InstaNews.ai

Automatically converts Instagram captions, stories, and visual metadata into full-length blog articles by analyzing caption text, hashtags, and image context through a multi-stage LLM pipeline. The system extracts semantic intent from short-form social content, expands it with contextual elaboration, and structures it into article format with headlines, body paragraphs, and metadata. Uses Instagram API webhooks to detect new posts and trigger async transformation workflows.

Implements a queue-based system that accepts multiple Instagram post URLs or IDs, validates them against the Instagram Graph API, and schedules them for sequential or parallel transformation. Uses async job scheduling to handle rate limits and API quotas, storing job status and transformation history in a persistent state layer. Supports both manual upload (URL list, CSV) and automated webhook triggers from Instagram.

Uses a multi-stage LLM prompt chain to expand short Instagram captions (typically 50-200 words) into full blog articles (800-2,000 words) by inferring context from hashtags, engagement metrics, and post timestamp. The system applies semantic analysis to identify post intent (announcement, tutorial, lifestyle moment, product showcase), then applies intent-specific expansion templates that add relevant sections (background, how-to steps, takeaways, call-to-action). Leverages few-shot prompting with examples from the creator's past posts to maintain consistency.

Automatically generates SEO-optimized metadata (title tags, meta descriptions, focus keywords, internal link suggestions) for transformed articles by analyzing expanded content, original Instagram hashtags, and competitor blog landscape. Uses keyword extraction and density analysis to identify primary and secondary keywords, then generates title variations and meta descriptions optimized for click-through rate (CTR) and search intent matching. Integrates with basic SEO scoring to flag articles with weak keyword coverage or suboptimal title length.

Analyzes image metadata, alt text, and visual characteristics from Instagram posts to inform article expansion and provide image-specific context cues. Extracts image descriptions via OCR or manual alt text, identifies dominant visual themes (product, person, landscape, text-overlay), and uses this information to guide content expansion toward image-relevant sections. Generates image captions and alt text for accessibility, and suggests where images should be placed within the expanded article structure.

Provides basic tone and style parameters (formal, casual, inspirational, educational) that influence LLM prompt templates used during content expansion. Users select a tone preset, which adjusts vocabulary, sentence structure, and section emphasis in the expansion pipeline. However, customization is limited to predefined templates; no fine-tuning on creator's actual writing samples or brand guidelines. Uses simple prompt engineering rather than model fine-tuning or retrieval-augmented generation (RAG) from creator's past content.

Integrates with WordPress REST API and other CMS platforms (Webflow, Wix, Medium) to automatically publish transformed articles directly to creator's blog without manual copy-paste. Handles authentication via API keys or OAuth, maps InstaNews.ai article structure to CMS-specific content models (post title, body, featured image, categories, tags), and manages post scheduling and status (draft, published, scheduled). Supports custom field mapping for extended metadata (author, publication date, custom taxonomies).

Implements a freemium tier that provides monthly credits for article transformations, with transparent per-action pricing (e.g., 1 credit per article, 0.5 credits per SEO optimization). Users can monitor credit consumption in real-time via dashboard, and credits reset monthly or roll over depending on subscription tier. Paid tiers offer higher monthly credit allowances and discounted per-credit rates. No hidden charges; all features are metered and visible to users.

Analyzes Instagram hashtags from original posts to infer content intent, topic domain, and target audience, then uses this information to guide content expansion and keyword selection. Maps hashtags to semantic categories (e.g., #fitnessmotivation → fitness, wellness, motivation) and uses category signals to select relevant expansion templates and keyword targets. Treats hashtags as explicit intent signals from the creator, improving context inference beyond caption text alone.