Industries · AI & technology
Training and validation data for AI companies
Validation and training data for AI companies on fixed launch dates, run on ready-made pipelines so you don't build data infrastructure from scratch.
Overview
Data work that keeps pace with an AI company
AI companies live on deadlines they don’t control. A customer’s validation window, a model release, a funding milestone — the date is set before the data work is scoped. And the data work is rarely one step. Raw files have to be ingested, converted, pre-labeled, labeled, checked, and packaged for the training or validation code, and each of those stages needs code someone writes, runs, and fixes when the next dataset looks slightly different. For a small team, that pipeline quietly becomes a second product.
The usual answers both cost time. Building the pipeline in-house pulls data scientists off the model for weeks. Hiring a data operations team is slow, and the work is uneven: heavy during a sprint, idle between programs. Meanwhile the quality bar doesn’t drop — validation data has to be more accurate than the model it tests, and training data for a language model has to be right, not merely plausible.
MLtwist gives AI teams a pipeline that already exists. Preprocessing and transformation run as Twists, ready-made or custom-built in the Twist AI Builder, with every run recorded. Expert contributors, sourced and skill-tested for each project, handle the judgment calls. Output is written to your storage or moved through the REST API into your workflows, with built-in QC before handover. You can bring your own labelers, use ours, or both, and you only hand over the stages you want to. Your files stay in your own cloud storage the whole time, encrypted at rest.
That’s how Bobidi finished a one-month audio validation program — nine stages and 100,000 file transformations — ahead of schedule at half the processing cost. The pipelines behind it are described on AI workloads & pipelines.
The problem
What makes ai & technology data hard
Launch dates don't move
Validation and training runs arrive with fixed dates set by customers, investors, or a release plan. There's rarely room to design, build, and debug a data pipeline from scratch first. Whatever the data work takes comes straight out of the time left for the model.
Many stages
One program ran nine processing stages and 100,000 file transformations before a single result reached the customer. Every stage is a place for errors to enter and a script someone has to maintain. Multiply that by every new dataset, and the pipeline becomes its own product.
Data work eats the budget
Every hour your data scientists spend moving, converting, and fixing files is an hour not spent improving the model. Hiring a data operations team to absorb it is slow and expensive, and the work is uneven: heavy during a sprint, idle in between.
How it works
How we plug into an AI team
You keep the model and the product. MLtwist takes the stages between raw files and your training or validation workflow.
- 01
Map your stages
Lay out every step from raw files to your training or validation workflow — ingestion, conversion, pre-labeling, labeling, review, packaging — and decide which ones MLtwist runs. Most teams hand over the repetitive stages and keep the ones tied to their model.
- 02
Run them as Twists
Preprocessing and transformation run on ready-made pipelines. Where your data needs something specific, custom Twists are built in the Twist AI Builder, test-built, and published as versions, and every run records who started it, which version ran, and its settings.
- 03
Add expert people
Coders, linguists, and domain experts sourced and skill-tested for your project handle the judgment calls the pipeline can't. They scale up for a sprint and back down when it's done, and your own team can work in the same tooling alongside them.
- 04
Push into your workflow
Processed data goes straight into your training or validation systems — written to your GCS, S3, or Azure storage, or moved through the REST API. Built-in QC runs before anything is handed over, so your team receives data that's already checked.
Case study · Bobidi
Bobidi finished a one-month validation program ahead of schedule
Bobidi, an AI validation platform founded by Google and Meta veterans, had one month to validate 5,000 audio files totaling 10GB: review 600,000 existing labels and create 50,000 new ones. The work ran through a nine-stage pipeline with 100,000 file transformations, and building and maintaining that pipeline from scratch would have put both the timeline and the budget at risk.
MLtwist ran it on out-of-the-box, scalable pipelines. Automated preprocessing handled the file transformations across every stage, built-in QC kept quality up at full volume, and processed data was pushed directly into Bobidi's validation workflows.
Bobidi got the work done ahead of schedule, cut deployment time by 90%, and halved its data processing spend — $25K a month, or $300K a year, freed for other work. The final datasets reached 98% accuracy against a 95% target, and Bobidi could put more of its resources into model performance instead of manual processing.
- faster deployment
- 90%
- lower data processing cost
- 50%
What you get
Data infrastructure you don't have to build
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Ready-made pipelines
Preprocessing and transformation at scale, running on pipelines that already exist. No custom build before the work can start.
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Experts on demand
Specialists sourced and tested for your project, scaled up for a sprint and back down when it's done. You don't carry a data operations team between programs.
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Direct integration
Output written to your storage or pulled through the REST API into your workflows. Your training and validation code reads it as is.
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Built-in QC
Multi-stage review keeps accuracy above target at full volume. Quality is measured before delivery, not discovered after.
More work
Related programs
FAQ
Common questions
We have our own labeling team. Can we still use MLtwist?
Yes. Your team can work in MLtwist's labeling tool and run on its pipelines, you can add our experts for volume or specialist work, or both. The tooling, QA, and delivery are the same either way.
Do we have to hand over our whole pipeline?
No. Most AI teams hand over the stages that are repetitive — conversion, preprocessing, labeling, review — and keep the ones tied to their model. Each stage runs as a Twist, so you can start with one and add more later.
Can MLtwist run our own code?
Yes. Your code can be packaged as a custom Twist, test-built, and published as a version that runs on your data like any other step. See AI workloads and pipelines for how Twists work.
Do you work on LLM data?
Yes. For one AI technology company, MLtwist sourced and tested coders for a proprietary coding language and built prompt-to-code and ranking data, cutting annotation timelines by more than 40%.
How does data get into our systems?
Pipelines read from and write back to your GCS, S3, or Azure storage, and the REST API supports programmatic uploads and downloads. Credentials are stored as secrets rather than pasted into settings.
What does it cost?
Pricing is custom, typically per file, based on data type and complexity, volume, quality requirements, and timeline.
Bring us your ai & technology data
Tell us the data type, volume, and timeline. We'll scope it with our team, yours, or both — and deliver it versioned, in your format.
Also available through Carahsoft and Google Cloud Marketplace.