Astica AI

@astica transcribe this customer support call recording and highlight any mentions of billing issues or refund requests

Audio transcription complete (3m 42s recording). The call contains two billing mentions: at 0:47 the customer asks about a duplicate charge on their March statement, and at 2:15 they request clarification on the refund timeline for a canceled subscription. The agent confirms a 5-7 business day refund window. No explicit refund request was made, but the customer expressed frustration with the billing cycle timing.

This example shows Astica's audio analysis tool converting speech to text, then the AI reasoning over that transcript. Requires a publicly accessible audio URL or base64-encoded file. Astica processes the audio; the summarization and issue-tagging happen in the AI layer. Check Astica's pricing for audio duration limits.

@astica extract all text from this scanned invoice image so I can log the line items in our expense tracker

OCR extraction complete. Detected text includes: Invoice #4782 dated March 15, 2024. Vendor: Acme Office Supplies. Line items: (1) Printer Paper, 10 reams @ $4.99 = $49.90; (2) Toner Cartridge, Black @ $67.50 = $67.50; (3) USB Cables, 5-pack @ $12.99 = $12.99. Subtotal $130.39, Tax $10.43, Total $140.82. Payment terms: Net 30.

Demonstrates Astica's OCR tool reading structured text from an image. Works best with clear scans or photos; handwriting recognition is limited. You must provide an image URL or base64 string. The tool returns raw text; any parsing into expense fields happens via the AI's follow-up reasoning. Confirm image resolution meets Astica's requirements for accurate extraction.

@astica read the text from this whiteboard photo and convert the brainstorming notes into a prioritized action-item list

OCR detected the following whiteboard text: 'Q2 Goals: 1) Launch mobile app beta, 2) Hire 2 engineers, 3) Finalize API docs, 4) Customer feedback survey.' Below that: 'Blockers: API rate limits, design review pending.' Converted to prioritized actions: (1) Finalize API docs — unblocks mobile beta; (2) Resolve API rate limits — critical blocker; (3) Complete design review — prerequisite for beta launch; (4) Launch mobile app beta; (5) Hire 2 engineers; (6) Customer feedback survey.

Combines Astica's OCR with AI-driven prioritization logic. The MCP extracts text from the photo; the AI infers dependencies and reorders items. Quality depends on image clarity—glare, shadows, or messy handwriting reduce accuracy. This workflow is useful for digitizing meeting notes but requires manual verification of the extracted text before acting on the prioritized list.

When audio transcription beats manual note-taking for async teams

A 6-person remote product team records their standups and design critiques but struggles to extract action items afterward. The Astica Audio tool transcribes those recordings into searchable text, letting anyone who missed the call catch up without watching a 40-minute video. This works best when your team already has a recording habit and needs the text for search or summarization—not real-time captioning. If your calls are under 10 minutes or mostly silent screen-shares, manual notes are faster. The API key setup takes five minutes, and the transcription accuracy is solid for clear English audio. Use this MCP when your team's knowledge lives in voice and you need it in writing without hiring a transcription service.

OCR for support tickets when customers send error screenshots

A 3-person support team at a SaaS startup gets 20-30 tickets a day, half of which include screenshots of error messages or config screens. The ASTICA_READ_TEXT tool extracts the raw text from those images so the team can search for known issues or paste error codes into their internal docs without retyping. This is the right call when your support volume is high enough that manual transcription wastes 30+ minutes a day but not so high that you need a dedicated OCR pipeline. If customers already paste text or your screenshots are mostly UI mockups with no readable text, skip it. The tool requires confirming the image source first, which adds a step but prevents accidental processing of non-text images. Deploy this when your support backlog is text-heavy but arrives as pixels.

When OCR makes sense for one-off scanned contract review

A 2-person legal ops team at a fintech needs to review 15 scanned vendor contracts from a recent acquisition, but the PDFs are image-only with no selectable text. The ASTICA_READ_TEXT tool converts those scans into editable text so they can search for liability clauses or payment terms without reading every page. This is a good fit for occasional bulk OCR jobs where you don't want to set up a dedicated document pipeline or pay per-page pricing from a legal-tech vendor. If you're processing hundreds of documents a month, a purpose-built OCR service with batch processing will be faster. The two-tool limit here means you're not building a full document workflow—just extracting text when you need it. Use this MCP when your document digitization is episodic and your team already has API key access.