SharpAPI

Generates product descriptions from minimal input (product name, category, attributes) using underlying AI models that synthesize marketing copy optimized for e-commerce platforms. The endpoint accepts structured product metadata and returns human-readable descriptions suitable for catalog listings, leveraging word-quota-based pricing where each generated description consumes a measurable word count against the user's monthly allocation.

Analyzes customer review text to extract sentiment polarity (positive/negative/neutral) and returns a confidence score indicating classification certainty. The implementation uses text classification models to process review content and outputs structured sentiment data that can be aggregated for product quality metrics or used to flag problematic reviews for manual inspection.

Identifies profane, offensive, or inappropriate language in text content and flags instances for removal or masking. The implementation uses word-list-based and ML-based profanity detection to identify offensive content, enabling automated content moderation and family-safe content filtering.

Analyzes text to determine whether content was generated by AI models or written by humans, returning a classification with confidence score. The implementation uses text analysis models trained to identify statistical patterns and linguistic markers characteristic of AI-generated text, enabling detection of synthetic content for authenticity verification and fraud prevention.

Identifies and extracts email addresses from unstructured text content and validates their format and deliverability. The implementation uses regex-based pattern matching combined with email validation rules to locate email addresses and verify they conform to RFC standards, enabling automated contact data extraction and list cleaning.

Identifies and extracts phone numbers from unstructured text content and normalizes them to E.164 international format (e.g., +1-555-0123). The implementation uses regex-based pattern matching combined with phone number parsing libraries to locate phone numbers in various formats and standardize them for international compatibility.

Identifies and extracts URLs (hyperlinks) from unstructured text content, including detection of broken or malformed URLs. The implementation uses regex-based URL pattern matching to locate hyperlinks in various formats and validates URL structure to identify potentially broken or suspicious links.

Identifies and extracts physical addresses from unstructured text content, including street addresses, cities, states, and postal codes. The implementation uses regex-based pattern matching combined with address parsing to locate and structure address components, enabling automated contact data extraction and address validation.

Identifies and extracts relevant keywords and tags from text content, returning extracted terms with relevance scores indicating importance or frequency. The implementation uses NLP techniques (TF-IDF, topic modeling, or neural embeddings) to identify salient keywords and rank them by relevance, enabling automated content tagging and SEO optimization.

Generates job descriptions from minimal input (job title, department, key responsibilities, required skills) using AI models that synthesize professional job postings optimized for recruitment. The endpoint accepts structured job metadata and returns complete job descriptions suitable for posting on job boards, with customizable tone and emphasis on specific qualifications.

Generates personalized thank-you emails for customers after purchase, with customizable tone, product mentions, and promotional offers. The implementation uses template-based generation combined with variable substitution to create personalized emails that reference customer purchase details, enabling automated post-purchase communication without manual email writing.

Provides programmatic access to all SharpAPI endpoints via standard HTTP REST API with JSON request/response format. The implementation uses API key authentication and word-quota-based usage metering where each API call consumes a measurable amount of the user's monthly word allocation, enabling developers to build custom integrations beyond pre-built connectors.

Classifies products into predefined category taxonomies based on product name, description, and attributes, returning category assignments with relevance scores indicating confidence in each classification. The endpoint uses multi-class or multi-label classification models to map products to hierarchical category structures, enabling automated catalog organization without manual tagging.

Extracts structured information from resume documents (PDF, DOC, DOCX, TXT, RTF, JPG, PNG, TIFF formats) and returns parsed data points including contact information, work history, education, skills, and certifications. The implementation uses document parsing and NLP to convert unstructured resume text into machine-readable JSON, enabling HR automation workflows like candidate screening and applicant tracking system (ATS) integration.

Extracts structured financial data from invoice documents (format support not fully specified) and returns parsed fields including vendor information, line items, amounts, dates, and tax details. The endpoint uses document understanding and OCR to convert invoice images or PDFs into machine-readable JSON, enabling accounts payable automation and expense management workflows.

Translates text content into 80+ languages with support for customizable writing styles and contextual tone adjustments. The implementation uses neural machine translation models with style transfer capabilities, allowing users to specify target tone (formal, casual, technical) and context (e-commerce, legal, marketing) to produce contextually appropriate translations rather than literal word-for-word conversions.

Condenses long-form text content into shorter summaries while preserving key information, with configurable output length and detail level. The implementation uses abstractive or extractive summarization models to identify salient content and generate concise summaries, enabling rapid content consumption and information triage workflows.

Rewrites text content while maintaining original meaning, with options to preserve or adjust tone, formality level, and writing style. The implementation uses neural language models to generate alternative phrasings that avoid plagiarism while keeping semantic content intact, enabling content reuse and plagiarism avoidance workflows.

Analyzes text for grammatical errors, spelling mistakes, punctuation issues, and style improvements, returning flagged errors with correction suggestions and explanations. The implementation uses rule-based and ML-based grammar checking to identify issues and suggest fixes, enabling automated content quality assurance without manual proofreading.

Classifies text content as spam or legitimate with confidence scores and explanations of detected spam indicators. The implementation uses text classification models trained on spam patterns to identify unwanted content (phishing, promotional spam, malicious links), enabling automated content moderation and security workflows.