Cloudglue

What do they actually do

Cloudglue provides developer APIs and SDKs that turn video and audio into structured, LLM‑ready data. It handles steps like transcription, segmentation, timestamps, speaker attribution, and entity extraction so teams can build search, Q&A, and analytics features on top of long‑form recordings without stitching together multiple tools themselves Cloudglue docs.

They also offer connectors to ingest content from sources like meeting platforms and storage (e.g., Gong, Zoom, S3) and tools for building video Q&A/chatbots and searchable video knowledge bases, aimed at product and internal knowledge use cases docs data connectors Gong integration.

Who are their target customer(s)

• Product teams building video Q&A/chatbots for training or course content: They need to make hours of footage searchable and answerable at the clip level, but stitching transcripts, timestamps, and context together is complex and brittle. Cloudglue positions “Video Q&A Chatbots” and searchable video knowledge bases for this use case docs.
• Sales and customer‑success teams recording calls and demos: They struggle to reliably extract who said what, action items, and product mentions from recordings at scale; manual review is slow. Cloudglue supports importing meeting recordings and extracting structured data, with a Gong connector for call ingestion Gong integration.
• Media companies/content platforms with large video libraries: They need consistent metadata (people, locations, mentions, clips) for surfacing, recommendations, and monetization; manual tagging is costly and inconsistent. Cloudglue advertises structured data extraction and entity collections for this docs.
• Learning & development/internal knowledge teams: Recorded trainings, demos, and town halls are hard to reuse; employees can’t quickly find the exact moment that answers a question. Cloudglue calls out video knowledge bases and tools to make long content queryable docs.
• Developers and ML/AI teams needing a reliable pipeline to structure video/audio: Building transcription, segmentation, diarization, and schema extraction in‑house is time‑consuming and error‑prone. Cloudglue provides APIs, SDKs, and a playground to integrate these steps programmatically data connectors docs.

How would they acquire their first 10, 50, and 100 customers

• First 10: Run hands‑on, time‑boxed design‑partner pilots with product/L&D/sales teams: connect their recordings (Gong/Zoom/S3) into Cloudglue and deliver one measurable win (e.g., “find the clip that shows X,” “extract action items from last week’s calls”) within 2–4 weeks to secure testimonials and feedback docs Gong integration YC profile.
• First 50: Productize the pilot wins with turnkey templates, SDK samples, and a short onboarding checklist so engineering teams can self‑install; run targeted outreach (L&D/CS playbooks, developer forums, webinars) and co‑market via connectors like Gong using pilot case studies as proof points docs.
• First 100: Adopt a hybrid motion: keep a few high‑touch enterprise closes while scaling self‑serve through better docs, templates, SDKs, and marketplace listings (Zoom, Gong, LMS, cloud storage). Invest in reproducible content and a community/Discord loop to highlight developer wins and reduce support friction docs.

What is the rough total addressable market

Top-down context:

Cloudglue sits across three software markets: video analytics/intelligence, speech‑to‑text APIs, and conversational AI that consumes transcripts. Recent estimates peg these at roughly $12.7B (video analytics), $3.8B (speech‑to‑text APIs), and $7.1B (conversational AI) in 2024 GVR video analytics GVR speech‑to‑text API IDC conversational AI.

Bottom-up calculation:

Summing the three software slices gives a pragmatic near‑term TAM of about $23.6B in 2024: $12.7B (video analytics) + $3.8B (speech‑to‑text APIs) + $7.1B (conversational AI) GVR video analytics GVR speech‑to‑text API IDC conversational AI.

Assumptions:

• Markets overlap in practice (e.g., transcription underpins conversational AI), so the summed figure is an upper‑bound snapshot for the software/API layer.
• Focus is on software/services, excluding hardware and vertical solution spend.
• Adjacent markets (e.g., corporate e‑learning, broader video intelligence) are excluded from the core total but represent upside GVR corporate e‑learning Insight Partners.

Who are some of their notable competitors

• AssemblyAI: Developer speech APIs that produce transcripts, summaries, named entities, and chapter timestamps—often used to build searchable video Q&A or automated notes without custom stitching docs.
• Deepgram: Speech‑to‑text API with diarization and multichannel support, a direct alternative for programmatic transcription and speaker attribution for calls/demos overview diarization.
• Google Cloud Video Intelligence: Cloud API extracting video metadata (transcripts, objects/labels, scene changes, OCR) for indexing large libraries for search and recommendations docs.
• Microsoft Azure AI Video Indexer: End‑to‑end video/audio indexing (transcripts, speaker timelines, detected people/topics) with a searchable timeline, positioned for enterprise media/training workflows product page.
• Gong: Revenue intelligence that records, transcribes, and turns calls into structured CRM insights (e.g., action items, competitors, stakeholders); a practical alternative for sales teams seeking outcomes over raw APIs AI Data Extractor.