Who we build for
AI for Start-ups
For a start-up the calculation is straightforward: the AI capability has to be in the product before there is revenue to justify a GPU fleet, and the cost has to track customers rather than run ahead of them.
So the services below are available as an API you can build against this week, with dedicated capacity behind it that grows as your volume does — from a single GPU to a hundred. What that buys you beyond a public API is control: fixed capacity rather than a shared queue, no per-token pricing surprise when a customer sends a large batch, the option to move the whole thing into your own cloud account when an enterprise buyer requires it, and no dependency on a provider that may deprecate the model your product depends on.
Services that apply
The services start-ups build on most often, grouped by what the product does. Each links to its own page with the models, hardware and throughput behind it.
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Documents and text
Turning what your users upload or write into structured data your product can use.
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PDF/document to structured JSON
The foundation of any product that ingests customer files — structure, tables and reading order recovered as data.
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Structured data extraction from text
Free text filled into your own schema, which is how an upload becomes a record without anyone retyping it.
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Invoice / receipt extraction
Supplier documents and expense receipts read into fields, with arithmetic validation you get free.
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Table extraction
Spreadsheet-ready tables out of the PDFs your customers actually have.
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Text summarization
Long content reduced to the substance, at whatever length your interface has room for.
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Multilingual translation
Reaching a second market without a translation vendor or a per-word bill.
Search and assistants
The retrieval and answering layer most AI products are built around.
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Semantic search
Search by meaning over your customers’ own content — usually the first AI feature a product ships.
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Search reranking
The cheapest large improvement to a search you already have: nothing else in the stack changes.
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Document question answering
Answers with the source passage cited, which is what makes an answer trustworthy in a product.
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Private chatbot / enterprise assistant
An assistant over your customer’s material, running on capacity you control rather than a shared endpoint.
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Duplicate / near-duplicate text detection
Keeping a user-generated corpus clean, and catching the same submission arriving twice.
Media
Vision, speech and video features, which are where a small team most needs the GPU capacity handled for them.
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Audio to text - transcription
Transcription with timings — the basis of most audio and meeting products.
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Automatic subtitles / captions
Delivery-standard subtitle files, with the cue and reading-speed rules applied rather than a wall of text.
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Image-to-text captioning
Descriptions and alternative text at volume, for accessibility and for making an image library searchable.
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Image classification
The simplest and most often correct vision feature: which of your categories does this image belong to.
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General object detection
Finding and labelling the things your product cares about, fitted to your own object types.
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Image upscaling / super-resolution
User-supplied images made presentable enough to publish.
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Voice cloning / custom voice TTS
Narration and spoken output at volume, with consent and marking handled properly.
Trust and safety
What a platform needs before it can accept content from strangers.
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Content moderation - text
User-generated text checked against your own written policy, with categories and severity rather than a yes or no.
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Live stream moderation / visual policy alerts
Picture and audio watched against policy fast enough for a moderator to act.
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PII detection and redaction
Personal data found and masked, which is what lets you use production data for analysis or training.
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Digital watermark / provenance embedding
Marking generated media as generated, which several jurisdictions now require.
Bring your own model
Most start-ups reach a point where a fine-tuned model is the product’s moat. When yours is ready, it runs on the same infrastructure as our services — no second vendor, no separate pipeline.
We help you select the right hardware, containerize and deploy the workload, build the processing pipeline and define the infrastructure required to scale it.
Your model. Your data. Your deployment choice.
Bring your existing GPU containers
We support standard Linux GPU workloads including:
- Docker
- OCI images
- NVIDIA CUDA
- vLLM
- TensorFlow
- PyTorch
and other GPU-enabled environments. Images can be uploaded directly or pulled from registries including AWS ECR, Docker Hub, GitHub Container Registry, Google Artifact Registry, Azure Container Registry, Harbor and private registries.
Where it runs
Start on Super-GPU Cloud, and move when a customer requires it — the same workloads run unchanged.
On premises, in your AWS account, GCP project or OCI tenancy, on Super-GPU Cloud, or across a hybrid environment that keeps latency-sensitive work local and reaches for cloud GPU capacity when scale demands it. You control where data is processed, stored and retained.