Formats
- Terraform, Bicep, or your infrastructure-as-code tool
- Python with Spark or Ray; containers for model steps
- Outputs as Parquet, Delta or Iceberg tables, or your vector store
Process fifty million documents without sending them anywhere.
We design and build the heavy pipelines behind AI work: distributed parsing, OCR and transcription, embedding generation, GPU batch inference for pre-labeling, and feature pipelines for analytics platforms. They run in your AWS, Azure, or Google Cloud account or on your own hardware, and each one ships with a cost model, monitoring, and a runbook.
The expensive part of AI data is often the plumbing
A notebook that processes ten thousand documents nicely will usually fall over at ten million. Memory runs out on the one 900-page PDF. GPUs sit idle waiting on storage reads. A failure at hour six restarts the whole job, and the second attempt doubles a bill nobody estimated. Meanwhile the data can't leave your tenant, so a managed service elsewhere isn't an option.
We build pipelines that treat those problems as requirements: partitioned so work spreads evenly, checkpointed so failures retry only what failed, idempotent so a retry never duplicates output, and instrumented so you can see throughput and spend while the job runs. Everything is written as code in your repository and deployed as infrastructure-as-code in your account, so your team can run it without us.
Pipelines we build most often
Document and audio ingestion
Parse, OCR, and transcribe millions of files, routed by type, with GPU models batched and a dead-letter queue for files that fail.
Embedding at scale
Chunk, embed, and build vector indexes, with batch sizes tuned to GPU memory and re-embedding limited to what changed.
Pre-labeling with batch inference
Run an open-weight model over a corpus to propose labels, then route by confidence so experts review only what needs them.
Deduplication and filtering
MinHash-LSH, embedding clustering, and quality classifiers at corpus scale, with every removal logged.
Feature pipelines for analytics
Turn text, sensor logs, and transactions into tested, versioned features for your warehouse or ML platform.
Choosing the engine
We pick the engine per workload and often use more than one in a pipeline. We have no platform to sell, so the choice follows the measurements.
| Workload | Usually runs best on | Why |
|---|---|---|
| Large joins, aggregations, tabular feature engineering | Spark, managed or self-hosted | Mature SQL and DataFrame engine, strong on shuffle-heavy work |
| Python-heavy document processing, OCR, and model inference | Ray | Mixes CPU and GPU stages in one pipeline and scales Python functions and model workers |
| Embedding and batch LLM inference | Ray Data or a batch inference server such as vLLM on GPU nodes | Continuous batching keeps GPUs saturated |
| Small, frequent incremental jobs | Your existing orchestrator with container or serverless tasks | No cluster to babysit; cost tracks usage |
| Air-gapped or on-premises | Kubernetes or Slurm on your hardware | Same code, and no data leaves the building |
A cost model before the first job runs
Every pipeline starts with a priced estimate. We measure throughput per node on a representative sample, project the node-hours the full run needs, add storage and network costs, and show the expected bill under on-demand and interruptible capacity. Interruptible capacity can be much cheaper: AWS lists Spot Instances at up to 90% off on-demand prices, and the other major clouds offer similar discounted capacity. The provider can reclaim it with little notice, so we design batch jobs to checkpoint and resume cleanly and use it wherever the deadline allows.
We also benchmark two or three GPU types on your actual data before committing. For embedding and pre-labeling, a smaller GPU with good batching is often cheaper per million tokens than the largest one available. After each run we compare actual cost to the estimate and update the model, so the second estimate is better than the first.
The pipeline build at a glance
You provide
- A project or sandbox in your cloud account, or access to on-premises nodes
- The workload: data volumes, formats, latency needs, and destination
- Your security requirements: network boundaries, key management, and logging
We deliver
- A pipeline in your account, as infrastructure-as-code and versioned source
- A cost model with measured throughput, projected node-hours, and expected bill
- Dashboards and alerts for throughput, failures, and spend
- A runbook your team can operate from, plus a handover session
Quality gates
- Throughput and cost measured on a representative sample before the full run
- Reruns are idempotent: a failed batch retries without duplicating output
- Record counts reconciled from input to output at every stage
- No data, and no logs containing data, leave your environment
Turnaround
Four to six weeks per pipeline
Team
A data platform engineer and an ML engineer, with a security review by your team
Starts at
$14,000per pipeline
One distributed processing or embedding pipeline designed and built in your cloud, with a cost model, monitoring, and a runbook.
Priced per pipeline
Pipeline build
Starts at
$14,000per pipeline
One distributed processing or embedding pipeline designed and built in your cloud, with a cost model, monitoring, and a runbook.
Request a scoped quoteCompute is billed by your cloud provider directly to your account; we don't resell or mark it up. The cost model tells you what to expect before you approve the full run.
Questions about processing pipelines
Do you run the pipeline for us, or hand it over?
Either. We build it in your account, and your team can operate it from the runbook. If you'd rather not staff it, our managed data operations team can run it on a schedule.
Do you prefer AWS, Azure, or Google Cloud?
No. We build on whichever cloud you already use, with its native storage, identity, and key management, so the pipeline fits your existing controls rather than requiring new ones.
Can you work on-premises or in an air-gapped environment?
Yes. The same pipelines run on Kubernetes or Slurm on your hardware, with model weights and containers brought in through your approved transfer process. Nothing calls out to the internet.
How do you keep sensitive data safe inside a distributed job?
Private networking with no public endpoints, encryption with your keys, logs that record counts and IDs rather than content, and de-identification before any stage that doesn't need identities. Your security team reviews the design before the first run.
What does it cost to embed or OCR our whole archive?
We'll tell you after measuring a sample, usually within the first week. The answer depends on page counts, scan quality, chunk sizes, and model choice, and a measured estimate beats a rule of thumb by a wide margin.
Keep reading
Bring us one messy data source and one question.
Tell us what the data is and what you want a model to do with it. A data engineer, not a salesperson, will reply within one business day with questions and a proposed first step.