn how many deals a filter combination matches, e.g. before paging through results. Filter with response_data using canonical plural keys such as "customers", "service_providers", "digital_products", and "hq_locations", each set as {"or": ["value"]}; never filter by numeric company id, use the company name instead. An empty response_data counts all deals in the corpus. Request Examples: Deal count for one customer: { "response_data": { "customers": { "or": [ "Walmart Inc." ] } } } Digital products (multi-value) plus HQ location: { "response_data": { "hq_locations": { "or": [ "India" ] }, "digital_products": { "or": [ "NetSuite", "NetSuite Supply Chain Management System" ] } } } Deal count for one service provider: { "response_data": { "service_providers": { "or": [ "Accenture" ] } } }

deals_get_data_count

Get Demographics Data Retrieves demographic, labor, and business environment data for specified job roles and locations. Request Examples: Filter by job_role and job_country: { "filters": { "job_role": "Software AND Mathematics", "job_country": [ "Vietnam" ] } } Filter by job_role and job_location: { "filters": { "job_role": "Data Scientist", "job_location": [ "Bengaluru Area, India" ] } }

demographics_data

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 top hiring accounts for a vertical Returns a ranked list of top hiring accounts (organizations) for the specified analysis entity (typically a vertical such as an industry). Each account entry includes the internal account identifier, organization name, logo URL when available, and the hiring count used for ranking. The response also contains percentile thresholds that can be used for classifying accounts into high, medium, and low hiring intensity segments. Request Examples: Automotive vertical: { "analysis_entity": "Automotive" }

ecosystem_get_vertical_organization_top_top_hiring_accounts

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

Get IT vertical geographic map Returns a list of metropolitan statistical areas (MSAs) with company strength and associated geographic metadata for a specified analysis entity (e.g., an industry vertical like "Automotive"). Request Examples: Automotive vertical example: { "analysis_entity": "Automotive" }

ecosystem_vertical_map_data_it

Get R&D geographic distribution for a vertical Returns a list of metro or regional statistical areas (MSAs) and related geographic metadata where companies in the specified vertical have R&D presence, along with an aggregated company_strength metric per area. The request body requires an analysis_entity such as a vertical name (for example, "Automotive"). Request Examples: Automotive vertical: { "analysis_entity": "Automotive" }

ecosystem_vertical_map_data_r_and_d

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 Partnerships and Mergers for an Account Returns the acquisitions made by the account in the path: a table of acquired companies, a yearly acquisition timeline and acquisition counts and deal value by country. It powers the acquisitions table, timeline and map widgets of the account Investments and Acquisitions (Partnerships) tab. Acquisitions can be limited to a recent time period, such as the last one or three years; with no period there is no date limit, though the timeline then covers the last five years. Each acquisition shows its date, acquired company, acquired workloads, primary acquisition intent, location, deal size in US dollars formatted in thousands, millions or billions, and the acquiree's Draup account when it has one, newest first, with every row returned. The timeline counts acquisitions per year, newest first, and the map gives counts and summed raw US dollar deal size per country, counting only acquisitions whose location maps to a Draup location. Request Examples: No filters: null Empty time period: null Filter by time period: { "filters": { "time_period": [ "Maximum" ] } }

get_account_acquisitions

Get About Profile for Account Returns the company profile of the Draup account in the path for the About widget at the top of the Universe account profile page: the about text, primary vertical, verticals and sub-verticals, headquarters and address, employee count, vision, mission, founding year, ownership type, links, parent company and Opportunity Index score. The widget always describes that single account and takes no filters. The Opportunity Index score is shown with two decimals and is a weighted sum of index components whose weights are normalised to total 10. Parent company details are filled only when the parent is itself a Draup account. Sales and Talent build the score from different components, with Sales weighing outsourcing, service-provider partners, globalization and investments and Talent weighing headcount, M&A, layoff signals and R&D workforce, so the same account can score differently. An account not visible to the caller's product returns an empty result. Request Examples: Basic About Widget Request: null

get_account_details

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 Sales Opportunity Index, a 0-10 score rounded to two decimals that weighs outsourcing size, number of service-provider partners, job postings, globalization, revenue, revenue growth, executive movement and investments 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

Retrieve financial information for a specific account Returns the latest financial snapshot of the Draup account in the path for the Financials widget of the Universe account profile: revenue, IT spend, R&D spend and EBITDA with their growth figures, the financial year they refer to, the ownership type, and private funding details such as number of rounds, total raised, last round, valuation and latest investor. The four headline figures are expressed in millions alongside a currency code and that currency's conversion rate to US dollars, and each is left empty when the figure or the rate is missing. The year shown is the latest financial year with revenue data. Total funding is the sum of all round amounts, and the last round's date, amount and company valuation are given. The funding details are empty for accounts with no recorded funding rounds, which is typical of public companies. No filters apply, and an account not visible to the caller's product returns an empty result.

get_account_financials

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

Get hiring timeline for an account Returns the monthly count of job postings published by the Draup account in the path over the trailing 12 months, with the average, standard deviation and band values behind the hiring timeline chart on the account's Hiring view. It helps spot hiring spikes or slowdowns. Counts can be narrowed by skills, workloads, business functions, job titles and location. The window runs from the same day last year up to yesterday. Complete calendar months, usually 11, are listed with zero where there were no postings, and the partial first and current months come separately with their start or end dates. The average and sample standard deviation cover the complete months, and the bands mark one and two deviations either side of the average, left empty when the average or deviation is zero or a lower band would fall below zero. An account with no postings still returns every month at zero. Request Examples: Sample body payload 1: { "filters": { "trend": false, "locations": [ "global" ] } } Sample body payload 2: { "filters": { "trend": false, "locations": [ "global" ] }, "pagination": { "sortBy": "job_posting", "sortType": "DESC" } }

get_account_hiring_timeline

Fetch hiring demand data by category Ranks the values of one job posting attribute, such as skills, job titles, workloads, business functions, education, certifications, job type or experience, across the job postings the Draup account in the path published in the last 12 months. It powers the Hiring demand widgets of the account profile. Results can be narrowed by skills, workloads, business functions, job titles and location, or limited to values labelled Trending Up or Declining. Each value carries its 12-month posting count, a 13-month trend whose first and last months are partial, and a label comparing quarterly totals inside the window. Values are ranked by posting count, most first by default, ten per page; only the top 1,000 are analysed and the total number of values is approximate. The experience attribute instead returns postings by required years of experience with a standard deviation and an availability percentage. When no postings match, the list is empty. Request Examples: Top 10 core skills by job postings: { "pagination": { "limit": 10, "offset": 0, "sortBy": "job_posting", "sortType": "DESC" }, "demand_type": "All" }

get_account_hiring_trends