Whirrwork

Arithmetic on published prices: unit price × quantity, using exact unit conversions (acre↔hectare, sq ft↔m², mile↔km), preferring benchmarks whose region matches the country, converting currency at a dated ECB rate. Returns the range plus the basis rows (each with source_url, as_of, region match) and caveats. Returns estimate=null with a reason when no sourced benchmark fits; never invents a number. Example: {"use_case":"crop-spraying-drone","quantity":500,"unit":"hectare","country":"BR","currency":"USD"}.

Submit a request for quotes for a real buyer who explicitly agreed to be contacted by matched suppliers (consent=true). Stored, auto-matched to providers by job + country, and reviewed by a human before anything is forwarded; nothing is emailed automatically. Returns id, status, matched provider ids, and a private status_url/access key for get_rfq_status. Use test=true for testing.

Side-by-side comparison of 2-10 providers: coverage for a country, engagement models, published prices (USD-normalized), HQ, contact routes, shared jobs, last checked. Facts only, no ratings.

Cost comparison of human labour (or a human service) against robot options (buy, lease, robots-as-a-service, robot service provider) for one of 22 jobs in one of 8 countries (US, DE, GB, JP, CN, IN, BR, MX). Uses the same model as whirrwork.com/tools/robot-vs-human/: every default input has a source or is flagged as an assumption (see input_sources). Returns monthly/annual cost, cost per unit, payback months, break-even utilisation, 5-year NPV vs the baseline, a verdict, non-cost factors, sensitivity ranking, and projections to 2035 with a crossover year range (robots-fast, base, robots-slow). Projections are modelled estimates, not facts: say so, and quote the range rather than one year. Examples: {"job":"warehouse-picking","country":"US"}; {"job":"commercial-floor-cleaning","country":"DE","inputs":{"volume":60000}}; {"job":"machine-tending","country":"US","region":"US-TX","include_projections":false}.

Shortlist providers for a job and place. Ranked: lists the region > lists the country > lists part of the country > headquartered there (HQ is not a service area) > works worldwide. Each result has coverage + coverage_reason, engagement models, published pricing with USD conversion, min_published_price_usd and public contact routes. Coverage is country-level unless the provider published regions. Filters combine (AND). Example: {"use_case":"drone-light-show","country":"AE"}; {"use_case":"commercial-floor-scrubbing","country":"CA","region":"ON","engagement_model":"raas"}.

Every price observation we have recorded (append-only; never overwritten) for a job and/or provider, grouped into series with trend (change_pct between first and latest observation). observed_at = when we recorded it; as_of = the date the source states. If every series has one observation, say so: there is no trend yet.

Current sourced prices for a job (benchmarks + provider-published), or for one provider. Every row has source_url and as_of. Set convert (e.g. "USD") to add a clearly labelled conversion with the FX rate, date and source. Benchmarks, not quotes.

Full public profile of one provider: website, HQ, service regions and explicit service areas (with sources), jobs, robot types, engagement models, published prices (with USD), contact routes, sources and last-checked date.

Status of a submitted RFQ (received → matched → sent_to_suppliers → quoted → closed) with its status history. Needs the id and the access key returned by submit_rfq (the key= part of status_url).

The exact fields to collect from the buyer before submit_rfq: core fields (required flags, formats) plus the job-specific checklist suppliers need to price it (as extra.{key}), regulatory notes, and a filled example. Ask the buyer for these, then call submit_rfq.

One Whirrwork Research statistic by slug: claim, figure, scope, measure type, primary source with archive link, publication/access/last-verified dates, verbatim source passage (when licensing allows), confidence, caveats, and citations in APA, Chicago, MLA and BibTeX (plus an APA citation of the original source). Licensed CC BY 4.0: attribute "Whirrwork (https://whirrwork.com), CC BY 4.0" and the original source.

Everything about one job: what it is, robot types, ways to buy (service/rental/RaaS/purchase), honest buyer tips (including when a robot is overkill), regulatory notes, every sourced price benchmark (with source_url, as_of and a USD conversion), price-history counts, the RFQ checklist, and providers. Pass country (and optionally region) to rank providers by who serves that place, with the reason.

The 20 job categories with use-case counts, for browsing when the buyer is vague.

For each country: providers that list it as a service area, providers serving parts of it (with sub-regions), and providers headquartered there; plus the number of worldwide providers. Use to tell the buyer honestly how well their country is covered.

Search Whirrwork Research: 100+ citable statistics on robot adoption, industrial and service robots, humanoids, drones, agriculture, cleaning, warehouses, labour shortages, wages, prices and funding, plus figures from Whirrwork's own dataset. Each result has the exact figure with unit and scope, the primary source (publisher, title, date, URL, Wayback archive), access and last-verified dates, a confidence level (high/medium/low), source_type (primary/secondary/whirrwork) and a permanent URL. Quote figures with their source and date; prefer high-confidence primary sources. Call get_stat for the verbatim quote, caveats and ready-made APA/Chicago/MLA/BibTeX citations. Examples: "humanoid robots sold 2025", "robot density Korea", "drone spraying price per acre", "robotics funding", topic="labour".

Find the robot job (use case) that matches what the buyer wants, in plain words and in any of 12 languages. Returns matching use cases (id, name, price summary, provider count) plus matching providers and guides. Start here, then call get_use_case with the best id. Examples: "drone light show for a wedding", "robotic floor scrubbing for a warehouse", "limpieza de ventanas con drones", "无人机灯光秀".