PIXELS · AUDIO · LANGUAGE
Making unstructured input legible to software.
Images, video, audio and free text are where most real business data lives and where most systems give up. Turning them into structured, queryable records is the unlock — and it is as much a pipeline problem as a model one.
- Streamed
- Processing that scales with volume
- Validated
- Confidence thresholds and review
- Structured
- Typed records out the far end
THE SHORT VERSION
Mostly a pipeline problem
The model is rarely the bottleneck. Decoding, frame sampling, batching, GPU scheduling, queue depth, storage throughput and retry semantics are what decide whether a system handles ten files or ten thousand.
We build the pipeline to be resumable and observable first, then slot the model in as a stage within it.
The difference is where the effort goes.
Single-machine scripts
- One long process that loses everything if it crashes at 90%
- No backpressure, so a burst exhausts memory
- Output quality unmeasured beyond spot checks
- Reprocessing means starting from the beginning
A processing platform
- Work split into resumable units with durable state
- Queue-based backpressure and controlled concurrency
- Accuracy measured on a held-out set per content type
- Selective reprocessing of only what changed
WHAT IT INCLUDES
Scope, in plain terms.
Every engagement is shaped to the problem, but this is the ground it typically covers.
Speech & transcription
Batch and near-real-time transcription with speaker and timing metadata.
Video processing
Transcoding, frame extraction and analysis pipelines built on FFmpeg.
Document OCR
Layout-aware extraction from scans, forms and photographed documents.
Image classification
Detection, tagging and quality inspection over image streams.
Text understanding
Entity extraction, classification and summarisation over free text.
Search over media
Making an archive of audio and video actually searchable.
What you get
- Ingestion and queueing layer with backpressure
- Processing workers with resumable, idempotent units of work
- Model integration with confidence thresholds and review routing
- Structured output schema and storage
- Accuracy evaluation per content type and monitoring in production
QUESTIONS
The things people ask first.
How accurate is transcription in practice?
It depends heavily on audio quality, accent and domain vocabulary. We measure on your own material before quoting a number, rather than repeating a benchmark figure.
Can this run on our own infrastructure?
Yes. Where data residency or cost requires it, the pipeline can run entirely within your environment with self-hosted models.
What about very large archives?
That is a throughput and cost question first. We size the pipeline against a sample and give you a per-hour processing cost before committing.
Often paired with
- 06Machine LearningPredictive models, recommendation systems and intelligent optimization.
- 03AI DevelopmentProduction-ready artificial intelligence systems built to solve practical business problems.
- 08Cloud & Backend EngineeringScalable APIs, distributed systems and cloud infrastructure engineered for reliability.
Need computer vision & nlp?
Tell us the problem and the constraint. We will tell you what it takes — and whether this is even the right service for it.