What it does
Main work loop
- Builds the pipeline from data and features to training and inference
- Helps with dataset quality, evaluation, and the next model iteration
- Moves ML toward a production service
<- All Solo scenarios
SOLO
Scenario
Solo agent for ML
Takes an ML task as a working loop: data, training, evaluation, inference, and integration into the product.
Solo scenario: personal BYO loop
Access by requestYour model access stays yoursHuman reviewLicenses and updates in the account
Examples
How the task is framed
- We need lead ranking by purchase probability: build features, a baseline, evaluation, and an inference service.
- We have support dialogues. Build classification of requests by topic and urgency so it can later go into the CRM.
- We need to detect suspicious transactions: prepare the dataset, train the model, describe the metrics, and the rollout approach.
Value
What you get
- Less manual fuss with pipelines
- Faster path from data to working inference
- A clear result after experiments
How the work is built
Three steps, one loop: you hand off the task, the agent works, you get the result.
Step 1
InputYou give the task in free form
You can come with an idea, a draft spec, code, a landing page, a CRM, or the current process.
Step 2
WorkThe agent takes the work area
It gathers context on its own, does the main work, and pulls you in only where a real decision is actually needed.
Step 3
OutputYou get the next finished layer
The output is a result: code, product structure, a lead list, a campaign, statuses, or the next step.
Need help choosing the role
Come with a task. We will choose the right product and operating setup.