All Services

Computes the statistical correlation between calculated post-discount prices and a set of benchmark values for retail scenario analysis.

Computes a statistical distribution summary for final prices after applying percent-based discounts to a list of retail list prices.

Computes how the final discounted price changes when either list price or discount percentage shifts by a small finite step, providing local elasticity for retail discount scenario analysis.

Accepts a sequence of list-price/discount-percent pairs and returns an ordered trend analysis of the resulting post-discount prices.

Analyzes up to 200 price basket observation records (item_price, quantity_weight) to produce a duplicate key report and category frequency breakdown in two independent local analytical reports.

Analyzes up to 200 price basket observation rows to produce a duplicate key report and a grouped numeric summary for item_price or quantity_weight fields.

Runs two local analytical reports on price basket observation data: detects duplicate row keys and filters records by a numeric interval on item_price or quantity_weight fields.

Generates a duplicate key report and numeric row ordering for up to 200 price basket observation records (item_price, quantity_weight fields).

Filters price basket observation rows by a numeric interval on a chosen field and fills missing values using a specified method (zero, mean, or median), returning up to 2 analytical reports for up to 200 rows.

Computes numeric row ordering and a trailing mean series for up to 200 price basket observation records containing item price and quantity weight fields.

Runs a record quality audit and category frequency analysis on up to 200 price basket observation rows (item_price, quantity_weight), returning two independent analytical reports.

Audits data quality and filters by numeric interval for up to 200 rows of price basket observations (item_price, quantity_weight), returning two analytical reports per call.

Analyzes price basket observation records by running string normalization and adjacent row change detection on item_price or quantity_weight fields, returning two analytical reports for up to 200 rows.

Runs two local analytical reports on price basket observation rows: string normalisation and category frequency analysis for item_price and quantity_weight fields.

Performs string normalisation and daily sum series analysis on up to 200 price basket observation records containing item_price and quantity_weight fields.

Runs two local analytical reports on price basket observation data: string normalisation and a date-by-category pivot, for up to 200 rows of item_price or quantity_weight records.

Runs string normalisation and duplicate key detection reports on up to 200 price basket observation records containing item_price and quantity_weight fields.

Applies string normalisation and equal-width histogram binning to price basket observation records (item_price, quantity_weight), returning two independent analytical reports for up to 200 rows.

Normalises strings and removes exact duplicate rows from price basket observation records containing item_price and quantity_weight fields, returning two analytical reports for up to 200 rows.

Normalises string fields and applies numeric row ordering to up to 200 price basket observation records (item_price, quantity_weight), returning two analytical reports.

Runs string normalisation and trailing mean series analysis on supplied price basket observation records (item_price, quantity_weight) for up to 200 rows.

Validates a CSV of price quote import data and identifies blank or empty rows within the dataset.

Converts cryptocurrency amounts between different currencies or tokens using current market rates

Encodes a payload into a signed JWT (JSON Web Token) string

Generates image variations from an input image or query using AI image processing

Calculates mortgage payments and related financial metrics given loan parameters

Converts energy values between different units (e.g. joules, kilowatt-hours, calories, BTUs) via a pay-per-call API on Base

Generates an original poem based on a text query or topic provided by the user

Calculates loan payment details such as monthly installments, total interest, and amortization based on loan parameters

Checks and corrects grammar in a given text string, returning grammar analysis or corrected output

Searches or retrieves LinkedIn post content based on a query string via a paid per-call API

Verifies a smart contract on the blockchain, returning verification status and details

Transcribes audio content into text using an AI-powered speech-to-text endpoint, paid per call in USDC on Base.

Converts a length value from one unit of measurement to another (e.g. meters to feet, inches to centimeters)

Generates a text caption for a given query or subject using AI

Tests whether a given string matches a regular expression pattern, returning match results

Searches Twitter/X for threads matching a query, returning thread content and metadata

Formats and standardizes a raw address string or query into a canonical, structured address format

Detects the language of a given text input and returns the identified language.

Shortens a long URL into a compact, shareable short link

Analyzes the readability of a given text, returning readability scores and metrics to assess how easy the content is to understand.

Counts the number of words in a given text string

Converts pressure values between different units (e.g. Pascal, bar, PSI, atm, mmHg)

Converts area measurements between different units (e.g., square meters to acres, hectares to square feet)

Generates a catchy marketing slogan for a given query or business/product description using AI

Calculates retirement savings projections and financial planning estimates based on user-provided parameters

Generates AI-written captions for Instagram posts based on a query or content description

Generates images from a text query or prompt using an AI image generation model, billed per call in USDC on Base.

Verifies the integrity of a hash value against input data

Counts the number of characters in a given text string

Converts a temperature value between units (Celsius, Fahrenheit, Kelvin) via a pay-per-call API on Base

Fetches AI-curated news headlines based on a search query, returning current top stories relevant to the topic

Generates email content using AI based on a provided query or prompt

Converts plain text or structured content into formatted Markdown via a pay-per-call API on Base using USDC.

Generates code snippets or programs from a natural language query using AI

Generates a joke on demand based on an optional query or topic, paid per call in USDC on Base

Reads data from a deployed smart contract on a blockchain network by executing a read/call function

Generates or retrieves SEO keyword suggestions and insights for a given search query or topic

Returns turn-by-turn directions between two locations via a geospatial routing query

Generates educational flashcards on a given topic or query using AI

Converts a time from one timezone to another given a natural language or structured query

Calculates compound interest growth given principal, rate, and time parameters

Returns install lifecycle hook metadata (preinstall, postinstall, etc.) for the @prisma/client npm package to support integration and upgrade planning.

Returns published artifact integrity metadata (checksums, signatures, publish info) for a given version of the @prisma/client npm package.

Audits the heading hierarchy of a process guide document supplied as Markdown or plain text, returning a structured heading hierarchy audit deliverable.

Extracts and inventories all quoted passages from a process guide document supplied as Markdown or plain text.

Reorders the recognised sections of a process guide document according to a standard template structure, using deterministic processing without generative prose.

Extracts and isolates individual sections from a process guide document supplied as Markdown or plain text

Analyzes process handoff records by computing category frequencies and adjacent row changes for handoff_count or wait_minutes across up to 200 rows.

Computes a daily sum time series and a secondary-measure weighted mean for process handoff records (handoff_count, wait_minutes) supplied by the caller.

Generates a duplicate key report and a Base-100 indexed series for supplied process handoff records (handoff_count, wait_minutes) on up to 200 rows.

Analyzes up to 200 process handoff records to produce a duplicate key report and a grouped numeric summary for handoff_count or wait_minutes fields.

Analyzes process handoff records (handoff_count, wait_minutes) to produce a duplicate key report and a two-measure correlation analysis for up to 200 rows.

Computes an equal-width histogram for process handoff counts and a secondary-measure weighted mean for wait minutes (or vice versa) from supplied rows, returning two analytical reports.

Deduplicates exact rows and computes daily sum time series for process handoff records (handoff_count, wait_minutes) across team groups.

Deduplicates exact duplicate rows and applies numeric ordering to process handoff records (handoff_count, wait_minutes), returning two analytical reports for up to 200 rows.

Analyzes process handoff records (handoff_count, wait_minutes) using IQR-based outlier detection and secondary-measure weighted mean, returning two statistical reports for up to 200 rows.

Filters process handoff records to a numeric interval and fills missing values using zero, mean, or median — returning two analytical reports for up to 200 rows.

Sorts process handoff records by numeric value and fills missing handoff_count or wait_minutes values using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.

Ranks process handoff records by numeric row order and computes a secondary-measure weighted mean across up to 200 supplied rows, returning two analytical reports.

Performs numeric row ordering and two-measure correlation analysis on process handoff records (handoff_count and wait_minutes) for up to 200 rows, returning two analytical reports.

Runs two analytical reports on process handoff data: a record quality audit and a daily sum time series for either handoff_count or wait_minutes, for up to 200 rows.

Runs three local analytical reports on process handoff records: a record quality audit, exact row deduplication, and a base-100 indexed series for up to 200 rows.

Runs a data quality audit and a weighted mean calculation on process handoff records (handoff_count, wait_minutes) for up to 200 rows, returning two independent analytical reports.

Runs three local analytical reports on process handoff data: a record quality audit, string normalisation, and a date-by-category pivot — up to 200 rows per call.

Runs three local analytical reports on process handoff records: a data quality audit, string normalisation, and a numeric interval filter on handoff_count or wait_minutes fields.

Runs three local analytical reports (record quality audit, string normalisation, numeric row ordering) on up to 200 rows of process handoff data (handoff_count, wait_minutes).

Normalises string fields and identifies duplicate row keys in process handoff records (handoff_count, wait_minutes) for up to 200 rows, returning two independent analytical reports.

Normalises team/group string fields and filters process handoff records (handoff_count, wait_minutes) by a numeric interval, returning two analytical reports for up to 200 rows.

Runs two analytical reports on process handoff records: string normalisation of team/group labels and a trailing mean time-series calculation over handoff_count or wait_minutes.

Computes a trailing mean time series and a date-by-category pivot table for process handoff records (handoff_count, wait_minutes) supplied by the caller.

Computes Pearson/rank correlation between handoff_count and wait_minutes, and compares two keyed process-handoff datasets row-by-row, returning two analytical reports for up to 200 rows.

Computes Pearson correlation between handoff_count and wait_minutes, plus a weighted mean for a chosen secondary measure, across up to 200 process handoff records.

Computes period-over-period adjacent row changes for procurement records (ordered_units or unit_cost) across up to 200 supplier rows.

Computes adjacent row delta changes and secondary-measure weighted mean statistics for procurement cycle records (request_days, approval_days) supplied by the caller.

Computes category frequency distributions and fills missing values in procurement cycle records (request_days, approval_days) for up to 200 rows, returning two analytical reports.

Analyzes procurement cycle records to produce category frequency breakdowns and numeric row ordering for request_days or approval_days fields, across up to 200 rows.

Analyzes user-supplied procurement cycle records to produce a duplicate key report and a date-by-category pivot table for request_days or approval_days fields.

Analyzes up to 200 procurement cycle records to produce a duplicate key report and numeric distribution for request_days or approval_days fields.

Performs exact row deduplication and numeric row ordering on procurement cycle records containing request_days and approval_days fields, returning two analytical reports.