We build the back end of media products: transcoding to adaptive HLS, audio tracks per language, transcripts and subtitles. Jobs run on Kubernetes, grow when uploads arrive and shrink to zero when the queue is empty.
Media work is heavy, uneven and fans out fast. A script on one server handles a few files. A catalog or an archive needs a pipeline.
A queue-driven service that adds nodes as jobs arrive and removes them all when the queue is empty.
One encode that produces every rendition and a master playlist that links all video and audio variants.
Recordings split at natural pauses, transcribed in parallel, merged into one timeline and cleaned up per language.
Each item links to the project where we built it.
Raw uploads turned into HLS streams in several resolutions, so players switch quality as the viewer’s connection changes.
Every audio stream detected, tagged with its language and encoded to its own bitrate ladder, so viewers can pick the language they want.
A job manager reads S3 upload events from SQS and gives each heavy job its own Kubernetes pod with set limits, then cleans up after it.
Timed transcripts with speaker labels, subtitles and summaries from audio-only files and video containers through one path.
Each chunk goes to the model tier that fits its length, so short and simple work does not pay the price of the largest model.
The product layer listeners touch: one account across the marketing site and the streaming app, and one API for news, comments and likes.
A media streaming business publishes video for several projects and publishers. Each raw file dropped into S3 must come out with several resolutions, separate audio per language, thumbnails and a preview. Uploads arrive unevenly, so large machines around the clock made no sense.
transcription precision on Hebrew and Aramaic lectures, with English mixed in.
subtitle timestamp accuracy, even when long recordings are split and merged again.
transcription jobs running at the same time under queue management.
lower AI and API cost through dynamic model selection.
An educational lecture archive holds recordings that often run an hour or longer. Speakers move between Hebrew, Aramaic and English in the same sentence, and the text needs diacritics and correct references to sources.
The pipeline probes each file, splits long recordings at pauses, transcribes the chunks with Gemini into a structured schema with timestamps and speaker labels, and merges them back into one timeline. A parallel module builds multi-bitrate HLS streams, thumbnails and previews. It runs on Kubernetes with retries and adaptive backoff.
Both pipelines on this page follow the same shape: inspect the file, queue the work, process it in isolated jobs, put the results back together, deliver them and release the compute.
Related services: cloud and DevOps for Kubernetes, queues and autoscaling, and AI development for transcription, summaries and model routing.
FFprobe checks every upload. Audio is extracted from video containers, and every audio stream is detected and tagged with its language.
Upload events land in a queue. If the same file is already in progress, the message goes back with a delay, so nothing is processed twice.
Each heavy job gets its own pod. Long recordings are split at natural pauses, and video is encoded to every rendition in one pass.
Transcript chunks are joined into one timeline with corrected offsets. Language-aware post-processing tidies the text.
Segments, playlists, subtitles, thumbnails and previews go to cloud storage in parallel, in a separate location per project or publisher.
When the queue is empty, the autoscaler removes the processing nodes. Compute cost drops to zero until the next upload.
A transcoding service for a streaming business, a transcription pipeline for a lecture archive and a music streaming platform.
An event-driven AWS and Kubernetes service that turns each uploaded video into adaptive HLS streams with multi-language audio, then scales back to zero.
A Kubernetes pipeline that turns hours of mixed-language lectures into timed transcripts, subtitles, summaries and HLS video.
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Anything missing? Send us your question and an engineer will answer, usually within one business day.
Contact usIn the pipeline we built for a media streaming business, each raw video becomes HLS streams in five resolutions from 240p at 300 kbps to 1080p at 5000 kbps, with 4-second segments. Every audio track is encoded to five bitrate tiers between 32 and 192 kbps, and a master playlist links all video and audio variants. Thumbnails and a short low-resolution preview are produced too.
Two ways. For transcoding, the service runs on Kubernetes nodes that the autoscaler adds as the queue grows and removes when it is empty, so idle compute costs nothing. For AI transcription, each chunk is sent to the Gemini model tier that fits its length, which cut AI and API costs by up to 60%.
Not necessarily. The encoder is chosen automatically across NVIDIA, Intel Quick Sync and AMD hardware, with a software fallback, so the same pipeline runs on whatever nodes are available.
The transcoding job manager handles SQS duplicates, visibility timeouts and retries for failed jobs without double processing. The transcription pipeline uses retries with adaptive backoff for API rate limits, and work re-syncs automatically when a connection drops, so no data is lost.
Yes. For an educational lecture archive where Hebrew, Aramaic and English are mixed in one recording, we reached 99% transcription precision and 99% subtitle timestamp accuracy across split recordings, with more than 300 jobs running at the same time. Our blog post on Gemini transcription explains the approach.
The transcoding service runs on AWS with SQS and S3. The transcription pipeline uses Google Vertex AI with storage on AWS S3 and Google Cloud Storage. Both run on Kubernetes, deployed with Helm and Docker through CI/CD.
You do. Code, cloud accounts, queues and storage are yours, with full IP assignment in the contract. We charge for the work, not per minute of media processed.
A spec, a Figma file or three paragraphs is enough to start.
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