Build and evaluate a machine learning model from raw data

Cleans your dataset, trains and compares models with AI, and sends the winner to your team for approval.

How the work actually flows

A straight line. Runs once per each candidate model approach; a person has to approve before it continues.

Pattern: Multiple Instances with a priori Design-Time Knowledge (13)

flowchart TD trig(("user submits dataset and goal")):::human s0["plan features and approach"]:::task s1["clean and engineer data"]:::task s2[["train and compare models"]]:::mi s3(("approve model for use")):::human trig --> s0 s0 --> s1 s1 -->|"one per each candidate model approach"| s2 hg(("team approves on Slack")):::human s2 --> hg hg -->|"approved"| s3 hg -. "sent back" .-> s2 out[/"trained model with performance summary"/]:::out pay{{"working model without a data scientist"}}:::pay s3 --> out out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
A stepRuns once per itemA personResultPayoff
Build size
Standard

A mid-size build with several tools working together.

Business functions
Messaging & NotificationsAPI & Webhook IntegrationDevOps & IT OperationsEducation & Training
Connects
SlackClaude

The problem it solves

Getting from a raw spreadsheet of data to a working, documented model normally means a data scientist manually cleaning data, testing several approaches, and writing up the results before anyone can sign off. Most small teams don't have that time or expertise on hand, so promising data goes unused.

Who it fits

Teams without dedicated data science resources who want to turn a raw dataset into a working model with a human sign-off step.

How it works

  1. You submit a dataset link and your business goal
  2. AI plans which features and approaches to try
  3. The system cleans the data and builds new features
  4. Several models are trained and compared, with AI picking the best one
  5. A summary and results are sent to your team on Slack for approval before use
What you get

A working model built from your raw data

You get a working machine learning model built and compared from your raw dataset, with results sent to your team for a quick approval.

What you get

A trained, documented model with a performance summary, ready for your team to approve on Slack.

What you need

An Anthropic API key and a Slack workspace.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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